**Future Trends**

**Provisional chapter**

## **MANET Network in Internet of Things System**

**MANET Network in Internet of Things System**

Rasa Bruzgiene, Lina Narbutaite and Tomas Adomkus Tomas Adomkus Additional information is available at the end of the chapter

Rasa Bruzgiene, Lina Narbutaite and

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66408

## **Abstract**

In the current world of technology, various physical things can be used for facilitation of a human work. That is why the Internet of Things,an innovative technology and a good solution which allows the connection of the physical things with the digital world through the use of heterogeneous networks and communication technologies, is used. The Internet of Things in smart environments interacts with wireless sensor network (WSN) and mobile ad‐hoc network (MANET), making it even more attractive to the users and economically successful. Interaction between wireless sensor and mobile ad‐hoc net‐ works with the Internet of Things allows the creation of a new MANET‐IoT systems and IT‐based networks. Such the system gives the greater mobility for a user and reduces deployment costs of the network. However, at the same time it opens new challeng‐ ing issues in its networking aspects as well. In this work, the authors propose a rout‐ ing solution for the Internet of Things system using a combination of MANET protocols and WSN routing principles. The presented results of solution's investigation provide an effective approach to efficient energy consumption in the global MANET‐IoT system. And that is a step forward to a reliable provision of services over global Future Internet infrastructure.

**Keywords:** MANET, IoT, Sensor, energy efficiency, dynamic routing

## **1. Introduction**

The Internet of Things (IoT) is a part of the Future Internet paradigm, which rapidly changes the development of technologies as well as provision of services over different communication networks. The capability of objects (like physical or virtual things) to identify and commu‐ nicate with each other at any time‐evolving communication technologies gives the possibil‐ ity to provide advanced services over global infrastructure (as Internet) in different areas of everyday life [1]. The interconnection of smart objects and its interoperability with global

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

communications serve as a main idea incorporated in Internet of Things systems. Wireless sensor network (WSN) plays a main role in the IoT system as its components include sensing, acquiring of data, heterogeneous connectivity, data processing, etc. Mobile ad‐hoc network (MANET) is a wireless, multi‐hop, self‐configuring network. Its each node operates as an end system and/or a router for other nodes in the network and is closely related to WSNs. The interaction between MANET and Internet of Things opens new ways for provision of services in smart environments and challenging issues in its networking aspects as well.

One of the important factors in such MANET‐IoT systems is the energy balancing over nodes, since the IoT system is based mostly on many different wireless sensors and MANET proto‐ cols focus on selecting the shortest and efficient paths for transactions. A proper utilization of sensor's battery power is a significant key in maintaining network connectivity of a multi‐ hop wireless network. Due to this, many researchers are focusing on designing energy effi‐ cient routing protocols that prolong such network lifetime. Wireless network protocols like MANET cannot be used directly due to resource constraints of sensors' nodes, computational speed, human interface with node's devices and density of nodes in network. Therefore, it is a need of composite solution for routing over MANET‐IoT networks, which can use efficiently residual energy of nodes and extend the network's lifetime.

In this chapter, the authors propose an algorithm of energy efficient and safe‐weighted clus‐ tering routing for the mobile IoT system using a combination of MANET and WSN routing principles. Clustering is one method of making routing less complex, and for some sensor networks, more energy efficient. Such combination of MANET and WSN routing principles is able to increase the lifetime of sensors in the overall mobile Internet of Things system. It is important to decide how many cluster heads (CHs) are needed and which of the sensor nodes are going to act as cluster heads. MANET network nodes were chosen as a cluster head and a proactive routing protocol was used in such a way that it is possible to control and update a table of information about the state of the network. Nodes that rapidly lose its energy or that are left with low energy were identified and their workloads were limited for transactions. All investigations for the selection of a routing path over the MANET‐IoT system were performed by using the MATLAB simulation platform.

This research work provides important key insights into the combination of MANET and WSN routing principles by increasing the lifetime of sensors in the overall Internet of Things system. The solution of routing optimization with an effective and efficient approach to energy consumption in the global MANET‐IoT system is presented as main result of this work, which can help in accessibility and provision of services for a longer period of time over global Future Internet infrastructure.

## **2. Background**

## **2.1. Mobile ad‐hoc network (MANET)**

The mobile ad‐hoc networks (MANETS) are autonomously self‐organized networks without fixed topology. In such a network, each node acts as both router and hosts at the same time. All network nodes are equivalent to each other and can move out or join in the network freely. The mobile nodes that are in the radio range of each other can directly communicate and transfer the necessary information. All network nodes have a wireless interface to communi‐ cate with another node in the range. This kind of network is fully distributed and can work at any place without the help of any fixed infrastructure as access points or base stations. **Figure 1** shows the example of mobile ad‐hoc network [2].

It can be assigned two multiple ad‐hoc network types: (a) mobile ad‐hoc network (MANET) and (b) mobile ad‐hoc sensor network. A mobile ad‐hoc sensor network has much wider sequences of operational, and at the same time needs a less complex setup procedure com‐ pared to typical sensor networks, which communicate directly with the centralized control‐ ler [3]. There are six main characteristics of MANETs [2]: distributed operation; multi‐hop routing, autonomous terminal, dynamic topology, lightweight terminals, and shared physical medium.

MANET routing protocols can be categorized into three types:

**1.** Topology‐based routing

communications serve as a main idea incorporated in Internet of Things systems. Wireless sensor network (WSN) plays a main role in the IoT system as its components include sensing, acquiring of data, heterogeneous connectivity, data processing, etc. Mobile ad‐hoc network (MANET) is a wireless, multi‐hop, self‐configuring network. Its each node operates as an end system and/or a router for other nodes in the network and is closely related to WSNs. The interaction between MANET and Internet of Things opens new ways for provision of services

One of the important factors in such MANET‐IoT systems is the energy balancing over nodes, since the IoT system is based mostly on many different wireless sensors and MANET proto‐ cols focus on selecting the shortest and efficient paths for transactions. A proper utilization of sensor's battery power is a significant key in maintaining network connectivity of a multi‐ hop wireless network. Due to this, many researchers are focusing on designing energy effi‐ cient routing protocols that prolong such network lifetime. Wireless network protocols like MANET cannot be used directly due to resource constraints of sensors' nodes, computational speed, human interface with node's devices and density of nodes in network. Therefore, it is a need of composite solution for routing over MANET‐IoT networks, which can use efficiently

In this chapter, the authors propose an algorithm of energy efficient and safe‐weighted clus‐ tering routing for the mobile IoT system using a combination of MANET and WSN routing principles. Clustering is one method of making routing less complex, and for some sensor networks, more energy efficient. Such combination of MANET and WSN routing principles is able to increase the lifetime of sensors in the overall mobile Internet of Things system. It is important to decide how many cluster heads (CHs) are needed and which of the sensor nodes are going to act as cluster heads. MANET network nodes were chosen as a cluster head and a proactive routing protocol was used in such a way that it is possible to control and update a table of information about the state of the network. Nodes that rapidly lose its energy or that are left with low energy were identified and their workloads were limited for transactions. All investigations for the selection of a routing path over the MANET‐IoT system were performed

This research work provides important key insights into the combination of MANET and WSN routing principles by increasing the lifetime of sensors in the overall Internet of Things system. The solution of routing optimization with an effective and efficient approach to energy consumption in the global MANET‐IoT system is presented as main result of this work, which can help in accessibility and provision of services for a longer period of time over

The mobile ad‐hoc networks (MANETS) are autonomously self‐organized networks without fixed topology. In such a network, each node acts as both router and hosts at the same time.

in smart environments and challenging issues in its networking aspects as well.

residual energy of nodes and extend the network's lifetime.

by using the MATLAB simulation platform.

global Future Internet infrastructure.

**2.1. Mobile ad‐hoc network (MANET)**

**2. Background**

90 Ad Hoc Networks

The routing types [3] are: (a) proactive routing protocols (routing table‐based), (b) reactive routing protocols (demand based) are presented in **Figure 2** and (c) hybrid routing protocols. These protocols are the combination of proactive and reactive routing protocols. One of them is ZRP (zone routing protocol).

**2.** Location‐based routing

To make routing decision, location‐based routing uses the actual position of nodes in any area. Location information can be obtained, for example, using global positioning system (GPS). Location‐aided routing (LAR) protocol is an example of location‐based routing.

**Figure 1.** Example of mobile ad‐hoc network.

**Figure 2.** Proactive and reactive routing protocols.

## **3.** Energy awareness‐based routing

Each node in the network supports multiple entries of routing in routing tables. For choosing optimal route in the wireless medium, routing assessing power levels of network nodes is available. In this case, routing table corresponding to the power level of nodes and maintained by transferring hello messages in between nodes at the power level. The number of entries in routing table of nodes is corresponding to the number of nodes reachable by using the power level. Thus, the number of entries in routing tables gives the total number of network nodes [4].

## **2.2. The characterization of Internet of Things (IoT)**

Today a human is surrounded by various things—from the smallest items to the gigantic objects. The need to "recruit" these things was a great reason for the connection of electronics, devices, with digital communications, using the Internet as a main medium for data trans‐ mission. The human is just as data traffic end user in such connection, as all communication, management and information exchange are processing among connected things and objects. The capability of real or virtual things and objects to be identifiable, to communicate with anything and to interact with anything lets to build networks of interconnected objects, end users or other entities in the global Internet network. So the term "Internet of Things" mainly

**Figure 3.** Global structure of Internet of Things system.

means the global infrastructure (**Figure 3**) of interconnected things, devices, or objects, which can communicate, actuate, exchange their information over Internet to the end users using the interaction between communication technologies and networks.

Internet of Things is the part of Future Internet (**Figure 4**) [5]. The concept of Future Internet connects the Internet of Users and Knowledge (IoUK), Internet of Networks (IoN), Internet of Services (IoS) and Internet of Things. IoUK is used for people's social gaming or users monitoring, IoN opens possibilities for unlimited connectivity of networks and IoS is use for provision of web‐based services in the global smart industry. Broadly, Internet of Things cov‐ ers the large potential of computing and communication capabilities into the objects, which can interoperate in global‐integrated communication platforms. It serves as a bridge between the real things and digital, information world.

Sensors are the main elements that connect things, their data with remote end users. The sen‐ sors collect useful information for the end users data, convert it to digital format and transmit it to the other devices in IoT‐based systems with the help of various existing wireless or wired technologies [6]. As sensors are well deployed and it quantity is growing rapidly in the world, it serves as main interface, connecting things, communications and end users. The selection of medium for the data transmission, processing of data routing over different heterogeneous networks are one of the major challenges in the IoT‐based systems [7]. Wireless technologies,

**Figure 4.** IoT in Future Internet concept.

**3.** Energy awareness‐based routing

92 Ad Hoc Networks

**Figure 2.** Proactive and reactive routing protocols.

Each node in the network supports multiple entries of routing in routing tables. For choosing optimal route in the wireless medium, routing assessing power levels of network nodes is available. In this case, routing table corresponding to the power level of nodes and maintained by transferring hello messages in between nodes at the power level. The number of entries in routing table of nodes is corresponding to the number of nodes reachable by using the power level. Thus, the number of

Today a human is surrounded by various things—from the smallest items to the gigantic objects. The need to "recruit" these things was a great reason for the connection of electronics, devices, with digital communications, using the Internet as a main medium for data trans‐ mission. The human is just as data traffic end user in such connection, as all communication, management and information exchange are processing among connected things and objects. The capability of real or virtual things and objects to be identifiable, to communicate with anything and to interact with anything lets to build networks of interconnected objects, end users or other entities in the global Internet network. So the term "Internet of Things" mainly

entries in routing tables gives the total number of network nodes [4].

**2.2. The characterization of Internet of Things (IoT)**

**Figure 5.** Internet of Things characteristics and it relation with wireless and ad‐hoc networks.

ad‐hoc wireless networks are the most effective and low‐cost way to transmit data in Internet of Things systems. Furthermore, it perfectly solves human's need for the mobility and signifi‐ cantly reduces the cost of installation of such systems, comparing it with the deployment cost of wired technologies.

The main characteristics [8] of Internet of Things and its relation with wireless and ad‐hoc networks are presented in **Figure 5**.

