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تعداد صفحات این فایل: ۲۶ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Rearrangement of mobile wireless sensor nodes for coverage maximization based on immune node deployment algorithm~~en~~

Abstract

One of the primary objectives of Wireless Sensor Network (WSN) is to provide full coverage of a sensing field as long as possible. The deployment strategy of sensor nodes in the sensor field is the most critical factor related to the network coverage. However, the traditional deployment methods can cause coverage holes in the sensing field. Therefore, this paper proposes a new deployment method based on Multi-objective Immune Algorithm (MIA) and binary sensing model to alleviate these coverage holes. MIA is adopted here to maximize the coverage area of WSN by rearranging the mobile sensors based on limiting their mobility within their communication range to preserve the connectivity among them. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation for different network environments with and without obstacles. Simulation results show that the proposed algorithm improves the coverage area and the mobility cost of WSN.

۱ Introduction

A Wireless Sensor Network (WSN) is a distributed system which is composed of tiny, low-cost, battery-operated sensor nodes that collaborate together for the purpose of achieving a certain task. For instance, WSNs can be used for environment and habitat monitoring, traffic measurement on roads, vehicle tracking and personnel tracking inside buildings [1]. Coverage is one of the most important performance metrics for Wireless Sensor Networks (WSNs) since it reflects how well a sensor field is monitored. The coverage problem in WSN has been addressed either as a target coverage or an area coverage [2]. The target coverage algorithms are adopted to maximize the number of targets that could be covered based on assumption that the sensing field is divided into targets [3,4]. On the other hand, the area coverage algorithms are used to maximize the covered area of the whole sensing field [5–۱۱].

The deployment strategy of sensor nodes in the sensor field is the most critical factor related to the network coverage. The sensor nodes can be deployed either deterministic or random. A deterministic deployment may be feasible in friendly and accessible environments. While, a random deployment is usually preferred in large scale WSNs not only because it is easy and less expensive, but also it might be the only choice in hostile environments such as battle field or forest environment. However, random deployment of the sensor nodes can cause coverage holes in the sensor field; therefore, in most cases, random deployment is not guaranteed to be efficient for achieving the maximum coverage [4–۷].

Solution of the coverage holes’ problem depends on how the sensor nodes are rearranged with respect to each other to maximize the coverage area and also prolongs the operational life of the individual nodes with limiting the mobility cost. This is Non-deterministic Polynomial-time hard (NP-hard) problem [12,13]. Therefore, a new deployment algorithm based on Multi-objective Immune Algorithm (MIA) [14–۱۶] and binary sensing model is proposed here to solve the above mentioned problem. The proposed algorithm utilizes the MIA to rearrange the random deployed sensor nodes based on maximizing the coverage area and minimizing the dissipated energy during the movement process. Moreover, the proposed deployment algorithm preserves the connectivity among the sensors by limiting their mobility within their communication range. The paper is organized as follows. Section 2 is a literature survey about various deployment algorithms. The network and sensing models and the objectives of the proposed algorithm are described in Section 3. Section 4 explains the proposed immune node deployment algorithm and how the multi-objective immune algorithm is used to maximize the covered area and minimizes the consumed energy during the movement process. In Section 5, the simulation results and discussion are given. Finally, Section 6 offers some conclusions.

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