**1. Introduction**

Wireless sensor networks consist of many sensor nodes connected and providing valuable information for further processing to serve industry, people and society. The trend in WSNs is toward miniaturization, rapid deployment in large applications, reliable routing of data and its proper handling.

The broad description is based on four basic facts which are:

Capture: referring to the action of transforming an analog physical quantity into a digital signal. In our case, a study on the types of sensors seems necessary for the adequate choice for the application detection of events.

Concentrate and route: studying current routing techniques and refurbishing them to suit the event detection application.

Extract and Process: the data collected in their binary forms will be exploited through the use of AI techniques in perfect symbiosis with the proposed general architecture.

Store and Present: the fact of aggregating data, produced in real time, meta tagged, arriving in a predictable or unpredictable way, and the ability to restitute the information in a way that is understandable by Man, while offering him a means of acting and/or interacting.

### **2. Deployment issues**

Identifying sensor node location and deployment strategies will be the first step toward building the network. Determination of sensor placement and rollout

method depends on several criteria, mainly the intended applications, surrounding area, estimated deployment time and cost.

α and β are the peripheral oriented parameters relative to the sensor node.

*WSN for Event Detection Applications: Deployment, Routing, and Data Mapping Using AI*

In deterministic deployment, sensor nodes are arranged around preset positions and carefully selected locations. Therefore, geometric shape designs can be adopted

The firs purpose is to identify the appropriate number of sensor nodes to be

Let "A" be the size of the AOI and "As" the area related to the sensor node's geometric configuration. The area of the geometric patterns of the sensor nodes is determined by knowing the length of the sides, designated by **lx**, i.e. **lc** for the quadrilateral shape and **lt** for the triangular one, and **lh** for the hexagonal structure.

> *<sup>N</sup>* <sup>¼</sup> *<sup>A</sup> AS*

To have full connectivity, the pattern surface for each sensor node is given as

The number of sensor nodes adequate to ensure total connectivity is given by:

ffiffiffi 3 p 2

*lh*<sup>2</sup> <sup>¼</sup> <sup>3</sup> ffiffiffi 3 p 4

*A*

8

>>>>>>>>>>><

>>>>>>>>>>>:

*A* ffiffiffi 3 p <sup>2</sup> *Rc*<sup>2</sup>

*A* 3 ffiffiffi 3 p 4 *Rc*<sup>2</sup>

*lc*<sup>2</sup> <sup>¼</sup> *Rc*<sup>2</sup> quadrilateral shape ffiffiffi

*Rc*<sup>2</sup> quadrilateral shape

*triangular shape*

*hexagonal shape*

*Rc*<sup>2</sup> *triangular shape*

*Rc*<sup>2</sup> *hexagonal shape*

(4)

(5)

(6)

**2.2 How detection and connectivity issues impact WSN coverage?**

Then, the estimated number of sensor nodes "N," is given by Eq. 4.

3 p 2 *lt*<sup>2</sup> <sup>¼</sup>

8 >>>>><

>>>>>:

*N*ð Þ¼ *con*

3 ffiffiffi 3 p 4

to form the coverage area as shown in **Figure 1**.

*DOI: http://dx.doi.org/10.5772/intechopen.94085*

*As*ð Þ¼ *con*

follows:

**Figure 1.**

**105**

*Geometrical shapes of cover areas.*

deployed for full connectivity in the area in question.

#### **2.1 Nodes deployment and connectivity**

#### *2.1.1 Strategies of nodes deployment*

Wireless sensor nodes may be enormously affected by node deployment. In fact, Wireless nodes deployment is able to extend network lifetime, efficient reliability and routing, well preserving of network energy, ensuring connectivity, etc. Placing nodes in a defined zone of interest is often not pre-determined at the outset of network layout and deployment. Indeed, the manner of how to locate the sensor nodes depends mainly on the application type and the operating conditions surrounding them. In WSNs, both random and deterministic methods of node placement can be applied [1, 2].

It should be noted here that roll-out can occur all at once or can be a continuous process where additional nodes could be redeployed in the given zone, thus two deployment strategies can be identified in WSN: uniform distribution and non-uniform one.

