Pattern Mutation in Wireless Sensor Deployment

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid & Force Based sensor deployment algorithms based on interval and packet size. From the results of Combination of Grid & Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.

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Random, PSO and MDBPSO based Sensor Deployment in Wireless Sensor Network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v10.i3.pp1278-1286 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1278 ◽

Cited By ~ 1

Author(s):

Aparna Pradeep Laturkar ◽

Sridharan Bhavani ◽

DeepaliParag Adhyapak

Keyword(s):

Particle Swarm Optimization ◽

Wireless Sensor Network ◽

Sensor Network ◽

Particle Swarm ◽

Wireless Sensor ◽

Sensor Deployment ◽

Binary Particle Swarm Optimization ◽

Swarm Optimization ◽

Wide Range ◽

Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deployment using Random; Particle Swarm Optimization (PSO) and grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization) methods. This paper analyzes the performance of Random, PSO based and MDBPSO based sensor deployment methods by varying different grid sizes and the region of interest (ROI). PSO and MDBPSO based sensor deployment methods are analyzed based on number of iterations. From the simulation results; it can be concluded that MDBPSO performs better than other two methods.

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An Intelligent Sensor Placement Method to Reach a High Coverage in Wireless Sensor Networks

Applications and Developments in Grid, Cloud, and High Performance Computing ◽

10.4018/978-1-4666-2065-0.ch011 ◽

2013 ◽

pp. 168-183

Author(s):

Shirin Khezri ◽

Karim Faez ◽

Amjad Osmani

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Sensor Placement ◽

New Method ◽

Wireless Sensor ◽

Sensor Deployment ◽

Distributed Sensor Networks ◽

Distributed Sensor ◽

Grid Points ◽

Adequate Coverage

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Optimizing the sensor deployment provides sufficient sensor coverage and saves cost of sensors for locating in grid points. This article applies the modified binary particle swarm optimization algorithm for solving the sensor placement in distributed sensor networks. PSO is an inherent continuous algorithm, and the discrete PSO is proposed to be adapted to discrete binary space. In the distributed sensor networks, the sensor placement is an NP-complete problem for arbitrary sensor fields. One of the most important issues in the research fields, the proposed algorithms will solve this problem by considering two factors: the complete coverage and the minimum costs. The proposed method on sensors surrounding is examined in different area. The results not only confirm the successes of using the new method in sensor placement, also they show that the new method is more efficiently compared to other methods like Simulated Annealing(SA), PBIL and LAEDA.

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