Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Qadri, Muhammad Yasir; Qadri, Nadia N.; McDonald-Maier, Klaus

doi:10.1016/j.micpro.2015.08.001

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…CHs are selected on the basis of node weighted probability [20]. Fuzzy logic based energy and dynamic clustering with Ant Colony Optimization (ACO) metaheuristic was based on Multi path Routing Protocol (MRP) [21,22]. CH is designated based on the remaining nodes energy and an enhanced ACO algorithm is implemented to hunt multiple paths which exist between CH and BS.…”

Section: Related Workmentioning

confidence: 99%

Efficient Energy Clustering Protocol UsingGenetic Algorithm in Wireless Sensor Networks

Srikanth¹,

Prasad²

2018

JESTR

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Nowadays, Wireless Sensor Networks (WSNs) are pondered as an exploration subject. Currently, progress in electronic communications has directed to multipurpose Sensor Nodes (SNs) with less price and power consumption. Energy efficiency is a major concern in WSNs as the sensor nodes are battery-operated devices. Clustering based techniques are implemented through data aggregation to make equal energy consumption among SNs for energy efficient data transmission. The existing clustering techniques make use of distinct Harmony Search Algorithm (HSA), Low-Energy Adaptive Clustering Hierarchy (LEACH) and Particle Swarm Optimization (PSO) algorithms. However, these algorithms have exploration exploitation trade-off and local search constraint individually. In order to obtain a global search with faster convergence, Efficient Energy Clustering Protocol (EECP) based on Genetic Algorithm (GA) is recently proposed to detect their immediate neighbors, balance energy consumption load among data transmission routes and energy efficient cluster head selection. The proposed algorithm exhibited high search efficiency and dynamic capability that improves lifetime of SNs. The presentation of the proposed algorithm was assessed using throughput, packet delivery ratio, energy consumption and end to end delay. The proposed algorithm showed an improvement in energy consumption and throughput by 95 and 90 Mbps respectively than existing clustering algorithm.

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Section: Related Workmentioning

confidence: 99%