The things, which are in the Internet of Things system, are identified and relations among them are specified in the digital domain; it has the ability to communicate to each other using wireless technologies as well to form different ad‐hoc wireless networks of intercon‐ nected things. Their sensing and actuating capabilities can be used for interaction with the surrounding environment. However, IoT‐based system needs to support main factors as heterogeneity of things and devices, efficient energy usage, interoperability and data management as well as security and privacy [8]. The capability of the IoT systems to sup‐ port these factors ensures IoT application in different areas of smart cities [9]: healthcare, energy, buildings, transport, industry, etc. Moreover, the key technologies for Internet of Things‐based systems' application in these areas are wireless sensor networks (WSNs) and MANETs [10, 11].

## **2.3. Internet of Things interaction with MANET and WSN**

Possibilities of wide application of Internet of Things systems in different areas are directly dependent on the opportunities of interoperability between different communication tech‐ nologies and networks in smart environments. The growth of sensors quantity leads to the increasing need of humans for a remote monitoring of different processes in smart environ‐ ments. And this is possible by widespread deployment of wireless sensor networks (WSN). Basically, WSN is a network, which consists of different sensors that are capable autono‐ mously to read information from the object, which is been measured, to handle sensed data, temporarily store it and transfer sensed data to another network node, which is also a sensor. As WSN is a normally centralized network [12], so the data, sensed and transferred from other sensors, are transmitted to the central node, which is usually called the sink. In this man‐ ner, the wireless sensors are able to communicate with each other and thus open very wide usability opportunities of wireless sensor networks in IoT systems. Wireless sensor networks mainly are the basic element in the global Internet of Things system, as sensors have the abil‐ ity to gather information from different things and transmit it over the network. However, the reliability of IoT systems is highly dependent on the power consumption and scalability of WSN [13]. The sensors should transmit measured data so efficiently to the sink, that the energy of their battery would be used at the minimum level. Due to this, the wireless sensor network should be constrained that it can easily accommodate changes in the network. This is related to the lifetime of WSN as well, as low or empty battery leads to the death of sensors. In this way, the routing principles and methods are very important and challenging issue of WSN as data should be transmitted by another sensor, eliminating dead sensor from the rout‐ ing path. And it should be done with respect to Quality of Service (QoS) over wireless sensor networks [14].

Wireless sensor network in general is similar to a mobile ad‐hoc network (MANET), since both are self‐organized and multi‐hopped networks. However, the topology of MANET is more changeable than WSN. MANET protocols can let it to act as a WSN backbone [15] and access wireless sensor network's nodes as well exchange information with WSN about MANET entry points [10]. Due to the task to use sensors' energy efficiency during the data transmission and to reduce data processing time by selecting proper routing protocols and principles, it is a demand for the convergence of MANET and WSN networks. Also, these two networks can enable more effective and reliable cross‐network routing in the Internet of Things context. The intersection of MANET, WSN and Internet of Things the authors called as a MANET‐IoT system, which is discussed in detail in Section 3. **Figure 6** presents the main aspects of interaction between Internet of Things, wireless sensor networks and mobile ad‐ hoc networks.


**Figure 6.** Intersection of IoT, WSN and MANET.

ad‐hoc wireless networks are the most effective and low‐cost way to transmit data in Internet of Things systems. Furthermore, it perfectly solves human's need for the mobility and signifi‐ cantly reduces the cost of installation of such systems, comparing it with the deployment cost

**Figure 5.** Internet of Things characteristics and it relation with wireless and ad‐hoc networks.

The main characteristics [8] of Internet of Things and its relation with wireless and ad‐hoc

The things, which are in the Internet of Things system, are identified and relations among them are specified in the digital domain; it has the ability to communicate to each other using wireless technologies as well to form different ad‐hoc wireless networks of intercon‐ nected things. Their sensing and actuating capabilities can be used for interaction with the surrounding environment. However, IoT‐based system needs to support main factors as heterogeneity of things and devices, efficient energy usage, interoperability and data management as well as security and privacy [8]. The capability of the IoT systems to sup‐ port these factors ensures IoT application in different areas of smart cities [9]: healthcare, energy, buildings, transport, industry, etc. Moreover, the key technologies for Internet of Things‐based systems' application in these areas are wireless sensor networks (WSNs) and

Possibilities of wide application of Internet of Things systems in different areas are directly dependent on the opportunities of interoperability between different communication tech‐ nologies and networks in smart environments. The growth of sensors quantity leads to the increasing need of humans for a remote monitoring of different processes in smart environ‐ ments. And this is possible by widespread deployment of wireless sensor networks (WSN).

of wired technologies.

94 Ad Hoc Networks

MANETs [10, 11].

networks are presented in **Figure 5**.

**2.3. Internet of Things interaction with MANET and WSN**

Networking in the MANET‐IoT system is based on the routing protocols of MANET, routing principles of wireless sensor network and data sensing from things, handling and process‐ ing using Internet of Things. In general, networking of such the system is a very challenging regarding routing aspects. Also, it is related to system mobility and limited resources of all sensors in the network. MANET protocols (most of them) are designed with the focus on QoS [16, 17] and routing in wireless sensor networks is focused on the efficient energy con‐ sumption of network nodes [18]. The connection of different things with limited features to the Internet and interaction with different wireless and mobile ad‐hoc networks must guar‐ antee connectivity, accessibility and reliability of the MANET‐IoT system in smart environ‐ ments. The solutions for the routing protocols of ad‐hoc network modification in order to fulfil the requirements of the Internet of Things were presented by Tian and Hou [19]. Routing principles were changed by integrating IPv6 [20]. However, the interaction of Internet of Things with MANET and WSN requires new, optimized solution for data routing in such the MANET‐IoT system. The authors proposed an algorithm for data routing, which is mainly focused on energy efficiency and safe weighted clustering in the MANET‐IoT system. The authors' proposed solution is described in Sections 3 and 4.

## **3. Proposed solution for data routing in the MANET‐IoT system**

## **3.1. Mathematical model for calculation of network energy cost function**

Sensors establish and maintain routes can proactively or reactively. Proactive protocols peri‐ odically monitor peer connectivity to ensure the ready availability of any path among active nodes. Sensors advertise their routing state to the entire network to maintain a common or partially complete topology of the network. Reactive protocols establish paths only upon request. For MANET sensor network in the IoT system information routing we use combina‐ tion of two routing principles: OLSR (optimized link‐state retrieval) and LEACH (low energy adaptive clustering hierarchy).

Clustering network is efficient and scalable way to organize WSN. Clustering is the method by which sensor nodes in a network organize themselves into hierarchical structures. By doing this, sensor nodes can use battery power more efficiently. A cluster head (CH) is responsible for conveying any information gathered by the nodes in its cluster and may aggregate and compress the data before transmitting it to the sink.

LEACH selects cluster head randomly among all nodes completely. Using our propose algo‐ rithm of energy efficient and safe‐weighted clustering routing for the mobile IoT system, the cluster head is a node that in actual time has more energy than the threshold value.

The sensing range of a sensor is the maximum distance that a sensor can sense. To form clus‐ ters, sensor nodes first elect a CH for each cluster. Nodes in the WSN which are not CHs find the closest CH within the range and become cluster members. The nodes in a cluster only communicate with one another and the CH. The number of CH can be different for every network topology. The propose algorithm implementing dynamical CH rotation that allows

**Figure 7.** MANET‐IoT network with a cluster topology.

us to distribute the workload CH across the mobile MANET‐IoT system and extend overall lifetime of our system.

The MANET‐IoT network with a cluster topology is shown in **Figure 7**. Sensors are grouped into clusters and individual sensors sense data and transmit to cluster heads (CH). Cluster heads aggregate this data and then forward, depending on the tree structure, to the base sta‐ tion or sink node. We assume that each sensor senses L bits and transmit to CH.

The energy consumed by a sensor node consists of these parts [21]:


Networking in the MANET‐IoT system is based on the routing protocols of MANET, routing principles of wireless sensor network and data sensing from things, handling and process‐ ing using Internet of Things. In general, networking of such the system is a very challenging regarding routing aspects. Also, it is related to system mobility and limited resources of all sensors in the network. MANET protocols (most of them) are designed with the focus on QoS [16, 17] and routing in wireless sensor networks is focused on the efficient energy con‐ sumption of network nodes [18]. The connection of different things with limited features to the Internet and interaction with different wireless and mobile ad‐hoc networks must guar‐ antee connectivity, accessibility and reliability of the MANET‐IoT system in smart environ‐ ments. The solutions for the routing protocols of ad‐hoc network modification in order to fulfil the requirements of the Internet of Things were presented by Tian and Hou [19]. Routing principles were changed by integrating IPv6 [20]. However, the interaction of Internet of Things with MANET and WSN requires new, optimized solution for data routing in such the MANET‐IoT system. The authors proposed an algorithm for data routing, which is mainly focused on energy efficiency and safe weighted clustering in the MANET‐IoT system. The

authors' proposed solution is described in Sections 3 and 4.

adaptive clustering hierarchy).

96 Ad Hoc Networks

compress the data before transmitting it to the sink.

**3. Proposed solution for data routing in the MANET‐IoT system**

Sensors establish and maintain routes can proactively or reactively. Proactive protocols peri‐ odically monitor peer connectivity to ensure the ready availability of any path among active nodes. Sensors advertise their routing state to the entire network to maintain a common or partially complete topology of the network. Reactive protocols establish paths only upon request. For MANET sensor network in the IoT system information routing we use combina‐ tion of two routing principles: OLSR (optimized link‐state retrieval) and LEACH (low energy

Clustering network is efficient and scalable way to organize WSN. Clustering is the method by which sensor nodes in a network organize themselves into hierarchical structures. By doing this, sensor nodes can use battery power more efficiently. A cluster head (CH) is responsible for conveying any information gathered by the nodes in its cluster and may aggregate and

LEACH selects cluster head randomly among all nodes completely. Using our propose algo‐ rithm of energy efficient and safe‐weighted clustering routing for the mobile IoT system, the

The sensing range of a sensor is the maximum distance that a sensor can sense. To form clus‐ ters, sensor nodes first elect a CH for each cluster. Nodes in the WSN which are not CHs find the closest CH within the range and become cluster members. The nodes in a cluster only communicate with one another and the CH. The number of CH can be different for every network topology. The propose algorithm implementing dynamical CH rotation that allows

cluster head is a node that in actual time has more energy than the threshold value.

**3.1. Mathematical model for calculation of network energy cost function**


The sensor energy dissipation for sensing activity and logging is evaluated in references [22, 23]

$$E\_s = L^\* V^\* I\_s^\* \, T\_{s'} \tag{1}$$

$$E\_{\log} = \frac{L^\*V}{8} \left( I\_r \, ^\*T\_r + I\_w \, ^\*T\_w \right), \tag{2}$$

where *L* = packet size, *V* = supply voltage, *I*<sup>s</sup> = current required for sensing, *I* r = current required for reading, *I* w = current required for writing, *T*<sup>s</sup> = time duration required for sensing, *T*<sup>r</sup> = time duration required for reading, *T*w = time duration required for writing.

We assume that energy used by CH is higher than that of a normal sensor node, because of additional data aggregation tasks per cycle from other sensors in parallel. Therefore, use coef‐ ficient *ϕ*, which indicate how much CH consumes more energy than a regular sensor node and these coefficients are >1. Then we have

$$E\_{s(\text{CH})} = \text{ } \mathfrak{op}\_1 \text{ } ^\ast E\_s \tag{3}$$

$$E\_{\log(\text{CH})} = \text{ og } ^\ast E\_{\log}.\tag{4}$$

The coefficient φ<sup>1</sup> is related to the number of cluster sensors which sending data to CH at the same time. The coefficient φ<sup>2</sup> is related to scanning the 'b' bit packet of data and loading it into memory.

The communication of neighbouring sensor nodes is enabled by a sensor radio. Radio energy dissipation model is shown in **Figure 8**.

The set of sensor nodes be denoted by ℕ. Each node *i* is assumed to generate data at a constant rate during its lifetime and the initial energy *E*<sup>i</sup> . According to Ben Alla et al. and Shi et al. [24, 26], the energy consumption for transmitting *L* bits from node *i* to *j* can be determined as follows

$$E\_{\rm tr} \left( \dot{\mu} \right) = L \, ^\ast E\_{\rm alec} + L \, ^\ast E\_{\rm amp} \, ^\ast \left( d\_{\parallel} \right) \tag{5}$$

and receive

**Figure 8.** Radio energy dissipation model [24, 25].

$$E\_{\rm re}(ij\ ) = L \, ^\*E\_{\rm oloc} \tag{6}$$

where *L* = packet size, *E*elec = energy dissipated to transmit or receive electronics, *E*amp = energy dissipated by the power amplifier, *α* = 2 for free‐space fading and *α* = 4 for multi‐path fading, *d* = distance.

