**2. Types of deployment environments**

Wireless Sensor Networks may be classified according to the deployment environment. **Figure 1** shows the classification of WSN according to the deployment environment. WSNs may be classified according to the type and environment of the data being acquired. The sensor nodes may either be deployed terrestrially, that is either aboveground or underground. They may be underwater or multimedia (when deployed to capture videos, images, and audios) or simply numeric data.

WSNs have also been classified according to their mobility, mobile (when the sensor nodes move in their deployment environment) and stationary [6]. In both underground and underwater wireless sensor networks (**Figure 1**), the sensor nodes are buried either in the soil or placed underwater to measure the condition of their respective environments. The buried sensor nodes communicate with a sink above ground and send data through it to a monitoring station [7].

#### **2.1 Wireless underground sensor networks (WUSN)**

Wireless Underground Sensor Networks (WUSNs) as shown in **Figure 2** is a well-studied area [4]. They are used in different applications which include intelligent agriculture, power grid maintenance, pipeline fault diagnosis, etc. [8, 9].

Compared with traditional terrestrial Wireless Sensor Networks, WUSNs suffer special communication challenges characterized by the weak signal propagation in soil, rocks and other underground materials. Underground signal propagation is challenged with strong attenuation and signal losses [7]. Traditionally, WUSNs use electromagnetic (EM) waves to establish connection among transceivers underground. However, EM waves have several shortcomings; antenna sizes, short communication range, and the channel conditions are highly unreliable. There are new techniques such as magnetic induction (MI) that have the potential to overcome the challenges posed by the use of EM waves in WUSNs. Underground sensors are equipped with batteries which are difficult to charge or replace when the sensor nodes energy are depleted. Conserving underground sensor nodes' energy is crucial to extending the lifetime of the network and to achieving optimal performance.

**241**

*Applications of Prediction Approaches in Wireless Sensor Networks*

*DOI: http://dx.doi.org/10.5772/intechopen.94500*

**2.2 Wireless underwater sensor networks**

and underwater telemetry.

**Figure 2.**

*Underground deployment.*

Wireless Underwater Sensor Networks (**Figure 2**) is an area that has caught the attention of researchers in recent times [10, 11]. Underwater Sensor Networks are useful due to their several implementation areas which include marine pollution monitoring, marine data gathering, tsunami detection, threat detection at seaports,

The monitoring is usually performed using navigation assistance such as autonomous underwater vehicles (AUV) and vehicle surveillance. Wireless Underwater Sensor Networks (e.g., marine monitoring) suffer from limited bandwidth, node failures due to harsh environmental conditions, signal fading, and propagation delay [12]. Underwater sensor nodes normally communicate using acoustic waves to a surface buoy or sink above the water. It is also possible to employ non-acoustic communication techniques such as radio frequency (RF), magnetic induction (MI), and

The dynamic nature of the water environment is related to the content salt, and its turbidity. Hence, the communication channel also becomes dynamic. RF signals, when exposed to these environmental characteristics, suffer high attenuation.

Magnetic Induction may also be used for underwater propagation but requires the use of large-sized antennas which is somewhat impractical in such environments. Acoustic communication is the preferred method for underwater communication since acoustic waves suffer less attenuation and are able to travel long distances due to their low frequencies. Nodes that are connected using acoustic waves constitute underwater acoustic sensor networks (UASNs). UASN nodes are energy hungry nodes which consume a great deal of power compared to sensor nodes deployed to monitor environmental conditions on land. There are several techniques discussed in the literature to overcome the energy problem in UASN and to minimize the energy consumed by the UASN to improve on the network lifetime. Energy efficient routing protocols and clustering protocols are two such techniques adopted to minimize the energy consumed by sensor nodes deployed in WUSNs and UASNs [13]. Current existing routing protocols are group into receiver-based and sender-based which are further categorized based

underwater free-space optics in underwater sensor networks [13].

on energy, geographic information, and hybrid routing protocols [10].

