Contention-Free Station Communication Matching Algorithm in Multi-Hop Power Management for Wireless Ad-Hoc Networks

Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

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Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

Maddikunta

¹

,

Madda

²

2017

International Journal of Grid and High Performance Computing

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Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

show abstract

Contention-Free Station Communication Matching Algorithm in Multi-Hop Power Management for Wireless Ad-Hoc Networks

Cited by 3 publications

References 12 publications

Energy-Aware Location Privacy Routing for Wireless Sensor Networks

Energy-Aware Location Privacy Routing for Wireless Sensor Networks

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

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