Optimized lowest ID in wireless sensor network using Invasive Weed Optimization (IWO)-genetic algorithm (GA)

Narendran, M.; Periasamy, P.

doi:10.1109/icammaet.2017.8186714

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…The perfect selection of cluster head and routing has been ignored which enhances the network lifetime. M narendran [14] applied a TABU search technique on Invasive weed optimization numerical problem which belongs to continuous and provides a simple mechanism for evolutionary mechanism. The limitations related to local optima is solved by TABU search mechanism.…”

Section: Related Workmentioning

confidence: 99%

Development of QoS optimized routing using Artificial bee colony and TABU-GA with a mobile base station in Wireless Sensor Network

Varsha¹,

Bala²,

Kumar³

et al. 2019

IJITEE

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Routing in Wireless Sensor Networks (WSNs) have been playing an important and progressive role in different scenarios like the environment and traffic monitoring. In this research paper, extensive contributions have been explored concerning the routing mechanisms involving WSN. Enhancing the critical attributes like network lifetime and reliability are prime areas of research in this field. To obtain the desired results, optimizations have been performed using Swarm Intelligence (SI) based routing mechanism like the Artificial-Bee Colony (ABC) optimization and TABU-GA routing technique. Although, since the past decade, the Ant-Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been introduced and applied in this field, but the ABC and TABU-GA approach is relatively novel and considered a prominent choice as compared to other methods. However, a lot of scope for further enhancement of the ABC optimization and TABU-GA problem. This paper highlights and explains the comparison between RZLEACH (Rendezvous LEACH), ABC and TABU-GA algorithm based routing mechanisms. The simulation results obtained show that the TABU-GA have significant result over the ABC and RZLEACH and found 6% improvement when compared with the RZLEACH approach in terms of Remaining Energy and 9% lesser in case of First node dead (FND) with comparison to RZLEACH

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Section: Related Workmentioning

confidence: 99%

Development of QoS optimized routing using Artificial bee colony and TABU-GA with a mobile base station in Wireless Sensor Network

Varsha¹,

Bala²,

Kumar³

et al. 2019

IJITEE

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“…To ensure the connectivity and stability of cluster heads, currently several studies on cluster head selection algorithms in FANETs have been conducted. In [5], the authors adopt a minimum ID algorithm to select the cluster head with the smallest UAV cluster ID number. The highest node degree clustering algorithm (HDD) is proposed in [6] by calculating the number of neighbours around a node.…”

Section: Introductionmentioning

confidence: 99%

A stability improved weighted cluster head selection algorithm for mission-oriented flying ad hoc networks

Hao

Zhu

Tian

et al. 2023

International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2023)

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Hierarchical cluster architecture is usually adopted in large scale unmanned aerial vehicle (UAV) network. Due to mission scheduling scheme, the assigned mission determines the number of UAVs in the cluster. Aiming at the problem of rapid changes of cluster head caused by high mobility in cluster, we propose a stability improved weighted cluster head selection algorithm (SI-WCSA) for mission-oriented flying ad hoc networks based on speed similarity, energy, distance and node degree. Firstly, it improves the mobility metric of speed similarity, which can accurately reflect relative movement trend. Then the scheme proposes three energy consumption rates and introduces weight threshold indicator for the maintenance of cluster head. Finally we adopt combination weight coefficient calculation methodology based on analytic hierarchy process and the entropy weight method. Simulation results indicate that the algorithm can significantly reduce the frequency of cluster head replacement and balance energy consumption within clusters compared to the benchmark approaches.

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Improved design of low energy clustering algorithm for mobile sensor network

Liu

Zhang

Shi

2021

Computer Communications

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Optimized lowest ID in wireless sensor network using Invasive Weed Optimization (IWO)-genetic algorithm (GA)

Cited by 6 publications

References 7 publications

Development of QoS optimized routing using Artificial bee colony and TABU-GA with a mobile base station in Wireless Sensor Network

Development of QoS optimized routing using Artificial bee colony and TABU-GA with a mobile base station in Wireless Sensor Network

A stability improved weighted cluster head selection algorithm for mission-oriented flying ad hoc networks

Improved design of low energy clustering algorithm for mobile sensor network

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