Optimized Fuzzy Logic Based Energy-Efficient Geographical Data Routing in Internet of Things

The Internet of Things (IoT) is being the key strategic enabler for realizing the vision of smart cities by allowing everyday objects to be connected through wireless sensor networks (WSNs). In large-scale WSNs, scalability, versatility, path performance, mobility support, and lower routing protocol overhead are all desirable characteristics. Given that GPS devices extract device locations approximately, geographic-oriented multicast routing techniques were selected because to their lower overhead. However, it is discovered that the current geographic-oriented routing models have several drawbacks. The sensor nodes’ uneven energy consumption and high routing overhead have a significant impact on the lifespan and efficiency of the network. The aim of this work provides an energy-efficient geographic (EEG) routing protocol based on the given 6-fold-objective function. The best routes are chosen during EEG routing by fuzzy logic that has been optimized for membership functions. Here, the best route selection takes into account QoS, trust, energy, distance, delay, and overhead. In this work, Harris Hawk’s Optimization (HHO) is utilized for optimization purposes. Furthermore, in the case of group 2, the mean performance of the proposed work is 25.6 %, 6.3%, 2.19%, 25.6%, 12.85%, and 10.64% better than that of CSO, EHO, MFO, WOA, DA, and SLnO, respectively. Lastly, the suggested work’s performance is evaluated using several metrics in comparison to other traditional methods.