For evaluation we assume that each sensor node has the same transmission range. The neigh‐ bours of node *i* define as *N*(*ij*) = {*j* ∈ ℕ|(*d*(*ij*) ≤ *d*)}

The transient energy can be defined by

$$E\_{\rm trans} = \left. T\_a \, \prescript{\*}{}{V} \left[ c\_n \, \prescript{\*}{}{I}\_a + \left( 1 - c\_n \right) \, \prescript{\*}{}{I}\_d \right] \tag{7}$$

where *V* = supply voltage, *I a* and *I sl* = current for active and sleeping mode, *Ta* == wake up duration.

If in the network are *N*(*i*) sensor nodes, and have *C*(*j*) clusters, then the total energy of sensor node in cluster of one information sending round can be expressed by equation [27],

$$E\_{\chi}(ij) = \left[ E\_s + E\_{\log} + E\_{\text{tr}} \left( d\_{\vert \rangle} \right) + E\_{\text{trans}} \right] \tag{8}$$

and

*<sup>E</sup>*log <sup>=</sup> \_\_\_\_

where *L* = packet size, *V* = supply voltage, *I*<sup>s</sup>

and these coefficients are >1. Then we have

w = current required for writing, *T*<sup>s</sup>

duration required for reading, *T*w = time duration required for writing.

for reading, *I*

98 Ad Hoc Networks

The coefficient φ<sup>1</sup>

into memory.

follows

and receive

same time. The coefficient φ<sup>2</sup>

dissipation model is shown in **Figure 8**.

**Figure 8.** Radio energy dissipation model [24, 25].

rate during its lifetime and the initial energy *E*<sup>i</sup>

*Etx* (*ij* ) = *L* \* *E*elec + *L* \* *E*amp \* (*dij*)

*L* \* *V* 8 (*I <sup>r</sup>* \* *Tr* + *I*

We assume that energy used by CH is higher than that of a normal sensor node, because of additional data aggregation tasks per cycle from other sensors in parallel. Therefore, use coef‐ ficient *ϕ*, which indicate how much CH consumes more energy than a regular sensor node

*Es*(CH ) = φ<sup>1</sup> \* *Es* (3)

*E*log (CH ) = φ<sup>2</sup> \* *E*log. (4)

The communication of neighbouring sensor nodes is enabled by a sensor radio. Radio energy

The set of sensor nodes be denoted by ℕ. Each node *i* is assumed to generate data at a constant

26], the energy consumption for transmitting *L* bits from node *i* to *j* can be determined as

is related to the number of cluster sensors which sending data to CH at the

is related to scanning the 'b' bit packet of data and loading it

∝

. According to Ben Alla et al. and Shi et al. [24,

*<sup>w</sup>* \* *Tw*), (2)

= time duration required for sensing, *T*<sup>r</sup>

r

= current required

= time

(5)

= current required for sensing, *I*

$$E\_{\rm CI} \begin{Bmatrix} \dot{\jmath} \end{Bmatrix} = \begin{bmatrix} E\_{s(\rm CIb)} + E\_{\rm log(\rm CIb)} + \left( \frac{N}{\mathbb{C}} - 1 \right) L \ \* \ E\_{\rm obs} + E\_{\rm tr} \begin{pmatrix} d\_{\dot{\jmath}} \end{pmatrix} + \frac{N}{\mathbb{C}} \* \ E\_{\rm cr} + E\_{\rm trans(\rm CIb)} \end{Bmatrix} \tag{9}$$

The total energy consumed by the entire network is

$$E\_{\rm IN} = \sum\_{j=1}^{\mathcal{C}} \left( E\_{\rm C:H}(j) + \sum\_{i=1}^{N\_l} E\_{\mathcal{N}}(i\bar{j}) \right) \tag{10}$$

During data transfer phase, the nodes transmit messages to their cluster heads and the cluster heads transmit the aggregated messages to the sink. The data transfer energy consumed by a cluster head and node are defined by [26]

$$E\_{\rm CH\,frame}(\dot{j}) = \left[ \left( \frac{N}{\mathbb{C}} - m \right) L \, ^\ast E\_{\rm solar} + \left( \frac{N}{\mathbb{C}} - m + 1 \right) E\_{\rm tr/m} \left( d\_{\dot{j}} \right) + L \, \in \left( d\_{\dot{j}} \right)^4 \right], \tag{11}$$

$$E\_{\mathbb{N}(\text{frame})}(\vec{\eta}) = \left[ L \, \, ^\*E\_{\text{tx}\,\text{ox}}\left(d\_{\vec{\eta}}\right) + L \, \in \left(d\_{\vec{\eta}}\right)^2\right].\tag{12}$$

There are *C* clusters and *N* nodes. In each iteration, *m* nodes are elected for each cluster. Thus, in each iteration *C\*m* nodes are elected as members of head‐sets. The number of iterations required for all *n* nodes to be elected ( \_*N <sup>C</sup>* \* *<sup>m</sup>*) which is the number of iterations required in one round. Iteration consists of two phase: election and data transfer stage. Therefore the energy consumed in one iteration

$$E\_{\rm CH/lator} = E\_{\rm CH/olect} + E\_{\rm CH/lata} \tag{13}$$

$$E\_{\text{N/iter}} = E\_{\text{N/elect}} + E\_{\text{N/data}} \tag{14}$$

where energy consumptions in a data transfer stage are

$$E\_{\rm CH/data} = \omega\_1 \, ^\ast D F \, ^\ast E\_{\rm CH/frame} \tag{15}$$

$$E\_{\text{N\% data}} = \omega\_2 \, ^\ast D F \, ^\ast E\_{\text{N\% frame}} \tag{16}$$

$$
\mathcal{E}\_{\text{N/data}} = \omega\_2 \, ^\ast D \mathbf{F} \, ^\ast \mathcal{E}\_{\text{N/frame}} \tag{16}
$$

$$
\omega\_1 = \left(\frac{1}{\frac{N}{C} - m + 1}\right) \ast \frac{1}{C} \tag{17}
$$

$$
\omega\_1 = \begin{pmatrix} \frac{N}{C} - m + 1 \\\\ \frac{N}{C} - m + 1 \end{pmatrix} \quad \overline{\mathbb{C}} \tag{17}
$$

$$
\omega\_2 = \begin{pmatrix} \frac{N}{C} - m \\\\ \frac{N}{C} - m + 1 \end{pmatrix} \* \frac{1}{\mathbb{C}} \tag{18}
$$

The start energy *E*start is the energy of a sensor node at the initial start time. This energy should be sufficient for at least one round. In one round, a node becomes a member of head‐set for one time and a non‐cluster head for ( \_*N <sup>C</sup>* \* *<sup>m</sup>* − 1 )t times. An estimation of *Estart* are used equation

$$E\_{\text{start}} = \frac{E\_{\text{CHolar}} + E\_{\text{Nolar}}}{C} + \frac{DF}{C} \ast \left(\omega\_1 \, ^\ast E\_{\text{CH}|\text{frame}} + \omega\_2 \, ^\ast E\_{\text{N/frame}}\right) \tag{19}$$

Our goal is minimizing the *E*TN by using a dynamic clustering algorithm and dynamic cluster head selection algorithm, which is adaptive to the current MANET‐IoT system conditions, analysing sensor nodes battery energy state and reduce their energy consumption.

Based on the location of the sink node (or base station), the optimal cluster numbers are applied to the two different locations that are both the centre of the sensing area and the outside of the sensing area. The optimal cluster number for the centre of the sensing area is given by [28, 29]

$$\mathcal{C}\_{\rm opt} = \sqrt{N} \tag{20}$$

where *N* is the number of sensor nodes.

The optimal cluster number for the outside of the sensing area is given by

$$C\_{\rm opt} = \sqrt{N} \, ^\ast \frac{M}{\sqrt{M^2 + 6 \, ^\ast d\_{\rm sink}}} \tag{21}$$

where *N* is the sensing area, *d*sink is distance from the centre of sensing area to the outside loca‐ tion of the sink and *M* is the network diameter.

Optimization model has also been used to study maximum lifetime conditions for sensor networks. The model balances the competing minimum energy and maximum information objectives by limiting the minimum information to be extracted to the station and minimizing the energy required to do this. The objective function is to maximize the network lifetime of the given wireless sensor network configuration [30]

*E*CH |iter = *E*CH |elect + *E*CH |data (13)

*EN*|iter = *EN*|elect + *EN*|data (14)

*E*CH |data = *ω*<sup>1</sup> \* *DF* \* *E*CH |frame (15)

*EN*|data = *ω*<sup>2</sup> \* *DF* \* *EN*|frame (16)

\_ *NC* <sup>−</sup> *<sup>m</sup>* + 1

\_ *N C* \_ − *m*

The start energy *E*start is the energy of a sensor node at the initial start time. This energy should be sufficient for at least one round. In one round, a node becomes a member of head‐set for

Our goal is minimizing the *E*TN by using a dynamic clustering algorithm and dynamic cluster head selection algorithm, which is adaptive to the current MANET‐IoT system conditions,

Based on the location of the sink node (or base station), the optimal cluster numbers are applied to the two different locations that are both the centre of the sensing area and the outside of the sensing area. The optimal cluster number for the centre of the sensing area is

\_\_

*<sup>N</sup>* \* \_\_\_\_\_\_\_\_\_\_\_ *<sup>M</sup>* <sup>√</sup> \_\_\_\_\_\_\_\_\_\_\_\_\_ *<sup>M</sup>* 2 + 6 \* *d*sink 2 

analysing sensor nodes battery energy state and reduce their energy consumption.

The optimal cluster number for the outside of the sensing area is given by

\_\_

where *N* is the sensing area, *d*sink is distance from the centre of sensing area to the outside loca‐

\_ *N <sup>C</sup>* <sup>−</sup> *<sup>m</sup>* + 1

\_*N*

 \_\_\_\_\_\_\_\_\_\_\_ *C* + \_\_\_ *DF*

\_1

) \* \_\_1

) \* \_\_1

*<sup>C</sup>* (17)

*<sup>C</sup>* (18)

*<sup>C</sup>* \* *<sup>m</sup>* − 1 )t times. An estimation of *Estart* are used equation

*<sup>C</sup>* \* (*ω*<sup>1</sup> \* *E*CH |frame + *ω*<sup>2</sup> \* *EN*|frame) (19)

*N* (20)

(21)

where energy consumptions in a data transfer stage are

*<sup>ω</sup>*<sup>1</sup> <sup>=</sup> (

*<sup>ω</sup>*<sup>2</sup> <sup>=</sup> (

one time and a non‐cluster head for (

*<sup>E</sup>*start <sup>=</sup> *<sup>E</sup>*CH |elect + *EN*|elect

*C*opt = √

where *N* is the number of sensor nodes.

*C*opt = √

tion of the sink and *M* is the network diameter.

given by [28, 29]

100 Ad Hoc Networks

$$Z = \operatorname\*{maximize} \left\{ f \left( \mathfrak{s} \left( \frac{\mathbb{E}\_{\mathbb{N} \times \text{CI}}}{\mathbb{E}\_{\text{IN}}} \right), \beta \left( \frac{T\_{\text{y}}}{T\_{\text{max}}^{\text{x}}} \right), \delta \left( \mathbb{S}\_{\text{cb}, \text{N}} \right), \varepsilon \left( \mathbb{H}\_{\text{route}} \right) \right) \right\} \tag{22}$$

where ∝ is the coefficient of energy, *β* is the coefficient of lifetime, *δ* is the coefficient of signal strength and *ε* the is coefficient of route hops.

