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Energy Efficient Clustering and Optimized LOADng Protocol for IoT

Divya Sharma1,*, Sanjay Jain2, Vivek Maik3

1 Department of Electronics and Communication, New Horizon College of Engineering, Bangalore, 560103, India
2 CMR Institute of Technology, Bangalore, 560037, India
3 Department of Electronics and Communication, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

* Corresponding Author: Divya Sharma. Email: email

Intelligent Automation & Soft Computing 2022, 34(1), 357-370. https://doi.org/10.32604/iasc.2022.025637

Abstract

In recent years, the use of Internet of Things (IoT) devices has increased exponentially due to the advancement of information and communication technologies. Wireless sensor networks (WSNs) are vital in the development of IoT and include low-cost smart devices for data collection. However, such smart devices hold some restrictions in terms of calculation, processing, storage, and energy resources. With such constraints, one of the primary difficulties for the WSN is to achieve the lowest possible energy consumption across the network. This article aims to develop an Energy-Efficient cluster-based Lightweight On-Demand Ad hoc Distance Vector Routing Protocol–Next Generation (LOADng) routing protocol in IoT to maximize network life by reducing the power consumed by each sensor. The proposed approach consists of three stages: cluster formation followed by appropriate selection of cluster head (CH) and finally routing. In this approach, initially, clusters are created using the k-means clustering algorithm. Then CHs are optimally selected using a seagull optimization algorithm (SOA). After the CH selection process, routing is done using the optimal LOADng protocol. The efficiency of the proposed approach is evaluated using different assessment metrics. Also efficiency of this approach is compared with existing algorithms.

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Cite This Article

D. Sharma, S. Jain and V. Maik, "Energy efficient clustering and optimized loadng protocol for iot," Intelligent Automation & Soft Computing, vol. 34, no.1, pp. 357–370, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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