Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN

Dattatraya, Kale Navnath; Rao, K. Raghava

doi:10.1016/j.jksuci.2019.04.003

Cited by 109 publications

(65 citation statements)

References 35 publications

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“…It employs an energy conservator in order to increase the longevity of the network. Dattatraya et al [19] proposed a hybrid algorithm by combining glom worm swarm optimization (GSO) and fruit fly optimization (FFOA). GSO suffers from low computational speed and low searching capacity.…”

Section: Meta-heuristic Approachesmentioning

confidence: 99%

“…The experimentation of the proposed algorithm is tested in the different scenarios of sensor nodes by varying the number of sensor nodes and the CHs. To analyze the efficacy of the proposed work is compared with the other algorithms namely end-to-end secure low energy adaptive clustering hierarchy (E-LEACH) [14], genetic algorithms (GA) [15,16], cuckoo search (CS) [17], particle swarm [18], and fruit fly optimization algorithm (FFOA) [19]. Our contributions in this paper are described as follows: − Proposed cluster head selection using grey wolf optimization with energy efficient parameters.…”

Section: Introductionmentioning

confidence: 99%

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An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

et al. 2020

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Clustering is considered as one of the most prominent solutions to preserve the energy in the wireless sensor networks. However, for optimal clustering, an energy efficient cluster head selection is quite important. Improper selection of cluster heads (CHs) consumes high energy compared to other sensor nodes due to the transmission of data packets between the cluster members and the sink node. Thereby, it reduces the network lifetime and performance of the network. In order to overcome the issues, we propose a novel cluster head selection approach using grey wolf optimization algorithm (GWO) namely GWO-CH which considers the residual energy, intra-cluster and sink distance. In addition to that, we formulated an objective function and weight parameters for an efficient cluster head selection and cluster formation. The proposed algorithm is tested in different wireless sensor network scenarios by varying the number of sensor nodes and cluster heads. The observed results convey that the proposed algorithm outperforms in terms of achieving better network performance compare to other algorithms.

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Section: Meta-heuristic Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

et al. 2020

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“…An integrated Glowworm swarm and Fruitfly algorithm-based CH scheme was proposed for maximum improvement of network lifetime. 27 This CH scheme was proposed for sustaining the energy balance in the network with the objective to prevent earlier death of the sensor nodes. This clustering scheme was proposed for preventing frequent selection of CHs that introduces energy overhead in the network.…”

Section: Related Workmentioning

confidence: 99%

An availability predictive trust factor‐based semi‐Markov mechanism for effective cluster head selection in wireless sensor networks

Amuthan

Arulmurugan

2019

Int J Communication

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Summary The longevity of the sensor networks purely depends on the effectiveness of a cluster head selection process that attributes towards effective network management in wireless sensor networks. However, the majority of the cluster head selection schemes are considered an unrealistic condition which ponders the sensor nodes that have the possibility of being selected as cluster heads are highly energy competitive and trustworthy. In this paper, an availability predictive trust factor‐based semi‐Markov mechanism (APTFSMM) was proposed for facilitating effective cluster head selection with the view to enhance its degree of longevity degree in wireless sensor networks. This proposed APTFSMM inherited the merits of semi‐Markov process for estimating availability predictive trust factor that quantifies the maximum likelihood probability under which it is selected as the cluster head through maximized exploration of multiple transition states of the sensors in the networks. This proposed APTFSMM is determined to be predominant in enhancing the lifetime of the sensor network by 31% with a significantly reduced energy consumption rate of 38% compared with the benchmarked cluster head selection approaches considered for investigation.

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“…In Ref. [17], the authors proposed a novel method for the maximization of the network lifetime. Vinitha et al [18] proposed a multi-hop routing technique that is energy efficient.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

Das¹,

Das²,

Mohapatra³

2020

Karbala International Journal of Modern Science

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An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the combination of prediction and forecasting methods in the cloud system, which results in less communication and more battery life for the sensor. In our model, we used the ANN model initially, which had predicted the temperature for a given day with an accuracy of 92%. The results of ANN, together with the earlier real temperatures, are given as input to the ARIMA forecasting model, which provides an accuracy of 96% for one day in advance. Our simulation shows that the proposed method saves more energy compared to the traditional approach.

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Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN

Cited by 109 publications

References 35 publications

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

An availability predictive trust factor‐based semi‐Markov mechanism for effective cluster head selection in wireless sensor networks

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

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