In general, we analysed an impact factor; therefore, sensor node parameters tied optimum logarithmic function:

$$f(\infty, \beta, \delta, \varepsilon) = \log\left\{\infty + \beta + \delta + \varepsilon\right\} \tag{23}$$

Because the optimum function will be transformed into a graph of weight function *G*(*x*, *y*, *z*,*ϑ*), Therefore, this function must be *G*(*x*, *y*, *z*,*ϑ*) > 0 , and *f*(∝,*β*, *δ*, *ε*) > 0 . During the work that has been chosen continuous logarithmic function with the value for the application of the environment must be non‐negative. The range of parameter is very different : energy consumed by the node *E TN* is from 2 to 85%, *Tn <sup>n</sup>* ‐ = from 1 to *T <sup>γ</sup>* of *N*, signal strength from –30 to –110 dBm, route hops from 1 to 16. For eliminating differences bound parameters and unifying their range have been consis‐ tently chosen to apply different parameter values for the calculation methods. The set Υ consisting of this coefficients and can be defined Υ = {∝,*β*, *δ*, *ε* ∈ ℝ}. The function coefficient ∝ is con‐ verted to percentages, and restriction of this parameter is ∝ ∈ [0 1 ]. Use the transform manipula‐ tion ∝ → (1 − *x*) <sup>2</sup> . Other parameters are: *β* ∈ [1 *Tn n* ], *β* → (0.15*z*), *δ* ∈ [0.027 0.85], *δ* → (0.027*y*) 2 , *ε* ∈ [1 16], *ε* → 0.1*ϑ*. According to the general definition of optimum function range, the common analytical function is given by

$$f(\mathbf{x}, \boldsymbol{\beta}, \boldsymbol{\delta}, \varepsilon) = \log\left(\left(1 - \mathbf{x}\right)^2 + \left(0.027y\right)^2 + 0.15z + 0.18\right) \tag{24}$$

The derived common analytical optimum function will be installing to the composite energy/ lifetime efficient routing model. This function will be converted into the weight function, and this function will be used for calculations of sensor node values that are used in graph theory.

### **3.2. Proposed algorithm for data routing in the MANET‐IoT system**

In developing energy aware route selection schemes, we assume that WSN is a graph with vertices indicating sensor nodes and edges representing communication links between ver‐ tices. Graphs are a suitable model to describe complex networks, such as WSN. The weight on a vertex denotes residual energy of that node and the weight on an edge indicates the amount of energy that a node requires to transmit a unit of information along the edge [31]. The residual energy of a route is defined as the lowest energy level of any node on the route.

**Figure 9.** The flowchart of the proposed MANET‐IoT system routing algorithm.

The energy consumed along a route is the sum of weights on all the edges present on the route. The most appropriate energy aware route selection scheme for WSNs is to utilize those nodes having higher energy levels and avoid those having lower energy levels, such that the overall energy consumption along the data forwarding path is minimized. For solution of this problem we composite routing over MANET‐IoT networks using the combination of MANET and WSNs routing principles.

The proposed algorithm adopts a dynamical monitoring, with controls the energy of the clus‐ ter heads, and a predefined threshold value. The purpose of this monitoring mechanism is for transferring cluster head based on the comparison result between the energy of cluster head and threshold value.

The algorithm presented in **Figure 9** has three phases: setup, steady and threshold. First step is a cluster head selection. After the cluster head selection phase, all the selected cluster heads send an advertisement message to all the non‐cluster head nodes in the field. Based on the received signal strength of the advertisement message, the non‐cluster head nodes decide their cluster heads for the current round and send back a join request message to their selected cluster heads informing their membership which leads to the formation of cluster. The message sent to the cluster heads includes the node's ID as well as the location of the sender node.

When all the sensor nodes are deployed, the entire network starts to select the cluster heads and carry out clustering and layering. Then, the nodes begin to periodically collect data and transmit them to the sink node. With the change of time, the network topology structure is also changing. If cluster head energy is lower than the predefined threshold value, the

**Figure 10.** Messages exchange between nodes using the proposed algorithm.

**Figure 9.** The flowchart of the proposed MANET‐IoT system routing algorithm.

102 Ad Hoc Networks

**Figure 11.** Proposed common route choosing algorithm.

`third loop is applied to replace cluster head by another node, which poses the largest energy within the cluster. The new cluster head continues to cooperate with cluster members. This way protects the cluster heads, which have lower energy. This mechanism can protect cluster heads from quick death and prolong the network lifetime. The messages exchanges between nodes are shown in **Figure 10**.

When have all information about network and nodes, then are choosing the routing method for transmission information. In this research work, our proposed common route choosing algorithm is presented in **Figure 11**. For evaluation network lifetime three route path selec‐ tion methods are used: NP (node place), BST (node battery state) and ER (energy resource). The NP aim to find route with minimum hop and for searching nodes, node location param‐ eters or methods are used (RSSI, AoA and ToA). The cluster head evaluates all neighbour nodes that are in the cluster. If the information does not satisfy required criterions, cluster heads send message for the neighbour cluster head to help find route to the sink. BST selects node which battery state is the higher. Using the ER method we calculate all network energy resource using the proposed algorithm. In the new algorithm, a threshold value is added in order to monitor the energy of node.

## **4. Performance evaluation of routing algorithms**

Simulation experiments are carried out using Matlab (2010b). The principal goal of these sim‐ ulations is to analyse our algorithm and compare it with other. For analysing and comparing the performance of our proposed method we used two metrics: node energy and network lifetime (or the number of rounds). Network lifetime is one of the main characteristics to evaluate the performance of sensor networks. Such a parameter includes coverage, connectiv‐ ity and node availability. The network lifetime *Tn n* is defined as that the sensor network loses connectivity. The route lifetime is defined as the first node fails, thus

$$T\_{\text{route}}^{\*} = \min T\_{\gamma'} \text{ } \gamma \in N \tag{25}$$

where *Tγ* is the lifetime of node *γ* in *ij*‐route.

`third loop is applied to replace cluster head by another node, which poses the largest energy within the cluster. The new cluster head continues to cooperate with cluster members. This way protects the cluster heads, which have lower energy. This mechanism can protect cluster heads from quick death and prolong the network lifetime. The messages exchanges between

When have all information about network and nodes, then are choosing the routing method for transmission information. In this research work, our proposed common route choosing algorithm is presented in **Figure 11**. For evaluation network lifetime three route path selec‐ tion methods are used: NP (node place), BST (node battery state) and ER (energy resource). The NP aim to find route with minimum hop and for searching nodes, node location param‐ eters or methods are used (RSSI, AoA and ToA). The cluster head evaluates all neighbour nodes that are in the cluster. If the information does not satisfy required criterions, cluster heads send message for the neighbour cluster head to help find route to the sink. BST selects node which battery state is the higher. Using the ER method we calculate all network energy resource using the proposed algorithm. In the new algorithm, a threshold value is added in

nodes are shown in **Figure 10**.

104 Ad Hoc Networks

**Figure 11.** Proposed common route choosing algorithm.

order to monitor the energy of node.

We test the proposed algorithm using an initial number of alive sensors *N* = 17, each with a range *d* = 8 m. We use a network of size *M* = 20 × 20 m, with a sink located at point coordinates [*x* = 7, *y* = 18]. According our proposed solution, first we calculated the optimal number of cluster using Eq. (21). As shown in **Figure 12** that in our case the optimal number of clusters are 3.

The network at which we apply our tests is shown in **Figure 13**.

**Figure 12.** Optimum number of clusters versus sensors and network size.

**Figure 13.** The test network structure.

**Figure 14. S**imulation results (a) network lifetime, (b) each node battery energy, (c) dependency of node energy on the number of rounds.

**Figure 13** shows the network topology structure. The green round circle denotes the sink. The blue round circle denotes the cluster heads. The red and yellow round circles denote the sen‐ sors, but red can be the cluster head also. The connection line denotes the path of a single hop from the sensor nodes to the cluster head. In the first scenario, sensors are sending informa‐ tion to the sink over three cluster heads. **Figure 14** presents the simulation results (a) network lifetime, (b) each node battery energy and (c) dependency of node energy on the number of rounds.

As can been seen in **Figure 14**, using such information to the routing method network lifetime (dimension is days) is 216, and the fastest losing power nodes 3 and 7. During the analysis of this data, we observe that the consumption of energy distribution is unbalanced in the network and observable the weakness network location. The next simulation step was to use routing change over the simulation period, when the node energy level is lower than the

**Figure 15.** WSN lifetime using a node energy level threshold value for routing change.

**Figure 16.** WSN energy parameters.

**Figure 13** shows the network topology structure. The green round circle denotes the sink. The blue round circle denotes the cluster heads. The red and yellow round circles denote the sen‐ sors, but red can be the cluster head also. The connection line denotes the path of a single hop from the sensor nodes to the cluster head. In the first scenario, sensors are sending informa‐ tion to the sink over three cluster heads. **Figure 14** presents the simulation results (a) network lifetime, (b) each node battery energy and (c) dependency of node energy on the number of

**Figure 14. S**imulation results (a) network lifetime, (b) each node battery energy, (c) dependency of node energy on the

As can been seen in **Figure 14**, using such information to the routing method network lifetime (dimension is days) is 216, and the fastest losing power nodes 3 and 7. During the analysis of this data, we observe that the consumption of energy distribution is unbalanced in the network and observable the weakness network location. The next simulation step was to use routing change over the simulation period, when the node energy level is lower than the

rounds.

number of rounds.

**Figure 13.** The test network structure.

106 Ad Hoc Networks

threshold value. **Figure 15** shows that after 90 rounds (a), the node 4 energy level is lower than the threshold value, therefore the sending information form node 7 we redirect from node 4 to node 3 and from nodes 11–7 to nodes 11–8. The next time (b) after 192 round we change other routes. Using this algorithm, our simulation network lifetime was 368 (c). The energy parameters are shown in **Figure 16**.

For evaluation of the effectives of our proposed algorithm, the next simulation was carried out. The simulation results are presented in **Figures 17** and **18**.

By comparing the results, we found that by using our proposed algorithm, the network life‐ time is the longest than using simple or clustering without weight routing methods. The main objective of the dynamic cluster head rotation mechanism is to evenly distribute the energy load among all the sensor nodes so that there are no overly utilized sensor nodes that will run out of energy before the others. And we can see that the distribution of network nodes' energy consumption becomes smoother (**Figure 18**). The assumptions made for compare different routing are as follows: network nodes are homogeneous and not mobility; they are equipped

**Figure 17.** WSN lifetime using the proposed algorithm.

**Figure 18.** WSN energy parameters using the proposed algorithm.

with power control; have active and sleep mode; each sensor has a unique identifier and uni‐ formly deployed over the target area to continuously monitor the environment.

**Figure 19.** The simulation network topology.

with power control; have active and sleep mode; each sensor has a unique identifier and uni‐

formly deployed over the target area to continuously monitor the environment.

**Figure 18.** WSN energy parameters using the proposed algorithm.

**Figure 17.** WSN lifetime using the proposed algorithm.

108 Ad Hoc Networks

**Figure 20.** Network nodes' "dead" versus the number of rounds using three different routing algorithms.

The second simulations are conducted for evaluating effectiveness of the proposed algorithm ER compare with other two (node place (NP) and battery state (BST)). The network topology is presented in **Figure 19**.

After such a network simulation we have seen over time as changing the number of nodes on the network (**Figure 20**). The first network node falls out after 15 round using the BP method and using our proposed algorithm ER after 23 rounds. When using the BST method the first

**Figure 21.** Average energy consumption of one node.

**Figure 22.** The network topology at the end of network lifetime.

node falls out from the network after 41, but then very suddenly battery energy of all nodes is ends. Network lifetimes of different methods are NP = 38, BST = 57 and ER = 63.

The average energy consumption of one node is shown in **Figure 21**, indicating the aver‐ age energy at the time, which is utilized in the network for one node. Analysing this graph, we can see that the largest energy consumption is using the NP method. The BST method compared to the other two at the beginning of a lifetime of approximately constant amount of energy per one node is 32 rounds. Late using BST method of energy consumption per node sharply increases when the network nodes begin to leave one after the other. Such a sharp jump is because that the network nodes in more or less all the battery depletes a similar amount of energy and begins to fall out of the network, all one after the other. When the other two methods fall just in time for most working nodes through which passes the shortest route.

As we can see in **Figure 22** at the end of the network lifetime, there are nodes which energy is not zero and its can still be used in all three cases. It is an indicator of the best network energy resource utilization.

According simulation results, the NP and ER method network resource utilization is very similar. Using the BST routing method we can extend the time up to the first node falling out. This is important for the network when all sensors are the same and send the similar information.

## **5. Conclusion**

In this chapter, we presented the proposed algorithm of energy efficient and safe‐weighted clustering routing for the mobile IoT system using a combination of MANET and WSN rout‐ ing principles. We choose the clustering method, because each sensor nodes in a network organize themselves into hierarchical structures. The simulation result show that if we use combination method for information routing in the wireless sensor network, we increase the lifetime of sensors in overall Internet of Things system. Because we used dynamical clus‐ ter head selection, the weighting factors are added for routing from the sensor to the sink. When the network is heterogeneous and mobility, the using routing weight is very important, because sensors have different characteristics, dynamical distance from the sink and CH node and if we want to choose the best route we need to calculate some objectives function. And this function must have possibility to eliminating differences bound parameters. For solving this we used the weight function, and this function will be used for calculations of each sensor node value and then calculation all route cost function.

