An Effective Cluster Head Selection Algorithm using Machine Learning in IoNT

Clustering is the first technique that comes to mind in order to achieve efficient routing in sensor networks. Instead of transmitting the packets to each other, selecting a cluster head (CH) among the nodes is the best way to collect the packets from the cluster members which leads to saving energy, reducing network traffic, preventing packet loss and prolonging network lifetime. Cluster head selection (CHS) is a challenging process in a network therefore, CHS should be efficient and effective in Wireless Nano-Sensor Networks (WNSNs) due to the nano-domain characteristics. In this paper, an effective CHS algorithm using Machine Learning (ML) is proposed for Wireless Nano-Sensor Networks (WNSNs) and Internet of Nano-Things (IoNT) applications. The proposed algorithm (PA) is compared with an ordinary cluster head selection (OCHS) algorithm. According to the simulation results, PA provides nano-sensor node coverage on the network by 89.235% while it covers 20.355% more nano-nodes and spends 1.29 minutes less compared to OCHS on average.