Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things

Abstract

Wireless Nano-Sensor Networks (WNSNs) are molecular-level networks consisting of nano-machines that have very limited energy capacity. Due to the high energy consumption of the nodes in WNSNs during the data transmission, energy-efficient routing algorithms may help to reduce overall energy consumption. On the other hand, metaheuristics are useful for solving problems such as routing, energy, security and connectivity for traditional sensor networks. Therefore, this study proposes a hybrid Firefly and BAT Optimization based energy-efficient multi-hop routing algorithm, namely nanoFFBAT, between nano-sensor nodes and randomly placed convenient nano-routers for WNSNs-supported Internet of Nano-Things (IoNT) applications. In this hybrid approach, Firefly optimization is used for selecting the most energy-efficient neighbor by a nano-sensor node based on the flashing behavior of fireflies on the way for forming multi-hop paths and the BAT algorithm is adapted for building energy-efficient routing path discovery in WNSNs and also finding the optimal solution in solution space. In addition, nanoFFBAT detects redundant nodes on the energy-efficient multi-hop routing paths and shortens these paths if there exists any neighbor node already has been selected. The results of nanoFFBAT are compared with the shortest path from a nano-node to the convenient nano-router, a genetic algorithm-based energy-efficient routing algorithm for sensor networks and a WNSN routing protocol in the literature. According to the experimental simulation results, nanoFFBAT saves 306.929 nJ on a path on average and prolongs network lifetime 16.298 times more on average compared to the mentioned algorithms.