Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network

Verma, Sandeep; Sood, Neetu; Sharma, Ajay K.

doi:10.1016/j.asoc.2019.105788

Cited by 166 publications

(124 citation statements)

References 41 publications

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“…This enhances the network lifetime and minimizes the packet loss. A genetic algorithm‐based optimized clustering (GAOC) protocol is proposed for heterogeneous WSN . Multiple data sinks are used.…”

Section: Related Workmentioning

confidence: 99%

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

Agrawal

Qureshi

Pincha

et al. 2020

Int J Communication

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Summary A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.

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Section: Related Workmentioning

confidence: 99%

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

Agrawal

Qureshi

Pincha

et al. 2020

Int J Communication

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“…However, it provided less reliability compared to other protocols. Verma et al [ 46 ] proposed two novel algorithms, namely the genetic algorithm-based optimized clustering (GAOC) protocol and data sink-based GAOC (MS-GAOC), for electing the CH node in WSN. It addressed the hotspot problem in the network.…”

Section: Related Workmentioning

confidence: 99%

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Luhach

et al. 2020

Sensors

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Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination. The selection of the cluster head (CH) plays a crucial role in gathering and aggregating the data from the cluster members and forwarding the data to the sink node. The inefficient CH selection causes packet failures during the data transfer and early battery depletion nearer to the sink. This paper proposes a cluster tree-based routing protocol (CT-RPL) to increase the life span of the network and avoid the data traffic among the network nodes. The CT-RPL involves three processes, namely cluster formation, cluster head selection, and route establishment. The cluster is formed based on the Euclidean distance. The CH selection is accomplished using a game theoretic approach. Finally, the route is established using the metrics residual energy ratio (RER), queue utilization (QU), and expected transmission count (ETX). The simulation is carried out by using a COOJA simulator. The efficiency of a CT-RPL is compared with the Routing Protocol for Low Power and Lossy Networks (RPL) and energy-efficient heterogeneous ring clustering routing (E2HRC-RPL), which reduces the traffic load and decreases the packet loss ratio. Thus, the CT-RPL enhances the lifetime of the network by 30–40% and the packet delivery ratio by 5–10%.

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“…• In a similar idea of research done by [1], [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], we also used random techniques in the cluster formation phase, but we added the remaining energy constraint to the CH selection.…”

Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

“…For a decade, LEACH has become prevalent, and many of the previous works have demonstrated another approach in cluster formation by introducing the remaining energy parameters, RSSI, etc. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] also in data gathering [56][57][58].…”

Section: Introductionmentioning

confidence: 99%

A New Bio-Inspired for Cooperative Data Transmission of IoT

et al. 2020

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The original queen honey bee migration (QHBM) was developed for independent action on solving efficient mobile routing in WSN. In this paper, we enhanced the original QHBM using Binary testing injection on the cooperative node's selection on IoT system. We also added a new cost function for making nodes' coalition, implementing the threshold value for modified QHBM (mQHBM for short), and demonstrating the mQHBM-CMIMO in fair comparison with another previous algorithms. Our research portrayed that mQHBM can perform better than its competitors such as Fuzzy-BT, Neuro Fuzzy and PSO in terms of network lifetime and the end to end delay.

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Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network

Cited by 166 publications

References 41 publications

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

A New Bio-Inspired for Cooperative Data Transmission of IoT

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