Enhanced energy optimized cluster based on demand routing protocol for wireless sensor networks

Sreevidya, B.; Rajesh, M.

doi:10.1109/icacci.2017.8126141

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…Thus, the EAgg energy aggregated for both transmission and reception is given by, EAgg = M bits (TE) + K bits (RE) (8) If EAgg≥ T than the cluster master ship is rotated.…”

Section: Figure 1 Hierarchy Of Grey Wolvesmentioning

confidence: 99%

“…The sensor node for example sensing the humidity, temperature for weather, manages a sensing work. Moreover, these sensor nodes can be left without watching as these nodes does the task of sensing, it sends it to the base station [8]. As sensing nodes are small, they are deployed all over the region to monitor the characteristic changes occurs in the particular area.…”

Section: Introductionmentioning

confidence: 99%

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Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

Pratha

Asanambigai

Mugunthan³

2021

Preprint

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Wireless Sensor Networks (WSN) is the fundamental technology for the Internet of Things (IoT). It is a network formed from several sensor nodes to sense the changes in the environment. The nodes are battery powered that performs sensing and transmission of information to other nodes in the network. Thus, the energy of the sensor node plays a crucial role in WSN. Thus, intelligent models are anticipated to solve the network problems by optimizing or minimizing the mechanism inorder to improve the energy efficiency. In this paper, a combined meta-heuristic approach called Grey Wolf Optimization based Game theoretical Approach (GWOGA) is proposed that helps for clustering to find the best solutions for selection of aggregation points and this optimal selection of aggregation points lead the nodes to maximize its battery/lifetime. Experimental and simulation analysis shows that the GWOGA outperforms the existing models and retains the lifetime of the network.

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“…Thus, the EAgg energy aggregated for both transmission and reception is given by, EAgg = M bits (TE) + K bits (RE) (8) If EAgg≥ T than the cluster master ship is rotated.…”

Section: Figure 1 Hierarchy Of Grey Wolvesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

Pratha

Asanambigai

Mugunthan³

2021

Preprint

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“…This proposed protocol improves energy efficiency in wireless sensor networks as well as data transmission . It is always good to use routing protocol which is cluster based [4]. LEACH-SM protocol is introduced by Bilal Abu Bakr which suggested the concept of the spare nodes in which nodes are chosen in parallel to decide whether it should be passive spare node or active node .…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing Coverage with Efficiency in WSN using Energy Efficient Algorithms

Dongaonkar*,

Srinivasu,

Adsul

2019

IJITEE

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Current years Wireless Sensor networks is a leading area in research due to its number of applications in massive areas .The major concern for Wireless sensor networks is an issue of power of batteries for sensor nodes , several algorithms were transcribed to solve this problem but in so many cases only energy efficiency is considered , coverage problem with energy efficiency is not addressed in most of the cases. To give a better solution for energy efficiency and coverage in this paper modified LEACH protocol is proposed with genetic algorithm, which gives comparatively better results than EBRP protocol

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“…If there is a power gap around the BS station, the network is disconnected and BS loses the connection to the remote gate. This increases the need of load balancing management [11][12][13][14][15][16][17][18] to avoid the power consumption problem and increase network life. To address these problems, it is necessary to present the circuit diagrams in this article to clusters and redirect custom-enabled nodes.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Load Balancing and Routing Using Multi-Objective Based Algorithm in WSN

Vijayvergia¹,

Modani²

2023

Intelligent Automation &Amp; Soft Computing

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In wireless sensor network (WSN), the gateways which are placed far away from the base station (BS) forward the collected data to the BS through the gateways which are nearer to the BS. This leads to more energy consumption because the gateways nearer to the BS manages heavy traffic load. So, to overcome this issue, loads around the gateways are to be balanced by presenting energy efficient clustering approach. Besides, to enhance the lifetime of the network, optimal routing path is to be established between the source node and BS. For energy efficient load balancing and routing, multi objective based beetle swarm optimization (BSO) algorithm is presented in this paper. Using this algorithm, optimal clustering and routing are performed depend on the objective functions routing fitness and clustering fitness. This approach leads to decrease the power consumption. Simulation results show that the performance of the proposed BSO based clustering and routing scheme attains better results than that of the existing algorithms in terms of energy consumption, delivery ratio, throughput and network lifetime. Namely, the proposed scheme increases throughput to 72% and network lifetime to 37% as well as it reduces delay to 37% than the existing optimization algorithms based clustering and routing schemes.

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Enhanced energy optimized cluster based on demand routing protocol for wireless sensor networks

Cited by 18 publications

References 9 publications

Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

Increasing Coverage with Efficiency in WSN using Energy Efficient Algorithms

Energy Efficient Load Balancing and Routing Using Multi-Objective Based Algorithm in WSN

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