Optimized Cluster Head Selection and Routing in WBANS Using Hybrid Improved Particle Swarm and Adaptive Cuckoo Search Algorithm

Wireless Body Sensor Networks (WBASN) or Wireless Body Sensor Networks (WBAN) have gained significant importance in medical applications and are now crucial for patient monitoring. Among other processes, routing is still considered a resource-intensive activity. Hence, it is imperative to develop a routing system for WBAN that optimizes energy use. Current routing algorithms prioritize energy savings over security. Nevertheless, security assaults result in heightened power consumption and diminish the overall network performance. WBAN, among others, employs node clustering as a strategy to address this problem. In order to accomplish the desired selection of CH (Cluster Head) for IoT devices, we take into account optimization aspects such as distance, latency, and power consumption. This work presents a sophisticated approach for selecting and routing CH (cluster head) using a combination of hybrid improved particle swarm optimization (IPSO) and adaptive cuckoo search optimization (ACSO). Using the ACS algorithm, the study applies energy-efficient opportunistic routing to control the network lifespan. This approach removes the data aggregation delay of the smart wearable patch and effectively prevents routing loops. Next, we evaluate the system's efficiency in terms of network lifetime, latency, error indicators, energy efficiency, and network performance. We carry out this study effort using the MATLAB software. We perform the simulations under diverse circumstances. In terms of energy use and network longevity, the suggested method is better than the most recent cutting-edge methods, such as Low Energy Adaptive Clustering Hierarchy (LEACH), PSO, Ant Lion Optimization (ALO), and hybrid IPSO-ACSO.