Hybrid Bird Swarm Optimized Quasi Affine Algorithm Based Node Location in Wireless Sensor Networks

Wireless sensor networks (WSN) with the Internet of Things (IoT) play a vital key concept while performing the information transmission process. The WSN with IoT has been effectively utilized in different research contents such as network protocol selection, topology control, node deployment, location technology and network security, etc. Among that, node location is one of the crucial problems that need to be resolved to improve communication. The node location is directly influencing the network performance, lifetime and data sense. Therefore, this paper introduces the Bird Swarm Optimized Quasi-Affine Evolutionary Algorithm (BSOQAEA) to fix the node location problem in sensor networks. The proposed algorithm analyzes the node location, and incorporates the dynamic shrinking space process is to save time. The introduced evolutionary algorithm optimizes the node centroid location performed according to the received signal strength indications (RSSI). The created efficiency in the system is determined using high node location accuracy, minimum distance error, and location error.