**Figure 1.** *WSN deployment types.*

**Figure 2.** *Underground deployment.*

*Wireless Sensor Networks - Design, Deployment and Applications*

Wireless Sensor Networks may be classified according to the deployment environment. **Figure 1** shows the classification of WSN according to the deployment environment. WSNs may be classified according to the type and environment of the data being acquired. The sensor nodes may either be deployed terrestrially, that is either aboveground or underground. They may be underwater or multimedia (when deployed to capture videos, images, and audios) or simply numeric data. WSNs have also been classified according to their mobility, mobile (when the sensor nodes move in their deployment environment) and stationary [6]. In both underground and underwater wireless sensor networks (**Figure 1**), the sensor nodes are buried either in the soil or placed underwater to measure the condition of their respective environments. The buried sensor nodes communicate with a sink

Wireless Underground Sensor Networks (WUSNs) as shown in **Figure 2** is a well-studied area [4]. They are used in different applications which include intelligent agriculture, power grid maintenance, pipeline fault diagnosis, etc. [8, 9].

Compared with traditional terrestrial Wireless Sensor Networks, WUSNs suffer special communication challenges characterized by the weak signal propagation in soil, rocks and other underground materials. Underground signal propagation is challenged with strong attenuation and signal losses [7]. Traditionally, WUSNs use electromagnetic (EM) waves to establish connection among transceivers underground. However, EM waves have several shortcomings; antenna sizes, short communication range, and the channel conditions are highly unreliable. There are new techniques such as magnetic induction (MI) that have the potential to overcome the challenges posed by the use of EM waves in WUSNs. Underground sensors are equipped with batteries which are difficult to charge or replace when the sensor nodes energy are depleted. Conserving underground sensor nodes' energy is crucial to extending the lifetime of the network and to achieving optimal

above ground and send data through it to a monitoring station [7].

**2.1 Wireless underground sensor networks (WUSN)**

**2. Types of deployment environments**

**240**

**Figure 1.**

*WSN deployment types.*

performance.

#### **2.2 Wireless underwater sensor networks**

Wireless Underwater Sensor Networks (**Figure 2**) is an area that has caught the attention of researchers in recent times [10, 11]. Underwater Sensor Networks are useful due to their several implementation areas which include marine pollution monitoring, marine data gathering, tsunami detection, threat detection at seaports, and underwater telemetry.

The monitoring is usually performed using navigation assistance such as autonomous underwater vehicles (AUV) and vehicle surveillance. Wireless Underwater Sensor Networks (e.g., marine monitoring) suffer from limited bandwidth, node failures due to harsh environmental conditions, signal fading, and propagation delay [12].

Underwater sensor nodes normally communicate using acoustic waves to a surface buoy or sink above the water. It is also possible to employ non-acoustic communication techniques such as radio frequency (RF), magnetic induction (MI), and underwater free-space optics in underwater sensor networks [13].

The dynamic nature of the water environment is related to the content salt, and its turbidity. Hence, the communication channel also becomes dynamic. RF signals, when exposed to these environmental characteristics, suffer high attenuation. Magnetic Induction may also be used for underwater propagation but requires the use of large-sized antennas which is somewhat impractical in such environments. Acoustic communication is the preferred method for underwater communication since acoustic waves suffer less attenuation and are able to travel long distances due to their low frequencies. Nodes that are connected using acoustic waves constitute underwater acoustic sensor networks (UASNs). UASN nodes are energy hungry nodes which consume a great deal of power compared to sensor nodes deployed to monitor environmental conditions on land. There are several techniques discussed in the literature to overcome the energy problem in UASN and to minimize the energy consumed by the UASN to improve on the network lifetime. Energy efficient routing protocols and clustering protocols are two such techniques adopted to minimize the energy consumed by sensor nodes deployed in WUSNs and UASNs [13]. Current existing routing protocols are group into receiver-based and sender-based which are further categorized based on energy, geographic information, and hybrid routing protocols [10].

