Red Deer Algorithm based nano-sensor node clustering for IoNT

Abstract

Node connectivity is a crucial issue for collecting and transmitting the values of the medium at nano-scale in Wireless Nano-Sensor Networks (WNSNs). This provides the network to be online when the sensor nodes maintain connectivity among themselves. Therefore, efficient connectivity methods need to be developed for permanent connection on the network. The basic way to provide connectivity over the network is clustering -based solutions which build efficient routing paths between the nodes. On the other hand, meta-heuristics are prominent methods to be preferred in order to select special nodes as cluster heads. In this paper, a modern meta-heuristic method, the Red Deer Algorithm (RDA) based distributed nano-sensor node clustering algorithm, namely nanoRDA, is proposed in order to provide efficient communication between nano-sensor nodes by selecting cluster heads on WNSNs in Internet of Nano-Things (IoNT) applications. nanoRDA is compared with two different Genetic Algorithm (GA) setups. According to the results, nanoRDA covers all non-cluster head nodes up to 99.38% on average all over the nano-scale networks while selecting fewer cluster heads than GA-based solutions.