Distributed Deployment Algorithms for Efficient Coverage in a Network of Mobile Sensors With Nonidentical Sensing Capabilities

IEEE Trans. Netw. Sci. Eng.

2020

In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, in reality, due to a limited budget or numerous targets, mobile sensors may be not enough to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (MWTCSCLMS) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coveragebased algorithm (WMCBA), is proposed for the subproblem of the MWTCSCLMS problem. Based on the WMCBA, the Steinertree-based algorithm (STBA) is proposed for the MWTCSCLMS problem. Simulation results demonstrate that the STBA provides better performance than the other methods.

Section: Related Workmentioning

confidence: 99%

On New Approaches of Maximum Weighted Target Coverage and Sensor Connectivity: Hardness and Approximation

Nguyen

Liu

IEEE Trans. Netw. Sci. Eng.

2020

“…Many researches have been conducted on coverage control issue in WSNs. The authors in [7] studied efficient node deployment algorithms in mobile WSNs to improve the network coverage rate. Based on the existing coverage holes in the network, their proposed algorithm locates the suitable position for each mobile sensor.…”

Section: Related Workmentioning

confidence: 99%

“…With the aim to fully cover the whole network, mobile sensor nodes are utilized in WSNs with static sensor nodes. For mobile sensor nodes, they can move to the coverage hole to fix it [7]. In recent research, swarm intelligence algorithm has become more and more popular and can be utilized to find the moving position for mobile sensor nodes [9].…”

Section: Introductionmentioning

confidence: 99%

A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs

Kim

et al. 2017

Cluster Comput

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs.

“…As a result, the area coverage ratio is improved. Further, a multiplicatives weighted Voronoi diagram is used to discover the coverage holes corresponding to different sensors with different sensing ranges [18]. However, Voronoi diagrams in these studies are constructed according to the position of mobile sensors, and thus need to be recomputed after each round of sensor movement.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Liao

IEEE Trans. Parallel Distrib. Syst.

Zhang

et al. 2015

175

114

Abstract-Coverage of interest points and network connectivity are two main challenging and practically important issues of Wireless Sensor Networks (WSNs). Although many studies have exploited the mobility of sensors to improve the quality of coverage and connectivity, little attention has been paid to the minimization of sensors' movement, which often consumes the majority of the limited energy of sensors and thus shortens the network lifetime significantly. To fill in this gap, this paper addresses the challenges of the Mobile Sensor Deployment (MSD) problem and investigates how to deploy mobile sensors with minimum movement to form a WSN that provides both target coverage and network connectivity. To this end, the MSD problem is decomposed into two sub-problems: the Target COVerage (TCOV) problem and the Network CONnectivity (NCON) problem. We then solve TCOV and NCON one by one and combine their solutions to address the MSD problem. The NP-hardness of TCOV is proved. For a special case of TCOV where targets disperse from each other farther than double of the coverage radius, an exact algorithm based on the Hungarian method is proposed to find the optimal solution. For general cases of TCOV, two heuristic algorithms, i.e., the Basic algorithm based on clique partition and the TV-Greedy algorithm based on Voronoi partition of the deployment region, are proposed to reduce the total movement distance of sensors. For NCON, an efficient solution based on the Steiner minimum tree with constrained edge length is proposed. The combination of the solutions to TCOV and NCON, as demonstrated by extensive simulation experiments, offers a promising solution to the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.