## **Acknowledgement**

This work was partially supported by the ICT COST Action IC1304—Autonomous Control for a Reliable Internet of Services (ACROSS), November 14, 2013—November 13, 2017, funded by European Union.

## **Author details**

node falls out from the network after 41, but then very suddenly battery energy of all nodes is

The average energy consumption of one node is shown in **Figure 21**, indicating the aver‐ age energy at the time, which is utilized in the network for one node. Analysing this graph, we can see that the largest energy consumption is using the NP method. The BST method compared to the other two at the beginning of a lifetime of approximately constant amount of energy per one node is 32 rounds. Late using BST method of energy consumption per node sharply increases when the network nodes begin to leave one after the other. Such a sharp jump is because that the network nodes in more or less all the battery depletes a

ends. Network lifetimes of different methods are NP = 38, BST = 57 and ER = 63.

**Figure 22.** The network topology at the end of network lifetime.

**Figure 21.** Average energy consumption of one node.

110 Ad Hoc Networks

Rasa Bruzgiene\*, Lina Narbutaite and Tomas Adomkus

\*Address all correspondence to: rasa.bruzgiene@ktu.lt

Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania

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## **Radio Frequency-Based Indoor Localization in Ad-Hoc Networks in Ad-Hoc Networks**

**Radio Frequency-Based Indoor Localization** 

Mehdi Golestanian, Joshua Siva and Christian Poellabauer Christian Poellabauer Additional information is available at the end of the chapter

Mehdi Golestanian, Joshua Siva and

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66523

## **Abstract**

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114 Ad Hoc Networks

2008;**7**(8):837–846.

The increasing importance of location‐aware computing and context‐dependent infor‐ mation has led to a growing interest in low‐cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range‐based and range‐free tech‐ niques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localiza‐ tion are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth‐based localization) to illustrate localization chal‐ lenges and how some of them can be overcome.

**Keywords:** ad‐hoc networks, wireless sensor networks (WSNs), localization, radio frequency (RF) signaling, Bluetooth low energy (BLE), Wi‐Fi, XBee, indoor localization, range‐based localization

## **1. Introduction**

Localization is a key requirement of most mobile and wireless networks. For example, wireless sensor networks are often deployed in an ad‐hoc fashion, which means that the locations of the sensors are not known a priori [1]. Localization is necessary to provide a physical context

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to sensor readings for services such as intrusion detection, inventory and supply chain man‐ agement. It is also a fundamental task for sensor network services such as geographic routing and coverage area management [1, 2]. Over the last few decades, localization technologies have undergone significant progress and they now play a crucial role for many location‐ and context‐aware services and applications such as navigation, robotics, patient monitoring and emergency response systems.

Localization algorithms can be classified into either range‐based or range‐free. Range‐based localization (like GPS and some forms of cellular‐based positioning) can have very high accu‐ racy by taking measurements of a signal at the cost of computational and implementation complexity. In contrast, range‐free localization algorithms (such as simple cell‐based localiza‐ tion) can provide a less accurate position (but perhaps "good enough" for the specific pur‐ pose) with a much more simple implementation. Additionally, the physical environment can have a great impact on the performance of a chosen localization algorithm. For example, while global positioning system (GPS) has been the primary localization approach for outdoor envi‐ ronments, indoor environments (and all other GPS‐denied areas) face severe challenges such as the limited accuracy of techniques like cellular‐based positioning [2] and radio propagation characteristics that can differ significantly from outdoor environments [3]. Therefore, as one of the most challenging topics in localization, indoor localization has attracted the attention of many researchers both in industry and academia.

In recent years, a variety of novel approaches have been presented, including positioning based on FM signaling [4], Wi‐Fi trilateration [5] and low energy Bluetooth beaconing (e.g., iBeacons) [6]. Most such techniques rely on the received signal strength indicator (RSSI) as the main parameter to extract distance information with acceptable accuracy [7]. Infrared‐based indoor localization [8], signal fingerprint‐based localization [9] and image‐based indoor local‐ ization [10] are examples of localization that do not rely on RSSI or other similar signal or link measurement for distance determination. However, these methods typically introduce higher costs and complexity and may not be as readily available.

Availability, cost, reliability and accuracy of localization are the most important parame‐ ters when selecting a localization method and technology. Among existing technologies, RF‐based methods based on Bluetooth and Bluetooth low energy (BLE), Wi‐Fi and XBee are popular choices due to their availability (e.g., BLE is available on most modern smart devices), low power consumption (particularly BLE and XBee) and low cost. Although RF‐based methods have several advantages for localization purposes, they also have a sig‐ nificant shortcoming in indoor environments, i.e., prior work has shown that RSSI is not a reliable metric and that it can easily be affected by the unique characteristics of the indoor environment [11]. On the other hand, timing‐based ranging approaches have attracted much attention using technologies such as XBee/ZigBee [12], ultra wideband (UWB) [13] and Wi‐Fi [14]. Timing methods come with their own collection of advantages and disadvantages in an ad‐hoc and indoor environment. In this chapter, we investigate recent work involving in the field of RF‐based localization to address the challenges of RSSI‐ and timing‐based localiza‐ tion in indoor environments and then review approaches that can be used to address these challenges.

The structure of the chapter is organized as follows: Section 2 presents the basic concepts of localization and its application including a discussion of common characteristics of local‐ ization algorithms. Section 3 narrows down the localization problem to indoor localization algorithms based on RF signaling. Section 4 discusses some of the challenges associated with RF‐based indoor localization. Section 5 reviews how some of these challenges are addressed in recent work and addresses future research directions to tackle the remaining challenges.

## **2. Introduction to localization**

## **2.1. Overview**

to sensor readings for services such as intrusion detection, inventory and supply chain man‐ agement. It is also a fundamental task for sensor network services such as geographic routing and coverage area management [1, 2]. Over the last few decades, localization technologies have undergone significant progress and they now play a crucial role for many location‐ and context‐aware services and applications such as navigation, robotics, patient monitoring and

Localization algorithms can be classified into either range‐based or range‐free. Range‐based localization (like GPS and some forms of cellular‐based positioning) can have very high accu‐ racy by taking measurements of a signal at the cost of computational and implementation complexity. In contrast, range‐free localization algorithms (such as simple cell‐based localiza‐ tion) can provide a less accurate position (but perhaps "good enough" for the specific pur‐ pose) with a much more simple implementation. Additionally, the physical environment can have a great impact on the performance of a chosen localization algorithm. For example, while global positioning system (GPS) has been the primary localization approach for outdoor envi‐ ronments, indoor environments (and all other GPS‐denied areas) face severe challenges such as the limited accuracy of techniques like cellular‐based positioning [2] and radio propagation characteristics that can differ significantly from outdoor environments [3]. Therefore, as one of the most challenging topics in localization, indoor localization has attracted the attention of

In recent years, a variety of novel approaches have been presented, including positioning based on FM signaling [4], Wi‐Fi trilateration [5] and low energy Bluetooth beaconing (e.g., iBeacons) [6]. Most such techniques rely on the received signal strength indicator (RSSI) as the main parameter to extract distance information with acceptable accuracy [7]. Infrared‐based indoor localization [8], signal fingerprint‐based localization [9] and image‐based indoor local‐ ization [10] are examples of localization that do not rely on RSSI or other similar signal or link measurement for distance determination. However, these methods typically introduce higher

Availability, cost, reliability and accuracy of localization are the most important parame‐ ters when selecting a localization method and technology. Among existing technologies, RF‐based methods based on Bluetooth and Bluetooth low energy (BLE), Wi‐Fi and XBee are popular choices due to their availability (e.g., BLE is available on most modern smart devices), low power consumption (particularly BLE and XBee) and low cost. Although RF‐based methods have several advantages for localization purposes, they also have a sig‐ nificant shortcoming in indoor environments, i.e., prior work has shown that RSSI is not a reliable metric and that it can easily be affected by the unique characteristics of the indoor environment [11]. On the other hand, timing‐based ranging approaches have attracted much attention using technologies such as XBee/ZigBee [12], ultra wideband (UWB) [13] and Wi‐Fi [14]. Timing methods come with their own collection of advantages and disadvantages in an ad‐hoc and indoor environment. In this chapter, we investigate recent work involving in the field of RF‐based localization to address the challenges of RSSI‐ and timing‐based localiza‐ tion in indoor environments and then review approaches that can be used to address these

emergency response systems.

116 Ad Hoc Networks

challenges.

many researchers both in industry and academia.

costs and complexity and may not be as readily available.

Before we investigate localization in an indoor environment with radio frequency devices in detail, we must first establish a solid understanding of localization. The general problem of localization is the determination of the position of an object (or person) within a specific space. This location could be within some local coordinate system or it could be a global coordinate system, such as latitude and longitude coordinates on the Earth's surface. The best‐known solution to the localization problem is the global positioning system (GPS), which uses the time of arrival of signals from multiple satellites to determine where the GPS receiver is located within several meters. Moreover, companies such as Apple, Microsoft, Google and Skyhook all have methods of estimating a user's location by fusing GPS, Wi‐Fi and cellular data for a multitude of purposes ranging from enabling context‐aware applications to locat‐ ing stolen devices. The usefulness and importance of localization are difficult to understate as is the difficulty and multitude of approaches to solving this problem. Not all localization systems are created equal, for they are implemented using a variety of technologies according to the necessities of the system in which the location is required. For example, locating a car on the road has different requirements than locating a person in a shopping mall or a drone in a building. Based on these requirements, such as high accuracy or low power consumption, a localization system can be developed either by repurposing or piggybacking on existing technology or by using an existing localization system, namely, GPS.

A common way that localization is implemented is to determine the distances between a tar‐ get and a sufficient number of reference points. This process is referred to as ranging. Once these distances are known, then it is possible to approximate the location of the target geo‐ metrically through trilateration or min‐max. Trilateration works by using the measured dis‐ tance as the radius of a circle around a reference point. The intersection of three circles is the estimated location of the target. If the circles do not intersect at a single point then further action must be taken to improve the estimate. An example of trilateration in two dimensions is shown in **Figure 1**. The min‐max method takes the measured distance between the target and the reference point and uses it to form a square with side length twice that distance with the reference point that made the measurement at the center. The target is assumed to be in the center of the rectangle formed by the intersecting reference nodes' squares. An example of min‐max in two dimensions is shown in **Figure 2**. Methods of localization using trilateration or min‐max belong to the group of range‐based localization methods. Because they are based on geometry and they are, perhaps, the most straightforward localization algorithms and have the potential for providing highly accurate location estimates.

**Figure 1.** Trilateration technique with three reference points.

**Figure 2.** Min‐max technique with three reference points.

GPS is an example of such a system, because once the time of flight (ToF) of the signals from the GPS satellites to the GPS receiver is known, then it is simply a matter of multiplying the speed of the signal (the speed of light) by the time it took to travel from the satellites to the receiver (accounting for relativistic effects along the way). Note that this relies on the sender and receiver having synchronized clocks to determine the time of arrival (ToA). Time‐based ranging can also be accomplished through what is called time difference of arrival (TDoA). In this method, one needs to only measure the difference in arrival time between two signals with different (known) propagation speeds. For example, one could measure the arrival time of a radio signal and that of an auditory signal and because the speed of the signals is known, it would be a simple matter to determine the distance to the target which initiated both signals. The final method of ranging relies on measuring the amount that a signal decays as it travels from (or to) a known location. That is, if we know how a signal such as a sound or a radio wave diminishes as it travels and we also know the strength of the signal to begin with, then we can determine how far the signal traveled by measuring its strength at the receiver.

It is not always necessary to determine the distance between some reference points and a target. Methods of localization that estimate a location without using ranges are called range‐ free. This class of localization algorithm is characterized by its use of network connectivity and geometry as well as radio characteristics to estimate the location of a target node. For example, given three anchor nodes (nodes whose locations are known) and a target that has a connection with all of them allow us to estimate the target's location by finding the centroid of the triangle created by the three anchors. There are many methods that improve this simple "find the centroid" approach as well as other methods that estimate distances based on the known anchor positions and the connectivity of the network. In general, range‐based local‐ ization methods are characterized by fairly high accuracy at the cost of higher computational complexity compared to range‐free localization. Additionally, some ranging methods, such as those based on timing, may require hardware that devices in a resource‐constrained sensor network may not have.

