Impact of Clustering Algorithms and Energy Harvesting Scheme on IoT/WSN Infrastructures

• Main Authors: Chaudhary, Ajay, Peddoju, Sateesh K.
• Format: Conference Proceeding
• Language: English
• Subjects: clustering; Clustering algorithms; Energy dissipation; Energy Harvesting; Hidden Markov models; Internet of Things; IoT; Machine Learning; Merging; Simulation; Wireless communication; Wireless sensor networks; WSN
• ISSN: 2155-6814
• DOI: 10.1109/MASS52906.2021.00086

With the rapid increase in the adaptability of Internet of Things (IoT) based solutions worldwide, there is a dire need to implement these solutions effectively and efficiently. One step towards such implementation is to run Wireless Sensor Networks (WSN) or IoT devices on minimal energy with an uninterrupted power supply to maintain the system unattended. In a way, a self-sustainable solution is needed to meet the long-term energy requirements of the system. A rational design of IoT/WSN nodes is an essential criterion for the system to be energy efficient. In this paper, we create an IoT/WSN simulation environment in MATLAB and integrate it with clustering algorithms and energy harvesting schemes to improve the life expectancy of the nodes. The simulation environment includes an infrastructure with 100 IoT/WSN nodes to test the impact of several clustering algorithms like K-Means, K-Medoids, and Fuzzy C-Means on Wireless Energy Harvesting (WEH) schemes. The simulation results show that the clustering algorithms balance the energy dissipation from the nodes in a WSN/IoT network. The Energy Harvesting (EH) provides extra energy to cater to the IoT/WSN operation for an extended period. As a result, when the EH scheme is implemented with a clustering algorithm, there is a significant improvement in node life expectancy over the simulation period. The results show that we can effectively extend the network lifetime by merging these two approaches, leading to a long uninterrupted operation of the deployed infrastructure.