#### *2.1.2 WSN connectivity*

Communication modeling in low-power and lossy networks is difficult because nodes transmit at reduced power and radio links lack sufficient reliability. Hence, this research adopts the disk model as a deterministic communication method to ease the process of analytical computation [3, 4]. The model considers each wireless node "ni" as being efficient in acquiring and transmitting data to nearby nodes. The communication range marked " Ri " is a function of the level of transmission power of a node. Accordingly, two nodes "ni" and "nj " can communicate with one another bilaterally only if the Euclidean distance there between is at least the minimum of their communication range (Eq. (1)).

$$d\left(n\_i, n\_j\right) \le \min\left\{R\_i, R\_j\right\} \tag{1}$$

#### *2.1.3 Detection in lattice WSNs*

A network of wireless sensors is deployed in an area of interest for event detection, so each point in the area is covered by a sensor node if the Euclidean distance between the sensor node and this point is less than the sensing range RS.

The probability that a sensor detects an event in the area of interest at a distance "d" is given by Eq. 2.

$$P(d) = \begin{cases} \mathbf{1} & \text{if } \ d \le R\_{\mathcal{S}} \\ \mathbf{0} & \text{if } \ d > R\_{\mathcal{S}} \end{cases} \tag{2}$$

Due to the environmental and geographical conditions relative to the area of interest and manufacturers' requirements, we will define an uncertainty parameter "e" to make the model more realistic. Hence, the probability that a sensor will detect an event in the area of interest at a distance "d" is given by Eq. 3.

$$P(\mathbf{d}) = \begin{cases} \mathbf{1} & \text{if } \mathbf{d} < (\mathbf{R}\_{\mathbf{S}} + \mathbf{e})\\ \mathbf{e}^{-\mathbf{a}(\mathbf{R} - \mathbf{R}\_{\mathbf{S}})^{\theta}} & \text{if } (\mathbf{R}\_{\mathbf{S}} - \mathbf{e}) \le \mathbf{d} \le (\mathbf{R}\_{\mathbf{S}} + \mathbf{e})\\ \mathbf{0} & \text{if } \mathbf{d} > (\mathbf{R}\_{\mathbf{S}} + \mathbf{e}) \end{cases} \tag{3}$$

*WSN for Event Detection Applications: Deployment, Routing, and Data Mapping Using AI DOI: http://dx.doi.org/10.5772/intechopen.94085*

α and β are the peripheral oriented parameters relative to the sensor node.

#### **2.2 How detection and connectivity issues impact WSN coverage?**

In deterministic deployment, sensor nodes are arranged around preset positions and carefully selected locations. Therefore, geometric shape designs can be adopted to form the coverage area as shown in **Figure 1**.

The firs purpose is to identify the appropriate number of sensor nodes to be deployed for full connectivity in the area in question.

Let "A" be the size of the AOI and "As" the area related to the sensor node's geometric configuration. The area of the geometric patterns of the sensor nodes is determined by knowing the length of the sides, designated by **lx**, i.e. **lc** for the quadrilateral shape and **lt** for the triangular one, and **lh** for the hexagonal structure. Then, the estimated number of sensor nodes "N," is given by Eq. 4.

$$N = \frac{A}{A\_S} \tag{4}$$

To have full connectivity, the pattern surface for each sensor node is given as follows:

$$As(\text{con}) = \begin{cases} lc^2 = Rc^2 & \text{quadlilateral shape} \\ \frac{\sqrt{3}}{2}lt^2 = \frac{\sqrt{3}}{2}Rc^2 & \text{triangle shape} \\ \frac{3\sqrt{3}}{4}lh^2 = \frac{3\sqrt{3}}{4}Rc^2 & \text{hexagonal shape} \end{cases} \tag{5}$$

The number of sensor nodes adequate to ensure total connectivity is given by:

$$N(con) = \begin{cases} \frac{A}{Rc^2} & \text{quadlilateral shape} \\ \frac{A}{\sqrt{3}} & \text{triangle shape} \\ \frac{\sqrt{3}}{2} Rc^2 & \text{else} \\ \frac{A}{3\sqrt{3}} & \text{hexagonal shape} \\ \frac{A}{4} & \text{2} \end{cases} \tag{6}$$

