FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks

2020 ◽  
Vol 163 ◽  
pp. 12-34
Author(s):  
Aparajita Chowdhury ◽  
Debashis De
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Wireless Sensor ◽  
Mobile Wireless ◽  
Swarm Optimization ◽  
Inference System ◽  
Glowworm Swarm Optimization ◽  
Mobile Wireless Sensor

scholarly journals An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2586
Author(s):  
Nguyen Thi My Binh ◽  
Abdelhamid Mellouk ◽  
Huynh Thi Thanh Binh ◽  
Le Vu Loi ◽  
Dang Lam San ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Mobile Wireless ◽  
Swarm Optimization ◽  
Mobile Wireless Sensor Networks ◽  
Coverage Quality ◽  
Mobile Wireless Sensor

Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms.

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