IHGWO-Based Optimization of IoT Wireless Sensor Networks

The Internet of Things (IoT) is a rapidly expanding field, with billions of devices now online. This expansion has increased demand for efficient and dependable wireless sensor connectivity. A new method for reducing wireless sensor connectivity time in IoT systems using the Improved Hybrid Grey Wolf Optimization (IHGWO) strategy is proposed. Grey Wolf Optimization (GWO) is a population-based metaheuristic algorithm based on wolf hunting behaviour. It has been demonstrated to be effective in solving a wide range of optimization problems, including wireless sensor network issues. We have used IHGWO in this paper to optimize the placement and connectivity time of wireless sensors in an IoT system. Our method is evaluated in three scenarios: (1) without optimization, (2) with IHGWO and 5G critical and hybrid additions, and (3) with IHGWO and optimized sensor positions determined by the defined algorithm. These results demonstrate the potential of IHGWO as a method for reducing the amount of time wireless sensors require to connect in Internet of Things (IoT) systems. Using IHGWO, we were able to reduce wireless sensor connectivity time significantly.