Static energy efficient clustering scheme for heterogeneous wireless sensor networks (S EECS)

Sharma, Akshay; Goel, Kunal; Bindal, Anuradha; Bindal, Amit Kumar

doi:10.1109/ic3.2016.7880193

Cited by 2 publications

(3 citation statements)

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“…Thus selected, the overall CH manages the data transmission between all the clusters and BS. Sharma et al [58] proposed a static clustering scheme accommodating five levels of energy heterogeneity in the network. In their work [58], the authors have provisioned energy-harvested solar-enabled sensor nodes to act as cluster heads in the network.…”

Section: Related Workmentioning

confidence: 99%

“…Sharma et al [58] proposed a static clustering scheme accommodating five levels of energy heterogeneity in the network. In their work [58], the authors have provisioned energy-harvested solar-enabled sensor nodes to act as cluster heads in the network. [58] proposes two different modes of communication depending upon the nodes' distance to the base station.…”

Section: Related Workmentioning

confidence: 99%

“…In their work [58], the authors have provisioned energy-harvested solar-enabled sensor nodes to act as cluster heads in the network. [58] proposes two different modes of communication depending upon the nodes' distance to the base station. If it goes beyond a certain threshold, nodes communicate their data via the traditional clustering approach; however, if it is below the threshold, nodes can communicate their data directly to the BS.…”

Section: Related Workmentioning

confidence: 99%

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An Energy-Efficient Hybrid Clustering Technique (EEHCT) for IoT-Based Multilevel Heterogeneous Wireless Sensor Networks

et al. 2023

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Internet-of-Things (IoT)-based Heterogeneous Wireless Sensor Network (HWSN) has emerged as a prevalent technology that plays a significant role in developing various human-centric applications. Like in a wireless sensor network (WSN), energy is also the most crucial resource in IoTbased HWSN. The researchers have proposed many works to achieve energy-efficient network operations by minimizing energy usage. A vast proportion of these works emphasize using the clustering approach, which has proved its worth to a great extent. However, most schemes require the repeated formation of clusters incurring a significant amount of nodes' energy in the clustering process. The protocol design of such schemes also varies with the changing levels of heterogeneity. In this work, a hybrid clustering scheme-An Energy-Efficient Hybrid Clustering Technique (EEHCT) has been proposed for IoT-based HWSN that minimizes the energy consumption in clusters' formation and distributes the network load evenly irrespective of the heterogeneity level to prolong network lifetime. It appropriately utilizes dynamic and static clustering strategies to formulate the load-balanced clusters in the network. EEHCT establishes its supremacy over state-of-the-art schemes via an extensive set of simulations and experimentation in terms of multiple network performance metrics like stability, throughput, and network lifetime. Like, it achieves a gain up to 90.27% with respect to network lifetime over its peers in the standard operating conditions and under varying network configurations. In addition to quantitative analysis, a statistical analysis has also been provided to demonstrate the formation of energy-balanced clusters through the proposed scheme.

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Section: Related Workmentioning

confidence: 99%