Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks: A Two Phase Particle Swarm Optimization Algorithm

Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks: A Two Phase Particle Swarm Optimization Algorithm

Nor Azlina Ab. Aziz (Multimedia University, Malaysia), Ammar W. Mohemmed (Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, New Zealand), Mohamad Yusoff Alias (Multimedia University, Malaysia), Kamarulzaman Ab. Aziz (Multimedia University, Malaysia) and Syabeela Syahali (Multimedia University, Malaysia)
Copyright: © 2012 |Pages: 21
DOI: 10.4018/jncr.2012040103
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Abstract

WSN is a group of low-cost, low-power, multifunctional and small size wireless sensor nodes that work together to sense the environment, perform simple data processing and communicate wirelessly over a short distance. Mobile wireless sensor networks (WSN) coverage can be enhanced by moving the sensors so that a better arrangement is achieved. However, movement is a high energy consumption task. To maximize coverage the sensors need to be placed not too close to each other so that the sensing capability of the network is fully utilised; however they must not be located too far from each other to avoid coverage holes. It is desired to achieve optimal coverage and at the same time not to relax the mobility energy consumption issue due to the fact that sensors have a limited energy supply. This research is interested in solving the coverage and energy conservation issues of mobile wireless sensors using PSO. In this paper the WSN coverage maximization problem is considered by taking into account the energy spent for sensor repositioning. Thus there are two objectives to be optimized, namely maximizing the coverage and conserving the energy. The two objectives are tackled one by one, starting with the coverage maximization followed by energy conservation. Hence a two-phase PSO approach is proposed. The results show that the proposed algorithm successfully achieves its objectives to reduce the energy usage while at the same time improve the coverage. The energy usage is reduced by cutting down the maximum distance moved.
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Introduction

WSN is a group of low-cost, low-power, multifunctional and small size wireless sensor nodes that work together to sense the environment, perform simple data processing and communicate wirelessly over a short distance (Zhao, Wen, Shang, & Wang, 2004). Some of these sensor nodes are able to move on their own. The mobility can be achieved by mounting the sensors on mobile platforms such as in the Robomote project (Dantu et al., 2005). With the ability to move independently, these mobile sensors are able to self deploy and self repair, thus adding more to their values (Howard & Poduri, 2005).

The WSN revolutionize the information systems, where it is able to automatically and continuously supply users with new information as the events happen (Zhao & Guibas, 2004). The advantage of WSN can be seen in the following scenario; a motorist is planning to take a highway which is monitored by WSN, using the data supplied by WSN, the motorist checks the traffic flow along the highway prior to the journey. The information from WSN shows that an accident is causing traffic congestion along the highway. Knowing this the motorist can now opt for a different route for a smoother journey. The motorist in this situation received the information much faster using WSN than any typical information channels.

Traffic monitoring as described above is one of the many applications of WSN. In general the applications of WSN can be divided into military, civilian and health care applications (Wu, 2006). Among the military use of WSN are targeting systems and battlefield surveillance. For the civilian application, WSN can be applied in environmental monitoring, building monitoring and control, wildlife monitoring, security, smart agriculture systems and many other applications. In health care, WSN can be used to monitor the vital signs of patients with chronic diseases. It is also very useful in elderly care, where other than monitoring their vital signs the sensors can also be used to track down the location of the elders.

However, these wireless sensors have several constraints such as restricted sensing and communication range as well as limited battery capacity (Ghosh & Das, 2006). The limitations cause issues such as coverage, connectivity, network lifetime, scheduling and data aggregation. The wireless sensor nodes are normally battery powered, therefore due to the limited battery capacity energy conservation measures are important to prolong the WSN lifetime. Coverage problem is caused by limited sensing range. To solve the problem the solution depends on how the sensors are positioned with respect to each other. Central to the coverage problem is the question on how to guarantee that each of the points in the region of interest (ROI) is covered. Therefore, to maximize coverage the sensors need to be placed not too close to each other so that the sensing capability of the network is fully utilised; however they must not be located too far from each other to avoid coverage holes (area outside sensing range of sensors).

For sensors with actuation capability, the initial coverage can be improved by moving them so that a better arrangement is achieved (Chellapan et al.,2007; Howard & Poduri, 2005; Kwok, Driessen, Phillips, & Tovey, 1997; Wang, Cao, & Porta, 2004; Wu & Yang, 2005; Zhao et al., 2004). Even though mobility is an advantage to WSN, it is a high energy consuming task (Dantu et al., 2005). Hence, movement planning is an important issue in WSN (Wang, Irwin, Berman, Fu, & Porta, 2005). It is desired to achieve optimal coverage and at the same time not to relax the mobility energy consumption issue due to the fact that sensors have a limited energy supply.

This research is interested in solving the coverage and energy conservation issues of mobile wireless sensors using PSO. The concept of PSO is introduced. Existing works on mobile WSN coverage optimization and energy conservation are reviewed. The two phase PSO algorithm proposed is discussed, followed by results and discussion. Finally is the conclusion of the work.

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