Mohamed Elhoseny - Dynamic Wireless Sensor Networks: New Directions for Smart Technologies (Studies in Systems, Decision and Control (165))

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Part I
WSN for Complex and Mobile-Based Applications

The first part of this book discusses a set of complex and mobile-based applications of wireless sensor networks. This part aims to show how a WSN affects the efficiency of each of those applications and how its performance is affected by the working environment. It introduces briefly the wireless sensor network concepts and terminologies such as the network lifetime, terminologies, role in real life, and its structuring models. Then, it explains the different routing models and network structures for data routing in WSN. Moreover, the application areas of WSN and the network types, i.e., homogeneous and heterogeneous network, are explained. One of the main goals of this part is to provide extended experiments in different environments with a variety of parameters to evaluate WSNs performance.

Springer International Publishing AG, part of Springer Nature 2019

Mohamed Elhoseny and Aboul Ella Hassanien Dynamic Wireless Sensor Networks Studies in Systems, Decision and Control

1. Mobile Object Tracking in Wide Environments Using WSNs

Mohamed Elhoseny 1 and Aboul Ella Hassanien 2

(1)

Faculty of Computers and Information, Mansoura University, Dakahlia, Egypt

(2)

Department of Information Technology, Cairo University, Giza, Egypt

Mohamed Elhoseny

Email:

Abstract

Covering a specific field and transferring data to Base Station (BS) is a real defiance. Although there are extended efforts to build a routing protocol that avoids a high energy consumption, the dynamic nature and complex environments of most of WSN recent application makes building such protocol a big challenge. To avoid energy exhaustion, many machine learning algorithms are used to manage the network operations. We proposed a new model to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. The proposed model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that the proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted.

1.1 Introduction

Wireless Sensor Networks (WSNs) are widely used in many applications such as industry [] to reach the BS instead of direct transmission, especially in large ROIs with only one BS.

Many target coverage methods assume that the targets are known, and each target is covered by one sensor [].

Non-stationary K-coverage is often needed when a reliable monitoring capability is desired as in surveillance and military applications. Due to the energy constraint of wireless sensors and often infeasibility of replacement or recharging, it is necessary for the sensors to be densely deployed. Keeping all sensors active will deplete their energy quickly. A typical scenario is multi-agent based corporative field monitoring. Mobile agents collect and transform data to ensure integrity and security [] in the parameter.

We propose a Genetic Algorithm (GA) []. In the introduced problem, the data transmission round is a period that the data of targets are collected and transmitted to the base station. A GA-based method was proposed to optimize the sensor covers with a goal of maximizing the network lifetime by determining the mode of sensor covers. Based on a set of factors such as the coverage range of each sensor, expected consumed energy, the distance to the base station, and targets positions, the GA forms the covers after determining the optimum cover heads that are responsible for transferring the data to the base station. Thus, the proposed model ensures that the monitored area is fully covered by a minimum number of sensors.

This study has two main contributions. Firstly, GA-based cover forming method that creates all possible sensor covers. Secondly, a WSN covers management method that switches between different sensor covers to maximize the network lifetime. Section .

1.2 Related Work

Target covering problem has been attracting significant attention in WSNs [] introduced a power efficient monitoring model which proposed: (1) an efficient data structure to efficiently represent the monitoring area; (2) an algorithm for sensor monitoring; and (3) distribution protocols to make a balance between the monitoring and communication power consumption. The results of their proposed model revealed a significant advantage in quality, flexibility, and scalability.

Cardei et al. in [] by increasing the lifetime of the network without the constraint that chosen set covers are disjoint; thus, a sensor may appear in different covers. The target covering was modeled as a Maximum Set Covers (MSC) problem, and two heuristic algorithms were employed to compute the sets based on linear programming and greedy approach.

Most studies in the field of wireless sensor network have assumed that the sensors have the same sensing range [], where each target was covered by at least K sensors, this is called K -coverage.

There are many studies that employ the bio-inspired algorithms to optimize the K -coverage problem. In [].

1.3 The Problem Formulation and the Proposed Solution

Let be a wireless sensor network, where is a set of sensors with a sensing range , is a set of targets with known locations, m is the number of sensors, and n is the number of targets. Each target is sensed with one or more sensors, e.g. the target is covered with the sensors and . The collected data are processed by a sink node. A sensor is in the active mode if it acquires or relays data, or both. A sensor in the sleep state when the sensor is not performing any tasks.

The network lifetime [] is defined as the period from the network being set up till (1) one or more targets cannot be covered by at least one sensor, or (2) a route between each sensor to the sink cannot be found. The network lifetime is maximized in the Connected Target Coverage (CTC) problem, which can be modeled as a Maximum Cover Tree (MCT).

The proposed model identifies the maximum number of non-disjoint sets of the sensors, namely sensor cover, which is bounded by

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