Cluster head selection based on Minimum Connected Dominating Set and Bi-Partite inspired methodology for energy conservation in WSNs

Priyadarshini, Rashmi; Sivakumar, N.

doi:10.1016/j.jksuci.2018.08.009

Cited by 34 publications

(18 citation statements)

References 23 publications

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“…In this experimental setup, the initial energy of the nodes is considered as 0.5 J as per previous studies 21–25 . Moreover, the total number of sensor nodes is 1000, in which the number of sensor nodes varies from 100 in increments of 100 during the process of experimentation.…”

Section: Simulation Results and Investigationmentioning

confidence: 99%

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

Arulmurugan

Amuthan

2021

Int J Communication

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Summary The clustering process in wireless sensor networks is considered to be predominant for facilitating minimized energy consumptions through the selection of potential cluster heads that are efficient in energy and trust. The significant cluster head selection is deterministic in topology control with the view to manage the sensor member nodes of the cluster with the view to enhance energy efficiency in the network. In this paper, a Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism (MMBPP‐CHSM) is proposed by modeling the success probability of sensor nodes' state into a Bernoulli process that dynamically evolves over time based on Markov chain for extending the lifetime of the network. This proposed MMBPP‐CHSM scheme is vital in quantifying the reliability and availability of the sensor nodes in a dynamically evolving wireless sensor network through the estimation of trust and energy in order to facilitate effective cluster head selection. The simulation results of the proposed MMBPP‐CHSM scheme confirmed a superior enhancement in percentage of alive nodes and death nodes in the network to a maximum level of 28% and 23% on par with the baseline cluster head selection approaches used for investigation.

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Section: Simulation Results and Investigationmentioning

confidence: 99%

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

Arulmurugan

Amuthan

2021

Int J Communication

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“…In 2018, R. Raj Priyadarshini and N. Sivakumar [6], simulated in the AVL tree rotation clustering method, which considers the cluster sensor node to achieve load balance. By exploiting the enhanced Kmeans clustering method a solitary LAN was separated into several clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Wolf Pack and Particle Swarm Optimization Algorithm for Multihop Routing Protocol in WSN

Wang¹

2020

JNACS

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In the WSNs environment, the methodological development ensued in the process of gathering and forwarding the enormous data among the nodes that is the most important challenges in WSNs as it is related with high energy loss and delay. This ensued in establishment of a routing protocol for the optimally chosen of multipath to development of a routing in WSNs. Hence, this work presents an energy-effectual routing in WSNs utilizing the hybrid Wolf Pack approach with Particle Swarm Optimization (WP-PSO) approach that selects the optimal hops in succeeding the routing. Initially, the CHs are chosen by exploiting the LEACH protocol which reduces the traffic in the network. In the multihop routing, the CHs are engaged, and the selection of the optimal paths is based on the developed hybrid optimization that chooses optimal hops on basis of the energy constraints, namely energy, inter-intracluster distance, link lifetime, delay, and distance. The experimentation outcomes show that developed routing protocol obtained maximum energy, minimum delay, the maximum number of the alive nodes and average throughput.

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“…In-network On-node (data prediction) (data statistical) Indeed, dividing the network into clusters is not an easy task and it faces many challenges. Hence, one can find a lot of works in the literature that are interested in issues related to cluster network like the selection of cluster heads [36][37][38], optimization of cluster size [39,40], communication between sensors/CHs and CHs/sink [41,42], etc. However, our concern in this paper is to study the variation of data collected by the sensors and not the formation of clusters themselves.…”

Section: Data Recoverymentioning

confidence: 99%

An energy-efficient data prediction and processing approach for the internet of things and sensing based applications

Harb

Jaoude

Makhoul

2019

Peer-to-Peer Netw. Appl.

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The Internet of Things (IoT) is a vision in which billions of smart objects are linked together. In the IoT, "things" are expected to become active and enabled to interact and communicate among themselves and with the environment by exchanging data and information sensed about the environment. In this future interconnected world, multiple sensors join the internet dynamically and use it to exchange information all over the world in semantically interoperable ways. Therefore, huge amounts of data are generated and transmitted over the network. Thus, these applications require massive storage, huge computation power to enable real-time processing, and high-speed network. In this paper, we propose a data prediction and processing approach aiming to reduce the size of data collected and transmitted over the network while guaranteeing data integrity. This approach is dedicated to devices/sensors with low energy and computing resources. Our proposed technique is composed of two stages: on-node prediction model and in-network aggregation algorithm. The first stage uses the Lagrange interpolation polynomial model to reduce the amount of data generated by sensor nodes while, the second stage uses a statistical test, i.e. Kolmogorov-Smirnov, and aims to reduce the redundancy between data generated by neighbouring nodes. Simulation on real sensed data reveals that the proposed approach significantly reduces the amount of data generated and transmitted over the network thus, conserving sensors' energies and extending the network lifetime.

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Cluster head selection based on Minimum Connected Dominating Set and Bi-Partite inspired methodology for energy conservation in WSNs

Cited by 34 publications

References 23 publications

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

Hybrid Wolf Pack and Particle Swarm Optimization Algorithm for Multihop Routing Protocol in WSN

An energy-efficient data prediction and processing approach for the internet of things and sensing based applications

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