**Figure 1.** *Geometrical shapes of cover areas.*

method depends on several criteria, mainly the intended applications, surrounding

Wireless sensor nodes may be enormously affected by node deployment. In fact, Wireless nodes deployment is able to extend network lifetime, efficient reliability and routing, well preserving of network energy, ensuring connectivity, etc. Placing nodes in a defined zone of interest is often not pre-determined at the outset of network layout and deployment. Indeed, the manner of how to locate the sensor nodes depends mainly on the application type and the operating conditions surrounding them. In WSNs, both

It should be noted here that roll-out can occur all at once or can be a continuous process where additional nodes could be redeployed in the given zone, thus two deployment strategies can be identified in WSN: uniform distribution and non-uniform one.

Communication modeling in low-power and lossy networks is difficult because nodes transmit at reduced power and radio links lack sufficient reliability. Hence, this research adopts the disk model as a deterministic communication method to ease the process of analytical computation [3, 4]. The model considers each wireless node "ni" as being efficient in acquiring and transmitting data to nearby nodes. The communication range marked " Ri " is a function of the level of transmission power

bilaterally only if the Euclidean distance there between is at least the minimum of

� �≤ min *Ri*, *Rj*

A network of wireless sensors is deployed in an area of interest for event detection, so each point in the area is covered by a sensor node if the Euclidean distance

The probability that a sensor detects an event in the area of interest at a distance

**1** *if d*≤*RS* **0** *if d*>*RS*

*if* ð Þ RS � e ≤d≤ð Þ RS þ e

*d ni*, *n <sup>j</sup>*

between the sensor node and this point is less than the sensing range RS.

�

Due to the environmental and geographical conditions relative to the area of interest and manufacturers' requirements, we will define an uncertainty parameter "e" to make the model more realistic. Hence, the probability that a sensor will detect

**1** *if* d<ð Þ RS þ e

**0** *if* d>ð Þ RS þ e

*P d*ð Þ¼

an event in the area of interest at a distance "d" is given by Eq. 3.

*e*�*α*ð Þ *<sup>R</sup>*�*RS <sup>β</sup>*

*P d*ð Þ¼

8 ><

>:

" can communicate with one another

� � (1)

(2)

(3)

random and deterministic methods of node placement can be applied [1, 2].

area, estimated deployment time and cost.

*Wireless Sensor Networks - Design, Deployment and Applications*

**2.1 Nodes deployment and connectivity**

of a node. Accordingly, two nodes "ni" and "nj

their communication range (Eq. (1)).

*2.1.3 Detection in lattice WSNs*

"d" is given by Eq. 2.

**104**

*2.1.1 Strategies of nodes deployment*

*2.1.2 WSN connectivity*

The second purpose is to identify the appropriate number of sensor nodes to be deployed to ensure total coverage; the geometric pattern surface relative to a sensor node is given by:

$$As(co\nu) = \begin{cases} Rs^2 & \text{quadlilateral shape} \\ 3\frac{\sqrt{3}}{2}Rs^2 & \text{triangle shape} \\ \frac{3\sqrt{3}}{4}Rs^2 & \text{hexagonal shape} \end{cases} \tag{7}$$

The number of sensor nodes adequate to ensure total detection coverage is given by:

$$N(\text{cov}) = \begin{cases} \frac{A}{Rs^2} & \text{quadrilateral shape} \\ \frac{A}{3\sqrt{3}} & \text{triangle shape} \\ \frac{3\sqrt{3}}{2}Rs^2 & \\ \frac{A}{3\sqrt{3}} & \text{hexagonal shape} \\ \frac{3\sqrt{3}}{4} & \end{cases} \tag{8}$$

network connectivity. Also, the sensors need to have at least K neighbors in order to have K degree of connectivity in the network. In other words, a network in which all the sensors have at least one neighbor (i.e. there is no isolated sensor) implies

*WSN for Event Detection Applications: Deployment, Routing, and Data Mapping Using AI*

For more practical purposes we can consider a 100 m x 100 m area, changing the number of deployed nodes (from N = 100 to N = 500) and the coverage radius RC (from 0 m up to 20 m). Looking at the probability of network connectivity we can see that: there is a crucial communication radius over which a high probability of network connectivity exists. This radius is determined based on the configuration of

that it is highly connected (high probability level of connection).