An Energy Optimized Path Unaware Layered Routing Protocol (E-PULRP) that minimizes the energy consumed in a dense 3D-WUSN is described in [14] as a typical example of an energy-based routing protocol. E-PULRP uses on the fly routing to report events to a stationary sink node.

E-PULRP has two phases: layering and communication. Nodes occupy layers in a concentric shell around the sink node in the layer phase. The nodes within one layer have the same number of hop counts to the sink node. The E-PULRP protocol is designed to follow a network model in which the total volume in the area of interest is subdivided into small-sized cubes with a binomial probability distribution for a node occupancy. The protocol assumes that the number of cubes is large. Following Poisson approximation to the binomial distribution, we can calculate the probability of k nodes occupying a volume V as:

$$\Pr\left[\left.\infty = k\right.\right] = \frac{(\left.\nu\rho dv\right)^{k}}{k!} \exp\left.-\left[\nu\rho dv\right.\tag{1}\right.$$

where:

*ρ* = is the volume density of the sensor nodes.

∫ *v* = indicates integral over the volume V.

Physical properties such as temperature and chemical properties affect underwater communication. Another key factor that affects underwater communication is the depth of transceivers. In E-PULRP, for a transmitted energy of *E*T, the received energy *E*R at distance *R*, is modeled as follows:

$$E\_{\mathbb{R}} = \frac{E\_{\mathbb{T}}}{\mathcal{R}^{(\mathcal{B}/10)} \mathbf{1} \mathbf{0}^{(\alpha R \ast \beta)/10}} \tag{2}$$

where:

*B* = takes values 10, 15 or 20 depending on the type of propagation.

*α* = is a range-independent absorption coefficient.

*β* = is a constant independent of range.

*E*T = the transmitted energy.

*E*R = the received energy/power of the control packet.

In this layering phase, the protocol allows communication to occur only when energy levels of layers close to the sink are chosen. Concentric circles are formed around the central sink and the structure ensures packet forwarding towards the central sink. The layers in this phase are formed as follows: 1) Layer 0 initiates a probe of energy; 2) Nodes with energy equal to the detection threshold (*E*D) assign layer 1 to themselves; 3) The nodes in layer 1 communicates with the sink using a single hop and 4) waits for time *k* to transmits a probe with energy to create layer 2 (i.e., made of nodes in layer 1 with energy equal to *E*D.

The detection threshold, (*E*D) and the waiting time *k* are calculated as follows:

γ

$$E\_D = \frac{E\_{pl}}{\alpha\_l^{(R+10)} \mathbf{10}^{(\alpha a\_l \circ \beta)/10}} \tag{3}$$

where

*Epl* = the probing energy

$$l = 
Theta 
approx$$

$$k = 
\frac{
\lambda\_{\text{min}}
\left(E\_R - E\_D
\right)}{
} \tag{4}$$

**243**

*Applications of Prediction Approaches in Wireless Sensor Networks*

*γ* = energy dependent factor which is the ratio of the energy remaining in the

In the communication phase, intermediate relay nodes are selected to send packets to the sink using multiple hop routing path to determine nodes *on the fly*. In this phase, nodes at the lower layers nearer to the source node are first identified as potential forwarding relay nodes. For example, if a source node, *S* in layer *l* sends a control packet, a node, *N* in the network who receives this control packet may declare itself as a potential forwarding node. This self-declared potential node waits at a time period given in Eq. (4) to listen if any other node has not declared itself as a relay node. It does this by comparing its signal strength Received Signal Strength

