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

Liao, Zhuofan; Wang, Jianxin; Zhang, Shigeng; Cao, Jiannong; Min, Geyong

doi:10.1109/tpds.2014.2333011

Cited by 175 publications

(124 citation statements)

References 37 publications

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“…Besides, it also completes the effective coverage of the whole monitoring area by adjusting the adjustable parameter. Satisfying the presupposition of the network connection, Liao et al 18 complete the coverage of goal nodes by taking advantages of the moving characteristic of the sensor nodes. Thus, it proposes the heuristic algorithm, taking the Thiessen polygon as the active area to reduce the sensor nodes' energy consumption to balance the network energy, proposing the sensor network coverage.…”

Section: Methodsmentioning

confidence: 99%

k-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks

Sun

Xing

et al. 2017

International Journal of Distributed Sensor Networks

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In the process of the wireless sensor network research, the issue on the energy consumption and coverage is an essential and critical one. According to the characteristic of the sensor nodes, it is homogeneous, and we proposed the kdegree coverage algorithm based on optimization nodes deployment. First, the algorithm gives the solving procedure of the maximum seamless coverage ratio, when the three-node joint coverage has been provided. Second, when the sensor nodes are covering the monitoring area, the algorithm gives the solving procedure of the expected coverage quality and the judgment methods of the coverage ratio, when the nodes are compared with the nearby ones. And when there is redundancy coverage in the given monitoring area, we have given the solving procedure of any sensor nodes that exist in the redundant nodes coverage. Finally, using the simulation experiment, the results of the coverage algorithm based on optimization nodes deployment are compared with other algorithms in terms of the coverage quality and the network lifetime, the performance indexes have enhanced to 13.36% and 12.92% on average. Thus, the effectiveness and viability of the coverage algorithm based on optimization nodes deployment have been proved.

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Section: Methodsmentioning

confidence: 99%

k-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks

Sun

Xing

et al. 2017

International Journal of Distributed Sensor Networks

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“…However, in 2014, Liao et al [9] have found a small number of works on minimizing the sensor mobility. Hence, they started investigating the mobile sensor deployment (MSD) problem to obtain the minimal movement and the maximum network connectivity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Steiner minimum tree method is constructed to solve the network connectivity problem. In 2014, Temel et al [5] studied works related to deterministic sensor deployment [9], and found out that the implementation of that method in 3D environment has not yet been accomplished. So, they proposed a deterministic sensor deployment method, which is based on wavelet transform (WT) to maximize the quality of coverage of a WSN with a minimum number of sensors on a 3D surface.…”

Section: Literature Reviewmentioning

confidence: 99%

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Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence

Jagtap

Gomathi²

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This paper addresses the major challenges that reside on target coverage problem, which is one among the two primary sub-problems of node deployment problem. In order to accomplish a cost-efficient target coverage, a Voronoi partition-based, velocity added artificial bee colony algorithm (V-VABC) is introduced. The V-VABC is an advancement over the traditional, target-based Voronoi greedy algorithm (TVgreedy). Moreover, the VABC component of V-VABC is a hybrid, heuristic search algorithm developed from the context of ABC and particle swarm optimization (PSO). The V-VABC is an attempt to solve the network, which has an equal number of both sensors and targets, which is a special case of TCOV. Simulation results show that V-VABC performs better than TV-greedy and the classical and base algorithms of V-VABC such as ABC and PSO.Key words: sensor, target, Voronoi, heuristic, ABC, PSO, VABC. Minimizing sensor movement in target coverage problem:A hybrid approach using Voronoi partition and swarm intelligence A.M. JAGTAP * and N. GOMATHI VEL-TECH Dr. RR & Dr. SR Technical University, Chennai 600 062, India to be complex in large networks. Based on immune algorithm, a new centralized algorithm is used to maximize the coverage area with less energy utilization. So, the centralized algorithm has an advantage over the very low processing power of the sensor nodes [10].Sensor deployment is the basic problem in WSNs. It is divided into two kinds of methods: based on continued points and based on grid [11]. The system cost must be low and the connected system must cover the sensing field within WSNs. So, the minimum-cost and connectivity-guaranteed grid coverage (MCGC) occurs as an optimization problem [12]. This paper addresses the challenges in the first problem of the node deployment problem, termed target coverage (TCOV) problem [5]. In order to overcome the problem, a meta-heuristic search-based node deployment algorithm is proposed. The algorithm moves the sensors to cover the target at minimum movement distance, based on Voronoi diagram and heuristic search. The heuristic search is performed by adopting a hybrid version of artificial bee colony (ABC) and particle swarm optimization (PSO). Hence, the key contributions of this paper can be presented as follows.We addressed the scenario of searching for servers (nodes) in the higher-order neighbour targets and the failure of the existing algorithm to handle it.1. We propose a Voronoi partition-based velocity added ABC, abbreviated as V-VABC. 2. We hybridize the ABC with PSO to introduce velocity added ABC (VABC). 3. We have simulated the scenario of higher order neighborhood searching for servers (nodes) and the superiority of V-VABC over TV-greedy algorithm. The rest of the paper is organized as follows. Section 2 reviews the related works and Section 3 gives the preliminaries of the system model and the objective of the TCOV solution. Section 4 explains the TV-greedy algorithm and the proposed V-VABC algorithm, and Section 5 details the hybrid version of

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“…This dissertation topic mainly focuses on minimization of sensors' movement to provide target coverage and network connectivity. For this, the Mobile Sensor Deployment (MSD) problem is defined to deploy mobile sensors to provide target coverage and network connectivity at minimum movement cost [1]. The formal definition of the MSD problem is as below.…”

Section: Proposed Systemmentioning

confidence: 99%

Enhancement to TV-Greedy Algorithm to Minimize Sensor Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Bhandurge¹

2017

IJRASET

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I. INTRODUCTIONWireless Sensor Network (WSNs) is a new emerging application of ad-hoc networks which provide high monitoring quality for massive geographical areas with relatively inexpensive equipment. Wireless Sensor Networks (WSNs) are currently used in a wide range of applications including environmental monitoring and object tracking to improve system performance, provide convenient and efficient method and can also fulfil functional requirement. Ensuring coverage and connectivity is a fundamental objective of wireless sensor network. The coverage of sensor network represents the quality of supervision that the network can provide, that is how well the region of interest is being monitored by sensors, and how effectively a sensor network can detect intruders [2]. In many applications, sensors are not mobile and remain stationary after their initial deployment. The coverage of such a stationary sensor network is determined by the initial network configuration. Once the deployment strategy and sensing properties of the sensors are known, then the network coverage can be calculated and remains unchanged over time. Similarly connectivity is also necessary for sensor in WSN to collect data and report data to the sink node. The main problem in connectivity is that sensor cannot directly communicate with the base station because its communication range is very limited. A network is said to be fully connected if every node is able to communicate with other either directly or through some intermediate relay node. Thus the network formed by the sensors should be connected so that the information collected by each sensor can be transmitted to the base station through the network rather than that of the direct link. In order to solve above problems, in recent years, mobility of the sensor has been exploited to improve the coverage quality and connectivity in randomly deployed WSNs by relocating some mobile sensors to new positions to enhance the coverage quality and the connectivity of the network.

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

Cited by 175 publications

References 37 publications

k-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks

k-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks

Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence

Enhancement to TV-Greedy Algorithm to Minimize Sensor Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

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