Localization in an ad‐hoc network further complicates the localization process by placing stronger constraints on the resources available and how they are accessed. Ad‐hoc networks, whether they are mobile ad‐hoc networks (MANETs), vehicular ad‐hoc networks (VANETs), or wireless sensor networks (WSNs), impose some limit on the amount of information avail‐ able at any particular node and come with the added complication of having to share that data in an ad‐hoc manner with other nodes in the network. For localization purposes, this means that combining widespread sources of information to estimate a range or location may no lon‐ ger be possible. In fact, it may not even be possible to estimate an absolute location if the net‐ work does not include any anchors. Additionally, adding in the issue of mobility, whether it is the somewhat predictable mobility of a VANET or the less predictable movement of nodes in a MANET, turns the localization problem into a tracking problem and also needs to be balanced with how and where localization computations are going to be carried out in the network.

## **2.2. Characteristics of localization algorithms**

on geometry and they are, perhaps, the most straightforward localization algorithms and

have the potential for providing highly accurate location estimates.

118 Ad Hoc Networks

**Figure 1.** Trilateration technique with three reference points.

**Figure 2.** Min‐max technique with three reference points.

Aside from the primary classification as a range‐based or range‐free, which is largely dictated by the hardware employed, localization systems can be further distinguished by their various characteristics including their network topology, computational strategy, use of anchors and ability to handle mobility, whether it be the anchors or the targets that are mobile.

The network topology is closely associated with the method of computation and has a major impact on the localization methods available because it dictates how information is going to be passed and to whom it will be passed. For example, in a star topology, the end nodes will not be able to communicate directly (or at all) with neighboring end nodes, which means that timing‐based approaches may be difficult to implement and computation will need to be carried out at the master node. However, if a mesh topology is available, then there are few restrictions (imposed by the network) on the method of localization and it opens the door to distributed computation and cooperative localization.

The availability of anchors can be a hardware constraint if a global reference frame is in use and GPS is unavailable. Anchors can also take the form of a node with a known location in an arbitrary (but consistent) frame of reference, so lacking even these data are also a possibility. The availability of an anchor could be determined by the nodes' mobility and location; for example, a node moving about within a building may become an anchor when it is in a loca‐ tion where it can receive a GPS signal. Anchor‐based localization algorithms are those that *require* anchors to function, whereas anchor‐free algorithms could operate in their absence. Range‐free localization algorithms that rely on finding the centroid of the shape formed by a set of nodes can only function if the locations of the nodes are known. In contrast, the range‐ based localization (e.g., ToF) can localize targets without anchors.

The various forms of mobility (or lack thereof) that a localization algorithm can handle are a characteristic that may be imposed by the hardware on the nodes or it may simply be an assump‐ tion made by the designers. For example, the computational burden than can be handled by sen‐ sors in a WSN may be severely limited, so the design of an algorithm that can handle precise tracking on such hardware would be infeasible. Similarly, the algorithm designers may assume that the targets to be localized are generally static or that their movements are fairly predictable.

In addition to these characteristics, considerations must be made regarding the impact of the localization algorithm on the normal network communication and the environment in which a localization system is to be deployed. The former consideration is one that is more impor‐ tant with regard to optimization, so it will not be discussed here. The latter is a point that will be considered in the next section as we discuss radio frequency (RF)‐based localization with a focus on its implementation indoors and the challenges arising from that.

## **3. RF‐based indoor localization**

## **3.1. Radio frequency devices**

The radio frequency devices under consideration in this chapter are Wi‐Fi, Bluetooth/BLE and XBee devices due to their wide commercial availability and frequent use in ad‐hoc networks. Wi‐Fi is a well‐known and ubiquitous radio technology based on the IEEE 802.11 standard. It operates in the 2.4 and 5 GHz bands with three nonoverlapping channels in the former and two dozen in the latter. Bluetooth falls under the IEEE 802.15.1 standard, operates in the 2.4 GHz band and is designed for wireless communication over short ranges. Instead of focusing on replacing wired networks, Bluetooth has found great usefulness in enabling communication on a smaller scale. BLE is a version of Bluetooth aimed at reducing power consumption, which has made it possible to integrate Bluetooth into power‐constrained devices. BLE has also led to the development of Bluetooth beacons, which can be used for proximity detection for the purposes of driving context‐aware applications such as navigation or advertisements in shop‐ ping malls. Finally, there are XBee radios that are designed for low data rate communication in personal area networks. They also operate in the 2.4 GHz band, have low bandwidth and are widely used for home automation and Internet of Things (IoT) applications. Since all of these forms of radios operate in similar frequency bands, they can and will interfere with each other; moreover, they all experience similar behavior with respect to radio wave propagation. With this in mind, there are a variety of range‐based and range‐free localization methods that can be implemented with all of these radios; however, there are some methods that may be better suited to one radio than another.

## **3.2. Range‐based localization**

characteristics including their network topology, computational strategy, use of anchors and

The network topology is closely associated with the method of computation and has a major impact on the localization methods available because it dictates how information is going to be passed and to whom it will be passed. For example, in a star topology, the end nodes will not be able to communicate directly (or at all) with neighboring end nodes, which means that timing‐based approaches may be difficult to implement and computation will need to be carried out at the master node. However, if a mesh topology is available, then there are few restrictions (imposed by the network) on the method of localization and it opens the door to

The availability of anchors can be a hardware constraint if a global reference frame is in use and GPS is unavailable. Anchors can also take the form of a node with a known location in an arbitrary (but consistent) frame of reference, so lacking even these data are also a possibility. The availability of an anchor could be determined by the nodes' mobility and location; for example, a node moving about within a building may become an anchor when it is in a loca‐ tion where it can receive a GPS signal. Anchor‐based localization algorithms are those that *require* anchors to function, whereas anchor‐free algorithms could operate in their absence. Range‐free localization algorithms that rely on finding the centroid of the shape formed by a set of nodes can only function if the locations of the nodes are known. In contrast, the range‐

The various forms of mobility (or lack thereof) that a localization algorithm can handle are a characteristic that may be imposed by the hardware on the nodes or it may simply be an assump‐ tion made by the designers. For example, the computational burden than can be handled by sen‐ sors in a WSN may be severely limited, so the design of an algorithm that can handle precise tracking on such hardware would be infeasible. Similarly, the algorithm designers may assume that the targets to be localized are generally static or that their movements are fairly predictable. In addition to these characteristics, considerations must be made regarding the impact of the localization algorithm on the normal network communication and the environment in which a localization system is to be deployed. The former consideration is one that is more impor‐ tant with regard to optimization, so it will not be discussed here. The latter is a point that will be considered in the next section as we discuss radio frequency (RF)‐based localization with a

The radio frequency devices under consideration in this chapter are Wi‐Fi, Bluetooth/BLE and XBee devices due to their wide commercial availability and frequent use in ad‐hoc networks. Wi‐Fi is a well‐known and ubiquitous radio technology based on the IEEE 802.11 standard. It operates in the 2.4 and 5 GHz bands with three nonoverlapping channels in the former and two dozen in the latter. Bluetooth falls under the IEEE 802.15.1 standard, operates in the 2.4 GHz band and is designed for wireless communication over short ranges. Instead of focusing on

ability to handle mobility, whether it be the anchors or the targets that are mobile.

distributed computation and cooperative localization.

120 Ad Hoc Networks

based localization (e.g., ToF) can localize targets without anchors.

focus on its implementation indoors and the challenges arising from that.

**3. RF‐based indoor localization**

**3.1. Radio frequency devices**

As introduced above, range‐based localization methods are used for triangulation, trilatera‐ tion, or min‐max. The go‐to method of ranging, due to its simplicity, is RSS ranging, which relies on the notion that the strength of a radio signal decays in a reliable and easily calculable way. One common approach is to assume that the antenna is isotropic and then make use of a path loss model to solve for the distance given the power of the signal at the source and at some unknown point. Other approaches include developing different path loss models as in Refs. [15, 16] and improving the estimated distances or locations through the use of maxi‐ mum‐likelihood estimation [17] or a Kalman filter [18]. Almost all of these methods of RSS ranging involve some sort of calibration (solving for environmental characterization values, calculating a path loss model, etc.) to be effective.

Another common form of ranging is to calculate the ToF of the radio signal from the sender to the receiver. Since the speed of the radio signal is the speed of light, the distance can be calculated readily. The difficulty lies in how to precisely measure ToF because the processing delay between the arrival of the signal at the radio and when these data are read at the appli‐ cation layer is significant and can greatly overshadow the actual signal propagation time. Rather than perform one‐way ranging (OWR), where both nodes must have clocks that are synchronized, another method is to perform two‐way ranging (TWR), where a message in not only sent from one node to the other, but the other node also responds with an acknowl‐ edgment. TWR is helpful because it helps to account for the processing times on either end and also does not require that the two have synchronized clocks. One can go even further and perform symmetric double‐sided TWR (SDS‐TWR) in which the TWR procedure is run through twice starting once at each node. This method can help with issues such as clock drift [19], which are not addressed by TWR. A final method of ToF ranging (described in Ref. [14]) leverages the ability to analyze channel characteristics combined with communication across many channels to determine ToF. The key to this method is to change finding the ToF into an application of the Chinese Remainder Theorem by analyzing the phase of the arriving signal across many channels of communication. One drawback of this last method is that it requires both the hardware and software to support such an analysis of the arriving signal, which may not be the case for a lot of consumer radio products.

The final method of radio frequency ranging is ranging in the general sense of measuring something between a reference node and a target node. Determining the angle of arrival (AoA) is a ranging method in which the angle between a reference node and an unknown node in the former's frame of reference is determined. There are two ways that this has been accomplished in the literature. The first method is to use an antenna array so that the time difference of arrival at each of the antennas in the array of the signal can be used to calculate the AoA of the signal. The second approach is referred to as synthetic aperture radar (SAR) and entails moving an antenna at a predetermined speed in a predetermined direction (for example, rotation about an axis). Using the time difference of arrival at the antenna at dif‐ ferent points in its trajectory, the direction of the signal can be determined. With additional sensors that can account for the movement of the node, SAR can be accomplished across an unknown trajectory as well as mentioned in Ref. [20].

## **3.3. Range‐free localization**

The accuracy of range‐free localization methods is typically less than that of the range‐based methods; however, these methods have the advantage of simplified implementation and hardware requirements. Additionally, it is not always necessary to calculate an exact location, so range‐free localization displays varying levels of accuracy and complexity, so that the right tool can be chosen for the job.

One of the most accurate forms of range‐free localization is called fingerprinting and relies on RSS measurements to develop a radio map of a location. Rather than attempt to draw a rela‐ tionship between the RSS measurements and the distance between the unknown and known nodes, the RSS measurements at many known locations are stored in a database during a con‐ figuration phase. Later, a new node that enters the mapped area can get its location by having its signal strength readings compared against the database.

There are many methods that revolve around the use of network connectivity information in addition to the known location of a set of nodes. With this information, the centroid of the triangle formed by three nodes with known locations is used as the location estimate of a node to which all three are connected. There are a variety of implementations of the centroid method that differ, largely, by the geometry they wish to exploit. Another method of localiza‐ tion, which can be used in a multihop network, is DV‐Hop, or distance vector hop [21], range‐ free localization. This method piggybacks on the distance vector routing algorithm with the knowledge of some of the node locations to estimate the length of a hop. The hop length esti‐ mate is then used to estimate the distances from the nodes with known locations to the target nodes. The final method of range‐free localization addressed here is a simple proximity‐based localization system in which connectivity to a reference node is assumed to mean that the target is located in the same place. If the signal range of the reference node can be controlled, then this method can be useful for room‐level localization.

## **4. Challenges of RF‐based indoor localization**

## **4.1. Evaluation parameters**

In order to study the challenges of the localization methods and evaluate their performance, we first need to present some metrics and concepts, which define their constraints and limitations.

(i) Localization accuracy: The localization accuracy is one of the most crucial parameters depending on the application of localization. For example, in safety applications in vehicular environments for pedestrian protection from accidents, the highest localization accuracy is desired (submeter accuracy). For less sensitive applications, lower accuracy coarse ranging (i.e., immediate, near and far region) is acceptable and for GPS‐based localization, an accu‐ racy of a few meters can be sufficient. The localization strategy, network structure, number of beacons and the devices' capabilities and technologies are other parameters that can impact the localization accuracy.

(ii) Localization reliability: Another important aspect of localization algorithms is the reli‐ ability of the methods, i.e., how consistent a localization method can be in different situations. Environment, the mobility of devices and objects in the network and type of technology are a few aspects that can impact the reliability of the localization method. Specifically, in RF‐based localization based on analysis of RSSI to extract proximity information, the susceptibility of RSSI to multipath and shadowing caused by environmental changes is the main challenge to reliability (and accuracy). We will explore this further in the next section.

