ESO-LEACH: PSO based energy efficient clustering in LEACH

Nigam, Gaurav Kumar; Dabas, Chetna

doi:10.1016/j.jksuci.2018.08.002

Cited by 61 publications

(41 citation statements)

References 15 publications

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“…Cui et al [32] proposed a bat algorithm (BA) [33,34] combined with centroid strategy to optimize the LEACH protocol. In [35], the concept of advanced nodes and an enhanced cluster head election rule set are used to optimize the LEACH protocol, and the meta-heuristic particle swarm enhancement algorithm is used to initially cluster the sensor nodes. Cai [36] proposes an improved FTBA algorithm for fusion curve strategy, which improves local and global search ability to select cluster heads to extend the lifetime of the network.…”

Section: Sink Nodementioning

confidence: 99%

A many-objective optimization WSN energy balance model

2020

KSII TIIS

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Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

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Section: Sink Nodementioning

confidence: 99%

A many-objective optimization WSN energy balance model

2020

KSII TIIS

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“…In [28] the authors have proposed a meta-heuristic particle swarm optimisation (PSO) clustering algorithm. In ESO-LEACH if the average energy of cluster is more than the remaining energy of CH then, the CH will be replaced by the node which has minimum fitness function value.…”

Section: Soft-computing Based Energy-swarm-optimisation Leach (Eso-lementioning

confidence: 99%

“…Then, according to the distance of the node from the BS, the fitness function is formed. Finally, the average energy of cluster is compared against the remaining energy of CH, if it is higher then the current CH will be replaced by the node which has minimum fitness function value [28]. Here, the authors compared the performance of ESO-LEACH against LEACH.…”

Section: Soft-computing Based Energy-swarm-optimisation Leach (Eso-lementioning

confidence: 99%

Fault tolerance in WSN through PE‐LEACH protocol

Mohapatra

Rath

2019

IET wirel. sens. syst.

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The fault is inevitable in case of the wireless sensor network (WSN) because of its remote deployment and constrained architecture. In literature, many algorithms have been proposed to address several types of fault in the WSN lifecycle. In the context of fault tolerance, the clustering method has been adopted as a proven technique. Heintzelman et al. first considered the clustering technique and proposed the seminal low energy adaptive clustering hierarchy (LEACH) algorithm for fault handling in WSN. In this study, the authors propose a LEACH variant clustering convention called partitioned-based energy-efficient-LEACH (PE-LEACH) protocol which tends to the energy-based fault-tolerant technique. They also study and present a taxonomy on LEACH variants. The execution of PE-LEACH is analysed against its predecessors; hard-computing based LEACH, energy-efficient LEACH (E-LEACH) convention and soft-computing-based energy-swarm-optimisation LEACH (ESO-LEACH). They have found through their recreation that PE-LEACH outperforms than LEACH and E-LEACH separately whereas, for ESO-LEACH the PE-LEACH is a tough competitor.

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“…A popular cluster-based approach is the low energy adaptive clustering hierarchy (LEACH). 7 LEACH has inspired other LEACH-based protocols that have attempted to improve the method of cluster head selection, such as EM-LEACH, 8 ESO-LEACH, 9 and LEACH-VA. 10 The fundamental goal of LEACH is to equally distribute energy consumption among the sensors in the network that utilize the randomized rotation of cluster heads. In data transmission, LEACH incorporates data fusion into the routing protocol to reduce the amount of information before transmitting data to the base station.…”

Section: Introductionmentioning

confidence: 99%

Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks

Pattamaset

Choi

2020

International Journal of Distributed Sensor Networks

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For the successful operation of smart home environments, it is important to know the state or activity of an occupant. A large number of sensors can be deployed and embedded in places or things. All sensor nodes measure the physical world and send data to the base station for processing. However, the processing of all collected data from every sensor node can consume significant energy and time. In order to enhance the sensor network in smart home applications, we propose the irrelevant data elimination based on k-means clustering algorithm to enhance data aggregation. This approach embeds the cluster head–based algorithm into cluster heads to omit irrelevant data from the base station. The pattern of measured data in each room can be clustered as an active pattern when human activity happens in that room and a stable pattern when human activity does not happen in the room. The irrelevant data elimination based on k-means clustering algorithm approach can reduce 55.94% of the original data with similar results in human activity classification. This study proves that the proposed approach can eliminate meaningless data and intelligently aggregate data by delivering only data from rooms in which human activity likely occurs.

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ESO-LEACH: PSO based energy efficient clustering in LEACH

Cited by 61 publications

References 15 publications

A many-objective optimization WSN energy balance model

A many-objective optimization WSN energy balance model

Fault tolerance in WSN through PE‐LEACH protocol

Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks

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