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A Multi-Objective Metaheuristic Approach Based Adaptive Clustering and Path Selection in IoT Enabled Wireless Sensor Networks


Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Chhattisgarh, India
 

The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.

Keywords

IoT Enabled WSN, Multi-Objective Optimization, Clustering, Quality-of-Service, Rider Optimization, Sailfish Optimization
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  • A Multi-Objective Metaheuristic Approach Based Adaptive Clustering and Path Selection in IoT Enabled Wireless Sensor Networks

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Authors

Pallavi Joshi
Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Chhattisgarh, India
Ajay Singh Raghuvanshi
Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Chhattisgarh, India

Abstract


The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.

Keywords


IoT Enabled WSN, Multi-Objective Optimization, Clustering, Quality-of-Service, Rider Optimization, Sailfish Optimization

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F209988