Saturday , June 23 2018


An Interactive Genetic Algorithm for
Mobile Sensor Networks

Ali NOROUZI, Faezeh Sadat BABAMIR, A. Halim ZAIM
Department of Computer Engineering, Istanbul University,

Avcilar, Istanbul, Turkey

Abstract: In this paper, we describe an interactive approach to design mobile sensor networks. The node-mobility aspect requires an online or interactive algorithm to determine the optimal network-coverage solution for a given area of interest. Hence, we develop a real-time genetic algorithm to find the suitable direction of node locomotion, considering either coverage of the target area or estimation of the optimum energy consumption. The main purpose is to provide a solution that can extend the network lifetime. The simulation results indicate that the proposed fitness function achieves our objectives.

Keywords: Mobile Sensor Networks, WSN, Genetic Algorithm, Network Life time, Optimum Energy Consumption.

>>Full Text
Ali NOROUZI, Faezeh Sadat BABAMIR, A. Halim ZAIM, An Interactive Genetic Algorithm for Mobile Sensor Networks, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (2), pp. 213-218, 2013.


Wireless sensor networks (WSNs) include numerous unsupervised devices capable of sensing, computation and communication. These energy-restrained devices are expected to be used for many different kinds of applications [1]. For instance, WSNs can be used for environment and habitat monitoring, traffic measurement on roads, vehicle tracking and personnel tracking inside buildings. Even though WSNs have a variety of applications, their deployment usually has two common objectives: (a) obtaining the maximum area coverage for a specific number of nodes and (b) prolonging the operational life of the individual nodes [2].

A mobile sensor network is a WSN with locomotion capabilities, consisting of several nodes with sensing, computation and communication functions [1]. This mobility aspect presents a design challenge in unknown environments. A genetic algorithm (GA) could be an innovative approach to simultaneously optimise coverage and lifetime problems. Network lifetime could be defined as the period of time it takes for the first node, or a fraction of all the nodes in the network, to be depleted of their energy.

The aforementioned GA method is applicable for the optimisation of both dynamic environments and dynamic network topologies [3]. In this paper, we develop a real-time GA in order to design a network with maximum coverage and minimum energy consumption, which results in extended network lifetime. In most cases, a basic sensor node consists of five main components: (a) a power supply that is considered to be the only energy source, (b) a controller with memory, (c) a sensing device, (d) a communications system and (e) a mobile platform for mobile wireless sensor nodes. All these constituent parts of the architecture consume energy; especially during the spreading procedure of mobile sensor nodes, which adjusts node position with a trade-off between area coverage and energy usage [4].

A GA is a heuristic search technique that is used to automatically find optimal solutions, while trying to avoid local maxima [5]. This method is inspired from nature and has numerous applications in model checking [3]. Also, it is suitable for solving non-linear optimisation problems and for finding the probable global optimisation value of a fitness function.

Fundamentally, a GA comprises three important components: recombination, mutation and a fitness function. Many researchers have concentrated on the fitness function, which operates on chromosomes. In our paper, as in [6], we also propose a fitness function (the main procedure of GAs) to estimate an optimal solution. The optimal solution(s) is selected according to two important parameters.

Energy consumption is one the most important parameters for measuring the efficiency of network positioning and can be divided into three parts: the energy usage of transmission for each node, receiving packets by cluster head and data transmission from cluster head to sink. In this paper, we assume that the energy consumption of transmission for each node represents the energy usage. Network coverage is another important parameter to consider in measuring the size of a chromosome. In this work, the size of a chromosome depends on its energy consumption and network coverage [7].

The remainder of this paper is organised as follows: (a) Section 2 reviews related work, (b) Section 3 shows our contribution, (c) in Section 4 we propose a solution and elaborate on details of our algorithm, (d) Section 5 presents our simulation and an analysis of our results and (e) Section 6 summarises the conclusions.


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