Intelligent Routing for IoT-Driven MANETs Using Computational Intelligence

The interlinkage of the Internet of Things (IoT) with Mobile Ad Hoc Networks (MANETs) creates an autonomous communication ecosystem, imperative for applications ranging from disaster recovery to smart agriculture. On the other hand, this coalescing gives rise to crucial challenges, especially in energy efficiency, higher transmission rates, and network connectivity. As the devices in IoT-driven MANETs have restricted battery life, the optimum use of energy is vital for extending network lifespan. The global explosion of IoT devices demands rapid data transfer and constant connectivity. Moreover, frequent topological changes brought on by the high mobility devices in IoT-driven MANETs elevate the chances of link failure. These disruptive actions undermine network productivity resulting in reduced throughput and higher latency. Computational intelligence approaches offer credible solutions for resolving these concerns. This study presents a Unified Energy-aware Link Auto-configuration Routing Model (UELARM) for IoT-driven MANETs that favors energy-aware paths and the auto-configuration of broken links. UELARM exploits the flower pollination algorithm by incorporating three vital parameters such as energy cost, average community time, and packet loss to explore the optimal route. Additionally, for automatically configuring broken links in IoT-driven MANETs, UELARM presents a route repair approach based on mobility prediction, drawing inspiration from the termite colony optimization algorithm. The simulation results indicate that the UELARM outpaces other state-of-the-art routing algorithms regarding energy consumption, throughput, delay and network lifetime.