(iii) Power requirements: Networks (especially sensor networks) can have strict power require‐ ments, which can impact localization performance. Two clear ways that power requirements impact localization are in the computational complexity of the localization algorithm chosen and the communication technology utilized. In the latter case, strict power requirements may push one to consider using BLE or XBee rather than Wi‐Fi.

(iv) Availability/cost: For practical implementations of localization algorithms, cost and avail‐ ability of the technologies and devices are the two essential factors. For example, BLE is one of the most popular and available technologies in smart devices and can easily be used for many localization purposes.

## **4.2. Challenges**

The final method of radio frequency ranging is ranging in the general sense of measuring something between a reference node and a target node. Determining the angle of arrival (AoA) is a ranging method in which the angle between a reference node and an unknown node in the former's frame of reference is determined. There are two ways that this has been accomplished in the literature. The first method is to use an antenna array so that the time difference of arrival at each of the antennas in the array of the signal can be used to calculate the AoA of the signal. The second approach is referred to as synthetic aperture radar (SAR) and entails moving an antenna at a predetermined speed in a predetermined direction (for example, rotation about an axis). Using the time difference of arrival at the antenna at dif‐ ferent points in its trajectory, the direction of the signal can be determined. With additional sensors that can account for the movement of the node, SAR can be accomplished across an

The accuracy of range‐free localization methods is typically less than that of the range‐based methods; however, these methods have the advantage of simplified implementation and hardware requirements. Additionally, it is not always necessary to calculate an exact location, so range‐free localization displays varying levels of accuracy and complexity, so that the right

One of the most accurate forms of range‐free localization is called fingerprinting and relies on RSS measurements to develop a radio map of a location. Rather than attempt to draw a rela‐ tionship between the RSS measurements and the distance between the unknown and known nodes, the RSS measurements at many known locations are stored in a database during a con‐ figuration phase. Later, a new node that enters the mapped area can get its location by having

There are many methods that revolve around the use of network connectivity information in addition to the known location of a set of nodes. With this information, the centroid of the triangle formed by three nodes with known locations is used as the location estimate of a node to which all three are connected. There are a variety of implementations of the centroid method that differ, largely, by the geometry they wish to exploit. Another method of localiza‐ tion, which can be used in a multihop network, is DV‐Hop, or distance vector hop [21], range‐ free localization. This method piggybacks on the distance vector routing algorithm with the knowledge of some of the node locations to estimate the length of a hop. The hop length esti‐ mate is then used to estimate the distances from the nodes with known locations to the target nodes. The final method of range‐free localization addressed here is a simple proximity‐based localization system in which connectivity to a reference node is assumed to mean that the target is located in the same place. If the signal range of the reference node can be controlled,

In order to study the challenges of the localization methods and evaluate their performance, we first need to present some metrics and concepts, which define their constraints and limitations.

unknown trajectory as well as mentioned in Ref. [20].

its signal strength readings compared against the database.

then this method can be useful for room‐level localization.

**4. Challenges of RF‐based indoor localization**

**4.1. Evaluation parameters**

**3.3. Range‐free localization**

122 Ad Hoc Networks

tool can be chosen for the job.

One of the main shortcomings of RSSI‐based localization (such as Bluetooth‐based local‐ ization) in indoor environments is that RSSI measurements only provide a rough estimate of the distance between a transmitter and a receiver. In realistic environments, increasing the distance between the transmitter and the receiver does not necessarily decrease the sig‐ nal strength (especially indoors where signals are often reflected multiple times). Another important point to consider is that even for a constant distance between the devices, the RSSI values can fluctuate very erratically over time. Based on prior work [22] and our own experiments, we can summarize some characteristics of using RSSI for ranging. The follow‐ ing experiments were carried out in a hallway that measures 2.41 m wide × 2.34 m high. The walls are concrete and there are multiple metal doors along them (which help to ensure the presence of multipath). In all cases, a node (either a receiver or transmitter) was affixed to the bottom of an EXIT so that the node was 2.13 m off of the ground while the other node was either carried or set on the ground for each measurement. For BLE experiments, we used Estimote beacons, and, in order to collect the BLE data (RSSI), we developed an application for smart phones using the Estimote SDK. Wi‐Fi data were collected using Raspberry Pi 2B (RPi) where the RPis are used for both transmitter and receiver. We wrote some Python code to configure the RPi as a transmitter and set the transmission parameters such as transmis‐ sion channel, transmission rate, etc. On the receiver side, a Python script was written to scan the channels and record the RSSI. Finally, XBee data were collected through the use of Digi XBee S2 radio modules with a 2 mW wire antenna and software running on a laptop that uses a remote AT command to get the RSSI of the last received packet. A single RSSI measurement as reported below is actually the median of five such RSSI request values, which provides a rough filtering of outliers. Care was taken to ensure that there were no obstacles between transmitter and receiver for all data collected. That is, there was always a line of sight path between the two nodes.

In stationary cases (i.e., no device mobility and the distance between transmitter and receiver is fixed), RSSI values can fluctuate significantly between adjacent beacons, as shown in **Figures 3**–**5**. The BLE fluctuation data in **Figure 3** were collected at a distance of 8 m and transmit power of 4 dBm. In **Figure 3**, the RSSI measurements vary by as much as 29 dB over a 45 s period and in **Figure 4**, maximum fluctuations of 10 dB can be observed for XBee radios. The XBee fluctuation data were collected at the same distance and transmit power as the BLE experiment. Finally, **Figure 5** presents the RSSI measurements from Wi‐Fi at a distance of 8 m with transmission power of 4 dBm. As can be observed, Wi‐Fi shows more stable RSSI behav‐ ior with less fluctuation (e.g., less than 10 dBm).

**Figure 3.** BLE RSSI fluctuations in an indoor environment.

These fluctuations are to be expected because all the walls, doors and floor serve to reflect the signal, which results in many copies of the same packet arriving at the transmitter with varying signal strengths. XBee would appear to be a good choice for communication in the presence of multipath effects due to its lower RSSI fluctuations, but the importance of this metric needs to be balanced with the ability to accurately and reliably relate RSSI to distance. The RSSI value is expected to decrease with increasing distances, but in practice, this relation‐ ship is not reliable. As shown in **Figure 6**, the average RSSI does not necessarily decrease as distance increases for BLE, Wi‐Fi and XBee (now transmitting at 5 dBm). Note that for BLE, the data were collected while walking away from the EXIT‐sign‐affixed node at a rate of 0.5 m/s. In particular, for BLE, the average RSSI at a distance of 10 m is greater than the RSSI at a distance of 6 m. A similar behavior can be observed for Wi‐Fi signals in **Figure 6**. We note that

**Figure 4.** XBee RSSI fluctuations in an indoor environment.

XBee S2 radio modules with a 2 mW wire antenna and software running on a laptop that uses a remote AT command to get the RSSI of the last received packet. A single RSSI measurement as reported below is actually the median of five such RSSI request values, which provides a rough filtering of outliers. Care was taken to ensure that there were no obstacles between transmitter and receiver for all data collected. That is, there was always a line of sight path

In stationary cases (i.e., no device mobility and the distance between transmitter and receiver is fixed), RSSI values can fluctuate significantly between adjacent beacons, as shown in **Figures 3**–**5**. The BLE fluctuation data in **Figure 3** were collected at a distance of 8 m and transmit power of 4 dBm. In **Figure 3**, the RSSI measurements vary by as much as 29 dB over a 45 s period and in **Figure 4**, maximum fluctuations of 10 dB can be observed for XBee radios. The XBee fluctuation data were collected at the same distance and transmit power as the BLE experiment. Finally, **Figure 5** presents the RSSI measurements from Wi‐Fi at a distance of 8 m with transmission power of 4 dBm. As can be observed, Wi‐Fi shows more stable RSSI behav‐

These fluctuations are to be expected because all the walls, doors and floor serve to reflect the signal, which results in many copies of the same packet arriving at the transmitter with

between the two nodes.

124 Ad Hoc Networks

ior with less fluctuation (e.g., less than 10 dBm).

**Figure 3.** BLE RSSI fluctuations in an indoor environment.

if the RSSI is not averaged then fluctuations such as those illustrated in **Figures 3** and **4** can have a much greater impact on the RSSI‐distance trend.

The measured RSSI values typically differ from the expected relationship between signal strength and distance. We performed experiments to show this observation for BLE, Wi‐ Fi and XBee. In **Figure 7**, we compare the measured RSSI with the mathematical relation between RSSI and distance for BLE presented in Eq. (1) as a function of transmission power, distance (*d*) and path exponent (*n*):

$$\text{RSSI} = -\{10n\text{log}(d) - A\}\tag{1}$$

**Figure 5.** Wi‐Fi RSSI fluctuations in an indoor environment.

**Figure 6.** RSSI versus distance for BLE, Wi‐Fi and XBee.

In Eq. (1), *A* is the measured power, which is the expected RSSI value at a distance of 1 m to the BLE beacon. The measured RSSI is a function of transmission power. We performed the same experiment with Wi‐Fi to investigate the relation between Wi‐Fi RSSI and distance. **Figure 8** shows the comparison of the average of RSSI at different distances with the analytical equation presented in Eq. (2). As can be seen in this equation, the relation between RSSI and distance is a function of frequency *f* and RSSI.

 $\text{distance is a function of frequency } f \text{ and RSSI.}$ 
$$\text{Distance} = 10 \left( \frac{27.5 - (20 \log(l) + \text{RSS})}{20} \right) \tag{2}$$

**Figure 9** shows the relation between RSSI and distance for XBee in the same environment using the same equation as BLE (using different parameters). This figure also indirectly illustrates the sensitivity of radio frequency communication to the environment because here the transmitter was held 1 m off the ground rather than sitting on the ground as shown in **Figure 6** and the rela‐ tion is much closer to a log relationship between distance and RSSI than the data in **Figure 6**.

The effect of an indoor environment on signal propagation is the primary reason for these data not matching with their respective theoretical model. Any particular packet (or set of packets) may be influenced by multipath despite averaging of results. Additionally, the trans‐ mit power, receiver sensitivity and antenna orientation all play a role in influencing RSSI and cause it to no longer follow the theoretical relationship. It is also important to note that the interference from other devices in 2.4 GHz band could have had a hand in driving the mea‐ sured RSSI away from the expected values.

**Figure 7.** RSSI versus distance for BLE (analytical model and measurements).

**Figure 6.** RSSI versus distance for BLE, Wi‐Fi and XBee.

**Figure 5.** Wi‐Fi RSSI fluctuations in an indoor environment.

126 Ad Hoc Networks

**Figure 8.** Average RSSI versus distance for Wi‐Fi‐2.4 GHz (analytical model and measurements).

**Figure 9.** Average RSSI versus distance for XBee radio (analytical model and measurements).

The differences in ranging accuracy are difficult to compare between the different types of radio frequency devices because RSSI is not, by any means, a strictly defined value. Beyond the fact that RSSI is an 8‐bit integer representing the strength of the signal, there is little specifi‐ cation as to its implementation. This means that RSSI implementations can differ between ven‐ dors of different radio frequency devices and even the same type of radio frequency device.

Finally, as mentioned earlier, the RSSI value fluctuates even for fixed distances between trans‐ mitter and receiver. However, these fluctuations vary based on distance, i.e., the larger the distance between transmitter and receiver, the larger the variations observed in the measured RSSI values, as shown in **Table 1**. As shown in **Table 1**, the errors become significantly larger when the receiver is further from a transmitter for BLE beaconing.


**Table 1.** Average RSSI and standard deviation for BLE.

**Figure 8.** Average RSSI versus distance for Wi‐Fi‐2.4 GHz (analytical model and measurements).

128 Ad Hoc Networks

**Figure 9.** Average RSSI versus distance for XBee radio (analytical model and measurements).

## **5. Possible approaches to address the RF‐based localization challenges in indoor environments**

In the previous section, we reviewed the main challenges of RF‐based localization. The main source of these challenges is the structure of indoor environments, which causes multipath effects, heavy shadowing, noise interference and nonline of sight (NLOS) conditions. Additionally, indoor environments must contend with the mobility of obstacles, which has a far greater effect on localization than in an outdoor environment. In the following section, we review various efforts to address these challenges.

