Differential Evolution and Mobile Sink Based On-Demand Clustering Protocol for Wireless Sensor Network

Ghosh, Nimisha; Prasad, T. Rajendra; Banerjee, Indrajit

doi:10.1007/s11277-019-06657-8

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…The proposed protocol PACMSD is compared with the MERAM-R [30], ACO-based protocol [31] and DE-based protocol [32]. The detail of these three protocols is introduced in Sect.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

2022

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With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. Artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

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“…The proposed protocol PACMSD is compared with the MERAM-R [30], ACO-based protocol [31] and DE-based protocol [32]. The detail of these three protocols is introduced in Sect.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Ghosh et al [32] proposed differential evolution (DE) and mobile sink based on demand clustering protocol. This protocol uses an on-demand based clustering, which reduces the energy consumption in the cluster formation phase.…”

Section: Mobile Bs-based Cluster Routingmentioning

confidence: 99%

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

2022

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“…We compared the AADE algorithm with the standard genetic algorithm (GA) [38][39][40], the standard DE algorithm (DE) [41][42][43] and the adaptive DE algorithm (ADE) [44][45][46] in optimizing the technical indicators. All four algorithms were used to solve the optimization model.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

Overall Dynamic Optimization of Metal Mine Technical Indicators Considering Spatial Distribution of Ore Grade Using AADE

2022

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In optimizing the production of a metal mine, either the overall dynamic relations between technical indicators or the spatial distribution of the ore grade are usually considered, but few studies have considered both factors together. These two factors in combination have a greater effect on the optimization of mine production in terms of economic benefit and resource utilization than they do individually. We proposed an overall dynamic optimization model of technical indicators of metal mine production that considers the spatial distribution of the ore grade to better optimize the technical indicators and improve sustainable development of mineral resources. We incorporated an adaptive mutation strategy and adaptive control parameters into a differential evolution algorithm (AADE) in order to overcome the drawbacks of the differential evolution algorithm in solving this optimization model. The adaptive mutation strategy and adaptive control parameters were used to increase the rate of convergence and improve the search for a global maximum. To assess the performance of AADE, we used a real case and four test functions (the Sphere, Griewank, Rastrigin and Rosenbrock functions) in tests that compared AADE with a standard genetic algorithm, a standard differential evolution algorithm and the recently developed adaptive differential evolution algorithm. The results indicate that the optimization model we created is better aligned with mine production processes than current optimization models. In optimizing the technical indicators of metal mine production to maximize economic benefits, AADE performed significantly better than the other three algorithms tested in terms of convergence rate and global search ability. INDEX TERMSMetal mine, technical indicator, overall dynamic relation, ore grade distribution, AADE.

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DHGRP: Dynamic Hexagonal Grid Routing Protocol with Mobile Sink for Congestion Control in Wireless Sensor Networks

Christopher

Jasper

2020

Wireless Pers Commun

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Differential Evolution and Mobile Sink Based On-Demand Clustering Protocol for Wireless Sensor Network

Cited by 4 publications

References 33 publications

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

Overall Dynamic Optimization of Metal Mine Technical Indicators Considering Spatial Distribution of Ore Grade Using AADE

DHGRP: Dynamic Hexagonal Grid Routing Protocol with Mobile Sink for Congestion Control in Wireless Sensor Networks

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