**Figure 2**.

**107**

**Figure 2.**

*Probability of network connectivity.*

*DOI: http://dx.doi.org/10.5772/intechopen.94085*

its intended application.

**3. Toward proper routing**

once deployment scenarios are dealt with.

the sensor nodes in accordance with the communication range as shown in

In this first section, we looked at the fundamental issues of wireless sensor network deployment, namely coverage and connectivity [5]. The first reflects the ability of the sensor network to provide detection in the area of interest and the second reflects the reliability with which the information collected by the sensor nodes will be transmitted for processing. But it is clear that the routing of data itself deserves to be properly considered for an appropriate sensor network dedicated to

Routing data across nodes toward endpoints becomes the next logical priority,

Commonly, collected sensor data should be directed to a sink node via multi-hop routing. Certain applications may have different requirements, or constraints on the quality of the data transmission, such as end-to-end delay, jitter or churn, packet loss, etc. A set of constraints must always be respected. In fact, there are several proactive and reactive routing protocols that have been proposed for WSN [6]. One of them is the standardized proactive RPL (Routing Protocol for Low-Power and Lossy Networks), designed basically for "many to one" communications. A

Destination Oriented Acyclic Graph (DODAG) is used for data collection, which is

mainly a tree directed to the sink node. The calculation of the DODAG tree parameters is based on an objective function that is defined according to a single

measurement. This section considers the use of RPL in WSN.

In harsh environments such as a battlefield or a disaster region, deterministic deployment of sensors is very risky and/or infeasible. In this case, random deployment often becomes the only option.

Providing connectivity within the network is a fundamental objective in wireless sensor networks for communication and data exchange between sensor nodes. A sensor is supposed to be connected if and only if it has a direct or indirect (multi-hop) communication path to a destination. For the sake of brevity, the explanation deals only with the case of a direct communication (a single jump) denoted 1-connected.

A wireless sensor network is said to be connected if all sensors are connected and if there is no isolated sensor in the network. Indeed, the number of its neighbors in its communication range RC is called the "degree of sensor node." It is in this sense that we will say that a sensor is isolated if its degree is equal to zero.

Consider arbitrarily a sensor in the network, the probability that it is isolated is equivalent to the probability that there is no neighboring sensor in its communication radius Rc, expressed in Eq. 9.

$$P\_{i o} \left( \mathbf{S}\_i \right) = \mathbf{e}^{-P \pi R \mathbf{c}^2} \tag{9}$$

The probability that the sensor Si is not isolated is given by:

$$P\_{\rm non-iso}(\mathbb{S}\_i) = \mathbf{1} - e^{-P\pi Rc^2} \tag{10}$$

If we consider that all sensors are deployed independently and uniformly in the area of interest, then they have the same probability of being non-isolated. Thus, the probability that there is no isolated sensor is as follows (Eq. 11)

$$P\_{non-iso} = \prod\_{i=1}^{N} P\_{non-iso}(\mathbf{S}\_i) = \left(\mathbf{1} - e^{-P\pi R c^2}\right)^N \tag{11}$$

The case where there is no isolated sensor in the network is required but not enough to say that this one stays connected. This gives us just the upper limit of the *WSN for Event Detection Applications: Deployment, Routing, and Data Mapping Using AI DOI: http://dx.doi.org/10.5772/intechopen.94085*

**Figure 2.** *Probability of network connectivity.*

The second purpose is to identify the appropriate number of sensor nodes to be deployed to ensure total coverage; the geometric pattern surface relative to a sensor

The number of sensor nodes adequate to ensure total detection coverage is given by:

*Rs*<sup>2</sup> quadrilateral shape

*Rs*<sup>2</sup> *triangular shape*

(7)

(8)

(9)

(10)

(11)

*Rs*<sup>2</sup> *hexagonal shape*

*Rs***<sup>2</sup> quadrilateral shape**

*triangular shape*

*hexagonal shape*

node is given by:

*As*ð Þ¼ *cov*

*N*ð Þ¼ cov

deployment often becomes the only option.

tion radius Rc, expressed in Eq. 9.

