• Rachit Manchanda

Wireless Sensor Networks (WSN) has a limited amount of resources. By reducing the quantity of data transmitted, clustering and data aggregations are utilized to lower network energy usage. Swarm intelligence, reinforcement learning, and neural networks are examples of machine learning methods that decrease data transmission and make advantage of the network's distributive properties. It presents a comparison of the performance of various algorithms in order to assist designers in developing acceptable machine learning-based clustering and data aggregation solutions. This work includes a review of multiple machine learning-based clustering and data aggregation approaches utilized in WSN, as well as a proposal for enhanced similarity-based clustering and data aggregation using Independent Component Analysis (ICA).

Internet of things; Wireless Sensor Networks; Clustering; Machine learning; Data aggregation

"The Role of IoT and WSN in Smart Cities: A Review of Recent Developments and Future Prospects", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.i514-i518, June-2024, Available :http://www.jetir.org/papers/JETIR2406855.pdf

"The Role of IoT and WSN in Smart Cities: A Review of Recent Developments and Future Prospects", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppi514-i518, June-2024, Available at : http://www.jetir.org/papers/JETIR2406855.pdf