Hybrid GSTEB routing protocol using clustering and artificial bee colony optimization

Kaur, Sandeep; Gangwar, Rakesh Chandra

doi:10.1109/icgciot.2015.7380546

Cited by 1 publication

(2 citation statements)

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“…GAs are utilized to model data transmission through the various alternate route. A general self-organized tree-based energy balance (GSTEB) routing protocol is presented in [31], in which an ABC is utilized to investigate the shortest path between source and sink, based on clustering. The proposed algorithm works in four phases: the initial phase, the tree construction phase, the self-organized data collection and transmission phase, and the information exchange phase.…”

Section: Related Workmentioning

confidence: 99%

“…To validate the proposed PSO-EELB technique, some existing approaches (DESA [20], ACOLBR [24], GACCTR [30], and GSTEB [31]) that also perform energy-based load-balancing based on other metaheuristic methods, as discussed in Section 2, are selected. These techniques have been implemented in the above environment and the results are compared in terms of different parameters: energy consumption, throughput, convergence rate, number of data packets received, execution time, network lifetime, and number of active nodes to prove the effectiveness of the proposed technique.…”

Section: Validation Of the Proposed Techniquementioning

confidence: 99%

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An intelligent PSO-based energy efficient load balancing multipath technique in wireless sensor networks

Randhawa¹,

Jain²

2017

Turk J Elec Eng & Comp Sci

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Abstract:To provide a reliable and efficient service, load balancing plays an important role in wireless sensor networks (WSNs). There is a need to maximize the network lifetime for WSNs applications with periodic generation of data. Due to the relationship between energy consumption and network sensor node lifetime, energy consumption in a network should be minimized and balanced in order to increase network lifetime. Energy-efficient load-balancing techniques are needed to solve this problem. In this paper, a particle swarm optimization (PSO)-based energy-efficient load-balancing technique is proposed, in which the required number of routing paths and energy consumption of different nodes and paths are calculated. Based on maximum residual energy, paths are selected and further PSO-based load balancing is performed among all the paths for data transfer at a particular point of time. The performance of the proposed technique is evaluated using real testbed and experimental results and shows that the proposed technique performs better than existing techniques in terms of network lifetime, energy consumption, throughput, number of alive nodes, number of data packets received, execution time, and convergence rate.

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Section: Related Workmentioning

confidence: 99%