Once its RSSI value is less than all others, then it forwards the data packet, otherwise it will go into silent mode. A classic example of a cluster-based energy efficient UWSN is SEEC: Sparsity-aware energy efficient clustering protocol for underwater wireless sensor networks. SEEC was proposed by [15] to search sparse regions in the network. The network is divided into subregions of equal sizes. With the use of sparsity search algorithm (SSA) and density search algorithm (DSA), sparse and dense regions in the network. The lifetime of the network is improved through sink mobility in the sparse regions and through clustering in the dense regions. SEEC minimizes the energy consumed in the overall network by balancing the two sparsity search algorithm (SSA) and density search algorithm (DSA). In SEEC, random nodes are deployed underwater and the network formed is divided into 10 regions. The position of each node in the network is dynamic due the dynamic nature of the deployable environment. The 10 regions are created to determine the sparse and dense regions. SEEC employs three sinks (i.e., a static sink at the top of central point of the sensor network field and two mobile sinks positioned at the sparse regions). Each sensor nodes coordinate is first determined in order to know its current region in the network field (i.e., sparse or dense). A simple algorithm that checks the number of nodes in a region is used to determine a sparse or a dense region. If the number of nodes is minimum, then then node is in a sparse region other the node is considered to be in a dense region. When the searching is completed, then the nodes in the dense region are placed into clusters. To conserve energy and increase the lifetime of the network, SEEC is designed to cluster the top four (4) densely populated regions. Nodes in a dense region collaborate to select their cluster head (CH). The CH is the node with low depth and high residual energy.

> *ave* <sup>=</sup> *TotalResidual Energy <sup>E</sup> Number of Alive Nodes*

> > ( ) <sup>1</sup> 1 , <sup>=</sup> <sup>−</sup>

Another type of WSN is the Wireless Multimedia Sensor Networks (WMSNs). These types of sensor networks are designed to monitor multimedia events and

*p mod r*

*p*

*<sup>p</sup> Th i*

**2.3 Wireless multimedia sensor networks (WMSNs)**

*randTh* (5)

*DOI: http://dx.doi.org/10.5772/intechopen.94500*

Indication (RSSI) with other nodes' RSSI.

node to the total initial energy

*λ*min = constant.

*γ* = energy dependent factor which is the ratio of the energy remaining in the node to the total initial energy

*λ*min = constant.

*Wireless Sensor Networks - Design, Deployment and Applications*

routing to report events to a stationary sink node.

*ρ* = is the volume density of the sensor nodes. ∫ *v* = indicates integral over the volume V.

energy *E*R at distance *R*, is modeled as follows:

*β* = is a constant independent of range.

*E*T = the transmitted energy.

*Epl* = the probing energy

*α* = is a range-independent absorption coefficient.

(i.e., made of nodes in layer 1 with energy equal to *E*D.

*E*R = the received energy/power of the control packet.

ity of k nodes occupying a volume V as:

where:

where:

where

An Energy Optimized Path Unaware Layered Routing Protocol (E-PULRP) that minimizes the energy consumed in a dense 3D-WUSN is described in [14] as a typical example of an energy-based routing protocol. E-PULRP uses on the fly

E-PULRP has two phases: layering and communication. Nodes occupy layers in a concentric shell around the sink node in the layer phase. The nodes within one layer have the same number of hop counts to the sink node. The E-PULRP protocol is designed to follow a network model in which the total volume in the area of interest is subdivided into small-sized cubes with a binomial probability distribution for a node occupancy. The protocol assumes that the number of cubes is large. Following Poisson approximation to the binomial distribution, we can calculate the probabil-

( )

<sup>∫</sup> = = <sup>−</sup> <sup>∫</sup> *<sup>k</sup> v dv Pr x k exp v dv <sup>k</sup>* ρ

Physical properties such as temperature and chemical properties affect underwater communication. Another key factor that affects underwater communication is the depth of transceivers. In E-PULRP, for a transmitted energy of *E*T, the received

> ( ) (α β

In this layering phase, the protocol allows communication to occur only when energy levels of layers close to the sink are chosen. Concentric circles are formed around the central sink and the structure ensures packet forwarding towards the central sink. The layers in this phase are formed as follows: 1) Layer 0 initiates a probe of energy; 2) Nodes with energy equal to the detection threshold (*E*D) assign layer 1 to themselves; 3) The nodes in layer 1 communicates with the sink using a single hop and 4) waits for time *k* to transmits a probe with energy to create layer 2

