Random, PSO & MDBPSO based Sensor Deployment in WSN

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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Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v10.i3.pp1287-1295 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1287

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 ◽

Packet Size ◽

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 & 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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Grid & Force Based Sensor Deployment Methods in WSN using PSO

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v10.i1.pp271-279 ◽

2018 ◽

Vol 10 (1) ◽

pp. 271

Author(s):

Aparna Pradeep Laturkar ◽

Sridharan Bhavani ◽

DeepaliParag Adhyapak

Keyword(s):

Probabilistic Approach ◽

Optimization Method ◽

Sensor Deployment ◽

Discrete Version ◽

Binary Particle Swarm Optimization ◽

Packet Size ◽

Local Optima ◽

Wide Range ◽

Battery Capacity ◽

Coverage Problems

<span>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. </span><span>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.</span>

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GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing

Discrete Dynamics in Nature and Society ◽

10.1155/2017/2013673 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Jianhua Qu ◽

Xiyu Liu ◽

Minghe Sun ◽

Feng Qi

Keyword(s):

Particle Swarm Optimization ◽

Large Scale ◽

Graph Drawing ◽

Optimization Problems ◽

Particle Swarm ◽

Optimization Methods ◽

Population Based ◽

Pictorial Representation ◽

Swarm Optimization ◽

Wide Range

Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.

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