An Extended Virtual Force-Based Approach to Distributed Self-Deployment in Mobile Sensor Networks

Li, Jun; Zhang, Baihai; Cui, Lingguo; Chai, Senchun

doi:10.1155/2012/417307

Cited by 46 publications

(35 citation statements)

References 23 publications

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“…A wide variety of meta-heuristic methods have been applied to the placement problem, ranging from the genetic algorithm (GA) [23], evolution algorithm with specialized operators [24], particle swarm optimization algorithm (PSO) [25], simulated annealing algorithm (SA) [12][13][14][15], virtual force algorithm (VF) [26], and the virtual force oriented particles algorithm [27]. Other algorithms are analyzed in [16] like the artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), and PSO for the sensor deployment problem with the target coverage.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

et al. 2017

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Abstract:Wireless sensor network (WSN) applications are rapidly growing and are widely used in various disciplines. Deployment is one of the key issues to be solved in WSNs, since the sensor nodes' positioning affects highly the system performance. An optimal WSN deployment should maximize the collection of the desired interest phenomena, guarantee the required coverage and connectivity, extend the network lifetime, and minimize the network cost in terms of energy consumption. Most of the research effort in this area aims to solve the deployment issue, without minimizing the network cost by reducing unnecessary working nodes in the network. In this paper, we propose a deployment approach based on the gradient method and the Simulated Annealing algorithm to solve the sensor deployment problem with the minimum number of sensor nodes. The proposed algorithm is able to heuristically optimize the number of sensors and their positions in order to achieve the desired application requirements.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

et al. 2017

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“…For instance, the extended virtual forces-based approach proposed in (24) copes with two drawbacks of the virtual forces algorithm: the connectivity maintenance and nodes stacking problems (i.e. two or more sensor nodes occupy the same position).…”

Section: Forces-based Strategymentioning

confidence: 99%

Survey of deployment algorithms in wireless sensor networks: coverage and connectivity issues and challenges

Khoufi

Minet

Laouiti

et al. 2017

IJAACS

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Wireless Sensor Networks (WSNs) have many fields of application, including industrial, environmental, military, health and home domains. Monitoring a given zone is one of the main goals of this technology. This consists in deploying sensor nodes in order to detect any event occurring in the zone of interest considered and report this event to the sink. The monitoring task can vary depending on the application domain concerned. In the industrial domain, the fast and easy deployment of wireless sensor nodes allows a better monitoring of the area of interest in temporary worksites. This deployment must be able to cope with obstacles and be energy efficient in order to maximize the network lifetime. If the deployment is made after a disaster, it will operate in an unfriendly environment that is discovered dynamically. We present a survey that focuses on two major issues in WSNs: coverage and connectivity. We motivate our study by giving different use cases corresponding to different coverage, connectivity, latency and robustness requirements of the applications considered. We present a general and detailed analysis of deployment problems, while highlighting the impacting factors, the common assumptions and models adopted in the literature, as well as performance criteria for evaluation purposes. Different deployment algorithms for area, barrier, and points of interest are studied and classified according to their characteristics and properties. Several recapitulative tables illustrate and summarize our study. The designer in charge of setting up such a network will find some useful recommendations, as well as some pitfalls to avoid. Before concluding, we look at current trends and discuss some open issues.

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“…For each Edge in polygon (10) Find midpoint of Edge (11) Append midpoint to new polygon (12) End For each Edge (13) Until vertices are very close to each other (14) Record the statistical average of new candidate location into newpositions (15) End For each polygon (16) Checkforbettercoverage (VD, positions, newpositions) // Function for if NCL > CL (17) Newcov = calculatecoverage (VD, newpositions) (18) Newcovpercent = (newcov/MFL 2 ) * 100 (19) IF coverage enhanced then (20) IF Round ≤ MR (21) calcEnergy (positions, newposition, permeterEnergy) (22) position = newpositions (23) totalcov = newtotalcov (24) totalcovpercent = newtotalcovpercent (25) else (26) exit Loop (27) End IF Round (28) Else (29) Exit Loop (30) End IF coverage (31) Next Round (32) calculatecoverage average for all rounds (33) calculateEnergy consumption average for all rounds (34) calculateconvergence average for all rounds Algorithm 3: Edge Based Centroid (EBC) of th Voronoi polygon ( ).…”

Section: Convergence Ratementioning

confidence: 99%

Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks

Aliyu

Abdullah

Chizari

et al. 2016

International Journal of Distributed Sensor Networks

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Sensor nodes in wireless sensor networks are deployed to observe the surroundings for some phenomenon of interest. The fundamental issue in observing such environments is the area coverage which reflects how well the region is monitored. The nonuniform sensor nodes distribution in a certain region caused by random deployment might lead to coverage holes/gaps in the network. One of the solutions to improve area coverage after initial deployment is by sensor nodes mobility. However, the main challenge in this approach is how to increase area coverage with the least energy consumption. This research work aims to improve area coverage with minimal energy consumption and faster convergence rate. The Edge Based Centroid (EBC) algorithm is presented to improve the area coverage with faster convergence rate in a distributed network. The simulation based performance evaluations of the proposed algorithms are carried out in terms of area coverage, convergence rate, and energy efficiency. Compared to the existing works, EBC improved area coverage with faster convergence. It is concluded that the proposed algorithm has improved area coverage with faster convergence and minimal energy consumption.

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An Extended Virtual Force-Based Approach to Distributed Self-Deployment in Mobile Sensor Networks

Cited by 46 publications

References 23 publications

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

Survey of deployment algorithms in wireless sensor networks: coverage and connectivity issues and challenges

Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks

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