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

2018 ◽  
Vol 10 (3) ◽  
pp. 1278 ◽  
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.

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

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.

scholarly journals Random, PSO & MDBPSO based Sensor Deployment in WSN

2018 ◽  
Vol 10 (1) ◽  
pp. 286
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Region Of Interest ◽  
Optimization Methods ◽  
Sensor Deployment ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity ◽  
Coverage Problems

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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