The detection threshold, (*E*D) and the waiting time *k* are calculated as follows:

( ) (

*l thelayer* =

 ( ) γ

 <sup>+</sup> <sup>+</sup> <sup>=</sup> <sup>10</sup> /10 <sup>10</sup> *<sup>l</sup> pl*

*E*

α

λ

*D B l*

αα β)

*E* (3)

<sup>−</sup> <sup>=</sup> min *E E R D <sup>k</sup>* (4)

*R B R <sup>E</sup> <sup>E</sup> R*

*B* = takes values 10, 15 or 20 depending on the type of propagation.

<sup>+</sup> ) <sup>=</sup> /10 /10 <sup>10</sup> *T*

ρ

(1)

(2)

! 

**242**

In the communication phase, intermediate relay nodes are selected to send packets to the sink using multiple hop routing path to determine nodes *on the fly*. In this phase, nodes at the lower layers nearer to the source node are first identified as potential forwarding relay nodes. For example, if a source node, *S* in layer *l* sends a control packet, a node, *N* in the network who receives this control packet may declare itself as a potential forwarding node. This self-declared potential node waits at a time period given in Eq. (4) to listen if any other node has not declared itself as a relay node. It does this by comparing its signal strength Received Signal Strength Indication (RSSI) with other nodes' RSSI.

Once its RSSI value is less than all others, then it forwards the data packet, otherwise it will go into silent mode. A classic example of a cluster-based energy efficient UWSN is SEEC: Sparsity-aware energy efficient clustering protocol for underwater wireless sensor networks. SEEC was proposed by [15] to search sparse regions in the network. The network is divided into subregions of equal sizes. With the use of sparsity search algorithm (SSA) and density search algorithm (DSA), sparse and dense regions in the network. The lifetime of the network is improved through sink mobility in the sparse regions and through clustering in the dense regions. SEEC minimizes the energy consumed in the overall network by balancing the two sparsity search algorithm (SSA) and density search algorithm (DSA).

In SEEC, random nodes are deployed underwater and the network formed is divided into 10 regions. The position of each node in the network is dynamic due the dynamic nature of the deployable environment. The 10 regions are created to determine the sparse and dense regions. SEEC employs three sinks (i.e., a static sink at the top of central point of the sensor network field and two mobile sinks positioned at the sparse regions). Each sensor nodes coordinate is first determined in order to know its current region in the network field (i.e., sparse or dense).

A simple algorithm that checks the number of nodes in a region is used to determine a sparse or a dense region. If the number of nodes is minimum, then then node is in a sparse region other the node is considered to be in a dense region. When the searching is completed, then the nodes in the dense region are placed into clusters. To conserve energy and increase the lifetime of the network, SEEC is designed to cluster the top four (4) densely populated regions. Nodes in a dense region collaborate to select their cluster head (CH). The CH is the node with low depth and high residual energy.

$$E\_{\text{ave}} = \frac{Total\,\,Residual\,\,Energy}{Number\,\,of\,\,Alive\,\,Nodes\,\,}$$

*randTh* (5)

$$Th(i) = \frac{p}{1 - p\left(\mod{\left(r, \frac{1}{p}\right)}\right)}$$

#### **2.3 Wireless multimedia sensor networks (WMSNs)**

Another type of WSN is the Wireless Multimedia Sensor Networks (WMSNs). These types of sensor networks are designed to monitor multimedia events and

are capable of retrieving images, videos, audios, and scalar wireless sensor data. WMSNs come with additional challenges on top of the challenges of traditional WSNs. WMSN challenges include real-time delivery, high bandwidth demand, security, tolerable end-to-end delay, coverage, and proper jitter and frame loss rate [16]. Video streaming requires high bandwidth for it to be delivered. Streaming at high date rate also means that more energy will be consumed.