**106**

3 ffiffiffi 3 p 2

8 >>>>><

*Wireless Sensor Networks - Design, Deployment and Applications*

>>>>>:

3 ffiffiffi 3 p 4

*A*

8

>>>>>>>>>>><

>>>>>>>>>>>:

*A* **3** ffiffiffi **3** p **<sup>2</sup>** *Rs***<sup>2</sup>**

*A* **3** ffiffiffi **3** p **<sup>4</sup>** *Rs***<sup>2</sup>**

deployment of sensors is very risky and/or infeasible. In this case, random

that we will say that a sensor is isolated if its degree is equal to zero.

The probability that the sensor Si is not isolated is given by:

the probability that there is no isolated sensor is as follows (Eq. 11)

*N*

*i*¼1

*Pnon*�*iso* <sup>¼</sup> <sup>Y</sup>

In harsh environments such as a battlefield or a disaster region, deterministic

Providing connectivity within the network is a fundamental objective in wireless sensor networks for communication and data exchange between sensor nodes. A sensor is supposed to be connected if and only if it has a direct or indirect (multi-hop) communication path to a destination. For the sake of brevity, the explanation deals only with the case of a direct communication (a single jump) denoted 1-connected. A wireless sensor network is said to be connected if all sensors are connected and if there is no isolated sensor in the network. Indeed, the number of its neighbors in its communication range RC is called the "degree of sensor node." It is in this sense

Consider arbitrarily a sensor in the network, the probability that it is isolated is equivalent to the probability that there is no neighboring sensor in its communica-

�*PπRc*<sup>2</sup>

�*PπRc*<sup>2</sup>

�*PπRc*<sup>2</sup> � �*<sup>N</sup>*

*Piso*ð Þ¼ *Si e*

*Pnon*�*iso*ð Þ¼ *Si* 1 � *e*

If we consider that all sensors are deployed independently and uniformly in the area of interest, then they have the same probability of being non-isolated. Thus,

*Pnon*�*iso*ð Þ¼ *Si* 1 � *e*

The case where there is no isolated sensor in the network is required but not enough to say that this one stays connected. This gives us just the upper limit of the network connectivity. Also, the sensors need to have at least K neighbors in order to have K degree of connectivity in the network. In other words, a network in which all the sensors have at least one neighbor (i.e. there is no isolated sensor) implies that it is highly connected (high probability level of connection).

For more practical purposes we can consider a 100 m x 100 m area, changing the number of deployed nodes (from N = 100 to N = 500) and the coverage radius RC (from 0 m up to 20 m). Looking at the probability of network connectivity we can see that: there is a crucial communication radius over which a high probability of network connectivity exists. This radius is determined based on the configuration of the sensor nodes in accordance with the communication range as shown in **Figure 2**.

In this first section, we looked at the fundamental issues of wireless sensor network deployment, namely coverage and connectivity [5]. The first reflects the ability of the sensor network to provide detection in the area of interest and the second reflects the reliability with which the information collected by the sensor nodes will be transmitted for processing. But it is clear that the routing of data itself deserves to be properly considered for an appropriate sensor network dedicated to its intended application.

#### **3. Toward proper routing**

Routing data across nodes toward endpoints becomes the next logical priority, once deployment scenarios are dealt with.

Commonly, collected sensor data should be directed to a sink node via multi-hop routing. Certain applications may have different requirements, or constraints on the quality of the data transmission, such as end-to-end delay, jitter or churn, packet loss, etc. A set of constraints must always be respected. In fact, there are several proactive and reactive routing protocols that have been proposed for WSN [6]. One of them is the standardized proactive RPL (Routing Protocol for Low-Power and Lossy Networks), designed basically for "many to one" communications. A Destination Oriented Acyclic Graph (DODAG) is used for data collection, which is mainly a tree directed to the sink node. The calculation of the DODAG tree parameters is based on an objective function that is defined according to a single measurement. This section considers the use of RPL in WSN.