## **5.1. Leveraging channel state information**

In recent years, Wi‐Fi chipmakers have made channel state information (CSI) per subcarrier (and per antenna) available on their chips. Using a CSI extraction tool, the authors [14, 20, 23] illustrate the use of the channel subcarrier information to achieve decimeter level localization accuracy. In particular, Kotaru et al. [23] use multiple antennas and CSI to calculate the angle of arrival (AoA) and uses a rough estimate of the ToF to provide resilience against multipath effects. Localization is accomplished using a combination of RSSI and AoA for ranging at each anchor. Vasisht et al. [14], as mentioned earlier, use the CSI to calculate the ToF by taking mea‐ surements at many different frequencies. Finally, in [20], the authors present Ubicarse, which combines CSI with the gyroscopes in a tablet to realize a synthetic aperture radar (SAR) to carry out localization. The ability to access and analyze this rich source of radio information is incredibly helpful in improving RF‐based ranging in an indoor environment and could easily see deployment in an ad‐hoc network as long as the hardware and software required to utilize CSI fit within the constraints of the network.

## **5.2. Calibration**

Another technique for improving the reliability of RSSI is using calibration or add‐ ing an adjustment factor based on the network parameters such as network dimension, transmission power and number of beacons. The main idea behind the calibration tech‐ nique is to use the relationship between RSSI and the actual distance between several nodes (usually the anchor nodes with known positions) and utilize it as an offset value to adjust the RSSI for distance estimation. In Ref. [24], the authors attempt to calibrate the RSSI‐dis‐ tance model by using least squares to adjust the reference power at 1 m as well as the path loss exponent. By adaptively calibrating the system, it achieves a lower error and better reliability than methods that only calibrate manually during setup or after a major change. Calibration in this way keeps the range estimation from deteriorating when the environ‐ ment changes.

As an example of calibration, we implemented a network with three Bluetooth beacons at known locations. We measured the RSSI values at different distances and compare it with the analytical equation as presented in **Figure 7**. To calibrate the ranging measurements, the RSSI was measured between two different pairs of beacons such that the distance separating the pairs was sufficiently dissimilar. Between these new RSSI‐distance points, we interpolate a line, which is used to adjust the RSSI measurements. **Figure 10** shows the RSSI‐distance graph after using the calibration.

Also, **Figure 11** shows the comparison of the distance error with and without calibration, showing that the calibration can improve the distance estimation error. However, there are still some large peaks in the error graph which illustrate that even with the use of calibration, RSSI is not a completely reliable parameter for distance estimation.

## **5.3. Adaptive beaconing**

Adaptive beaconing is another approach that can be useful for different situations in the net‐ work by changing the transmission rate and/or transmission power of the beacons.

Adaptive transmission rate depends on the application of localization. For some applications, such as mobile device tracking and navigation systems, higher transmission rates are needed to keep track of a mobile device. A serious challenge for such systems with high transmis‐ sion rate is the high packet collision and delay that can affect the localization performance significantly.

**Figure 10.** The measured RSSI after calibration.

effects. Localization is accomplished using a combination of RSSI and AoA for ranging at each anchor. Vasisht et al. [14], as mentioned earlier, use the CSI to calculate the ToF by taking mea‐ surements at many different frequencies. Finally, in [20], the authors present Ubicarse, which combines CSI with the gyroscopes in a tablet to realize a synthetic aperture radar (SAR) to carry out localization. The ability to access and analyze this rich source of radio information is incredibly helpful in improving RF‐based ranging in an indoor environment and could easily see deployment in an ad‐hoc network as long as the hardware and software required to utilize

Another technique for improving the reliability of RSSI is using calibration or add‐ ing an adjustment factor based on the network parameters such as network dimension, transmission power and number of beacons. The main idea behind the calibration tech‐ nique is to use the relationship between RSSI and the actual distance between several nodes (usually the anchor nodes with known positions) and utilize it as an offset value to adjust the RSSI for distance estimation. In Ref. [24], the authors attempt to calibrate the RSSI‐dis‐ tance model by using least squares to adjust the reference power at 1 m as well as the path loss exponent. By adaptively calibrating the system, it achieves a lower error and better reliability than methods that only calibrate manually during setup or after a major change. Calibration in this way keeps the range estimation from deteriorating when the environ‐

As an example of calibration, we implemented a network with three Bluetooth beacons at known locations. We measured the RSSI values at different distances and compare it with the analytical equation as presented in **Figure 7**. To calibrate the ranging measurements, the RSSI was measured between two different pairs of beacons such that the distance separating the pairs was sufficiently dissimilar. Between these new RSSI‐distance points, we interpolate a line, which is used to adjust the RSSI measurements. **Figure 10** shows the RSSI‐distance graph

Also, **Figure 11** shows the comparison of the distance error with and without calibration, showing that the calibration can improve the distance estimation error. However, there are still some large peaks in the error graph which illustrate that even with the use of calibration,

Adaptive beaconing is another approach that can be useful for different situations in the net‐

Adaptive transmission rate depends on the application of localization. For some applications, such as mobile device tracking and navigation systems, higher transmission rates are needed to keep track of a mobile device. A serious challenge for such systems with high transmis‐ sion rate is the high packet collision and delay that can affect the localization performance

work by changing the transmission rate and/or transmission power of the beacons.

RSSI is not a completely reliable parameter for distance estimation.

CSI fit within the constraints of the network.

**5.2. Calibration**

130 Ad Hoc Networks

ment changes.

after using the calibration.

**5.3. Adaptive beaconing**

significantly.

**Figure 11.** Comparison between distance errors when using the calibration function.

Adaptive transmission power, or multirange beaconing, is an approach that can be utilized to improve the distance estimation for localization purposes. In Ref. [25], the effect of transmis‐ sion power and number of anchors on accuracy of localization and connectivity of nodes is investigated. In order to evaluate this approach, we ran some experiments by choosing five transmission power levels (–30, –20, –12, –4 and 4 dBm) and measuring the distance error for each transmission power. The average distance error over all transmission powers was also recorded. The measurements were conducted in separate scenarios to avoid any interference. **Figure 12** shows the comparison of the distance error estimation for our measurements. As indicated that with increasing distance, the error increases for each particular transmission power. More importantly, the distance estimation error changes for different transmission powers. Low transmission power can provide very high accuracy distance estimations over short distances from the beacon while high transmission powers are better for distance esti‐ mation of greater distances. This illustrates that adapting the transmission power to the dis‐ tance can provide higher accuracy distance estimation than using a single transmission power alone (or averaging the results of all the transmission powers).

**Figure 12.** Effect of transmission power on distance estimation error.

## **5.4. Combination of RF signaling**

Another approach for improving the distance estimation and reliability of RSSI is combining different RF‐based communication technologies. Recently, Bluetooth‐based localization has attracted a lot of attention because of its availability and cost, but many of the efforts rely on unreliable RSSI for proximity extraction in indoor environments. To address this problem, several prior efforts combined Bluetooth beaconing with other technologies such as Wi‐Fi [26], Zigbee [27] and RFID [28] to further improve the localization accuracy. Although combining these technologies can improve the localization accuracy, it introduces other challenges such as availability, complexity, or implementation problems that make them less practical solutions.

## **5.5. Fusing additional sensor data**

Adaptive transmission power, or multirange beaconing, is an approach that can be utilized to improve the distance estimation for localization purposes. In Ref. [25], the effect of transmis‐ sion power and number of anchors on accuracy of localization and connectivity of nodes is investigated. In order to evaluate this approach, we ran some experiments by choosing five transmission power levels (–30, –20, –12, –4 and 4 dBm) and measuring the distance error for each transmission power. The average distance error over all transmission powers was also recorded. The measurements were conducted in separate scenarios to avoid any interference. **Figure 12** shows the comparison of the distance error estimation for our measurements. As indicated that with increasing distance, the error increases for each particular transmission power. More importantly, the distance estimation error changes for different transmission powers. Low transmission power can provide very high accuracy distance estimations over short distances from the beacon while high transmission powers are better for distance esti‐ mation of greater distances. This illustrates that adapting the transmission power to the dis‐ tance can provide higher accuracy distance estimation than using a single transmission power

Another approach for improving the distance estimation and reliability of RSSI is combining different RF‐based communication technologies. Recently, Bluetooth‐based localization has attracted a lot of attention because of its availability and cost, but many of the efforts rely on unreliable RSSI for proximity extraction in indoor environments. To address this problem, several prior efforts combined Bluetooth beaconing with other technologies such as Wi‐Fi [26], Zigbee [27] and RFID [28] to further improve the localization accuracy. Although combining these technologies can improve the localization accuracy, it introduces other challenges such as availability, complexity, or implementation problems that make them less practical solutions.

alone (or averaging the results of all the transmission powers).

**5.4. Combination of RF signaling**

132 Ad Hoc Networks

**Figure 12.** Effect of transmission power on distance estimation error.

With the availability of multiple sensors on nodes in some ad‐hoc networks, one approach to improve localization estimates is to somehow fuse data from these other sources to, hope‐ fully, cover up some of the shortcomings of RF ranging. For example, ToA and RSSI ranging using anchors can be combined with dead reckoning to improve localization as demonstrated in Refs. [29, 30]. In the former, the authors used two‐way time of arrival (TOA) for localization of vehicles in a vehicular network. They assume the existence of a road side unit (RSU) where the vehicles use two‐way communication with the RSU and a partial dead reckoning method to determine the position of vehicles in GPS‐denied areas. In the latter work, pedestrian dead reckoning location estimates are combined with RSSI localization estimates through the use of an extended Kalman filter (EKF) with some success.

## **5.6. Cooperative localization**

In some network layouts, it is not always possible for every node to communicate with the anchors nodes (if there are any). In such a case, nodes must work together through cooperative localization to figure out where they are all located. Cooperative localization is similar to relative localization but also includes the possibility of some anchors nodes somewhere in the network as well as the potential for fusing additional data from onboard sensors (available in a vehicle or a smartphone) to help localize a node. In [31], pedestrian dead reckoning (PDR) with a smartphone is combined with Wi‐Fi RSSI ranging where RSSI is used to determine when two smartphones are near to each other. In this way, the RSSI ranging can help correct heading inaccuracies from PDR. The path that a person takes is abstracted as a series of lines and joints where the RSSI ranging allows for the correction of the joint angles. In Ref. [32], relative localization through ranging is combined with the network topology and graph theory concepts to develop distributed cooperative localiza‐ tion algorithms that can greatly reduced computational complexity and provide resilience to noisy internode measurements. Cooperative localization has great potential in robotics, MANETs and VANETs.

## **6. Discussion and conclusion**

In general, localization in any network can be improved through the exploitation of addi‐ tional information whether it is from multiple sensors, extra data from the radio, or additional location estimates from neighbors. The goal is to leverage data that is already available so as to not increase the cost or resource requirements of nodes in an ad‐hoc network. However, the solutions presented to the above indoor radio frequency localization issues are still not the end of the road for indoor localization or localization in GPS‐denied locations. No single solu‐ tion is going to work everywhere because of constraints on the network, e.g., the availability of particular hardware to carry out localization. Additionally, some networks may not have the luxury of dedicated beacons or anchors, or they may be located in a highly dynamic envi‐ ronment such that RSSI ranging becomes even more troublesome than usual. Finally, on top of the issues of accuracy and cost, there is the issue of the impact of the localization scheme on the performance of the network. The use of passive beacons or active ranging messages could, in the best case, mildly interfere with communication or, at worst, impose a severe restriction on the communication capabilities of the network. The difficulty and importance of indoor localization will ensure that creative and innovative solutions will continue to be sought by those hoping to develop the indoor equivalent of GPS. Whether a single method will satisfy the requirements and constraints of the many disparate networks in use today remains to be seen.

## **Author details**

Mehdi Golestanian, Joshua Siva\* and Christian Poellabauer

\*Address all correspondence to: joshua.t.siva.1@nd.edu

University of Notre Dame, Notre Dame, Indiana, USA

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## *Edited by Jesus Hamilton Ortiz and Alvaro Pachon de la Cruz*

A mobile ad hoc network (MANET) is a collection of two or more wireless devices with the capability to communicate with each other without the aid of any centralized administrator. Ad hoc networks have no fixed routers, these nodes can be connected dynamically in an arbitrary manner. MANETs, due to their operational characteristics, the dynamics of their changes and the precariousness of their resources, offer huge challenges due to the architecture and service nature in the next generation of mobile communications. MANETs play an important role in the future of next-generation networks. This special collection identifies and studies the most important concerns in MANETs, and includes contributions from researchers, academics, etc.

Photo by StationaryTraveller / iStock

Ad Hoc Networks

Ad Hoc Networks

*Edited by Jesus Hamilton Ortiz* 

*and Alvaro Pachon de la Cruz*