Several approaches have been proposed to also reduce the amount of energy consumed for delivering the content. Current studies have looked into the design of energy efficient MAC and routing layer protocols that are capable of handling low data rates [17, 18]. Also, to overcome the other challenges apart from the amount of energy utilized during content capturing and delivery, new approaches have been proposed in WMSNs to ensure data sharing security, quality of service assurance in providing real-time multimedia data and to ensure algorithms are designed to compress the images, videos, and audios before transmission to reduce the amount of energy consumed for such operations [19].

#### **2.4 Mobile wireless sensor networks (MWSNs)**

There are some application domains that static wireless sensors may not be a good option to deploy, hence, the introduction of Mobile Wireless Sensor Networks (MWSNs). Mobile sensors are capable of moving freely in their environment. This type of WSNs are good for deployments that require maximum coverage to monitor the physical environmental conditions since the mobile nodes can spread out when gathering information and reposition themselves. Mobile sensors improve coverage, energy efficiency, and channel usage [12]. In the last decade, studies into MWSNs have focused on sensor node coverage, energy efficiency, sensor relocation and deployment [20]. Following the studies conducted, the energy efficiency schemes discussed in this area is of key interest. There are two main approaches of improving the energy efficiency in MWSNs: reducing the energy consumption and harvesting energy to power sensor nodes.

#### **2.5 WSN topologies**

The arrangement of wireless sensor nodes in a Wireless Sensor Network is critical for maximizing the network lifetime. The network topology adopted in a deployment environment affects factors such as network connectivity. Network connectivity becomes more reliable if the proper topology is chosen for the deployment [13]. The topology also affects the energy consumed by nodes in network. For example, if a network is designed in such a way that the wireless sensor nodes are distributed far from their neighbors and the sink, the nodes will require high energy budget to establish connections and communicate [13].

Wireless Sensor Networks employ mesh (also known as peer-to-peer), star, star-mesh, and tree topologies, as show in **Figure 3**. Static network topologies do not suffer from topological changes but they suffer from minimum battery power and MAC layer problems.

In a star topology (**Figure 3**), the nodes are one-hop away from the sink. The sink or base station is at the central point and all the nodes in the network broadcast data through the sink to other nodes in the network. The star topology is energy efficient when adopted in WSN projects. But in situations where the sensor nodes are far from the sink node, the sensor nodes in the star topology requires a ton of energy compared to multi-hop through mesh. The challenge with this topology is that it is susceptible to failures when the sink node fails [21].

**245**

the sink.

**Figure 3.**

*Underwater deployment.*

ment strategy [23].

**3. WSN deployment techniques**

approaches or methodologies in **Figure 4**.

*Applications of Prediction Approaches in Wireless Sensor Networks*

In **Figure 3**, the sink serves as the root of the tree and all other nodes are considered as child nodes. In **Figure 3**, the sink or base station is a hop away from some of the nodes, which are its nearest neighbors. The other nodes require multiple hops to reach the sink. In a full mesh, every sensor node is connected to every other sensor node in the network. Finally, in **Figure 3**, the sink node is at the central point. Some nodes broadcast to the sink directly whilst other nodes require a hop to reach

In WSNs, sensor node deployment is the process of setting up or positioning wireless sensor nodes to be fully functional and operational in either real-world using testbeds, laboratory or simulated environments [22, 23]. Deploying sensor nodes in the environment (i.e., land, air, water) may differ from one application domain to the other. In some cases, deploying the sensor nodes to communicate from one medium to the other (i.e., air/land to water, water to air/land, water to land and vice versa, and water to water) require the right selection of the deploy-

The sensor nodes are deployed to collect data/information about their environment and transmit to a base station for onward processing. Nevertheless, the primary objective for node deployment consideration in WSN is to gain energy advantage since the sensor nodes are low powered devices. There are several deployment strategies for static and mobile sensor networks (**Figure 4**). Sensors in their physical environment play several roles in the network (i.e., act as a source node, relay node, cluster head, or sink/base station node) are deployed with any of the

The objective function for selecting the desired methodology or approach should be based on the coverage area, network connectivity, network lifetime, and data fidelity (ensuring that the data gathered is credible) [24]. Unlike static environments, placing and controlling sensor nodes in mobile environments is challenging. Similarly, node replacement is also a difficult task. The best deployment strategy for any implementation must meet the following criteria: 1) have clear objectives to meet the application requirements; 2) improve system

*DOI: http://dx.doi.org/10.5772/intechopen.94500*

**Figure 3.** *Underwater deployment.*

*Wireless Sensor Networks - Design, Deployment and Applications*

high date rate also means that more energy will be consumed.

of energy consumed for such operations [19].

harvesting energy to power sensor nodes.

budget to establish connections and communicate [13].

that it is susceptible to failures when the sink node fails [21].

**2.5 WSN topologies**

and MAC layer problems.

**2.4 Mobile wireless sensor networks (MWSNs)**

are capable of retrieving images, videos, audios, and scalar wireless sensor data. WMSNs come with additional challenges on top of the challenges of traditional WSNs. WMSN challenges include real-time delivery, high bandwidth demand, security, tolerable end-to-end delay, coverage, and proper jitter and frame loss rate [16]. Video streaming requires high bandwidth for it to be delivered. Streaming at

Several approaches have been proposed to also reduce the amount of energy consumed for delivering the content. Current studies have looked into the design of energy efficient MAC and routing layer protocols that are capable of handling low data rates [17, 18]. Also, to overcome the other challenges apart from the amount of energy utilized during content capturing and delivery, new approaches have been proposed in WMSNs to ensure data sharing security, quality of service assurance in providing real-time multimedia data and to ensure algorithms are designed to compress the images, videos, and audios before transmission to reduce the amount

There are some application domains that static wireless sensors may not be a good option to deploy, hence, the introduction of Mobile Wireless Sensor Networks (MWSNs). Mobile sensors are capable of moving freely in their environment. This type of WSNs are good for deployments that require maximum coverage to monitor the physical environmental conditions since the mobile nodes can spread out when gathering information and reposition themselves. Mobile sensors improve coverage, energy efficiency, and channel usage [12]. In the last decade, studies into MWSNs have focused on sensor node coverage, energy efficiency, sensor relocation and deployment [20]. Following the studies conducted, the energy efficiency schemes discussed in this area is of key interest. There are two main approaches of improving the energy efficiency in MWSNs: reducing the energy consumption and

The arrangement of wireless sensor nodes in a Wireless Sensor Network is critical for maximizing the network lifetime. The network topology adopted in a deployment environment affects factors such as network connectivity. Network connectivity becomes more reliable if the proper topology is chosen for the deployment [13]. The topology also affects the energy consumed by nodes in network. For example, if a network is designed in such a way that the wireless sensor nodes are distributed far from their neighbors and the sink, the nodes will require high energy

Wireless Sensor Networks employ mesh (also known as peer-to-peer), star, star-mesh, and tree topologies, as show in **Figure 3**. Static network topologies do not suffer from topological changes but they suffer from minimum battery power

In a star topology (**Figure 3**), the nodes are one-hop away from the sink. The sink or base station is at the central point and all the nodes in the network broadcast data through the sink to other nodes in the network. The star topology is energy efficient when adopted in WSN projects. But in situations where the sensor nodes are far from the sink node, the sensor nodes in the star topology requires a ton of energy compared to multi-hop through mesh. The challenge with this topology is

**244**

In **Figure 3**, the sink serves as the root of the tree and all other nodes are considered as child nodes. In **Figure 3**, the sink or base station is a hop away from some of the nodes, which are its nearest neighbors. The other nodes require multiple hops to reach the sink. In a full mesh, every sensor node is connected to every other sensor node in the network. Finally, in **Figure 3**, the sink node is at the central point. Some nodes broadcast to the sink directly whilst other nodes require a hop to reach the sink.
