Wireless sensor networks (WSN) consists of a huge number of nodes that are positioned randomly to obtain information regarding the environment and communicate with each other. On detection of an event, obtaining information regarding the geographical location of the sensor is beneficial in most applications. Range-free and range-based localization schemes are the major categories of localization algorithms available. Range-free localization algorithms utilize the connectivity information to provide a cost efficient localization solution. On the other hand, range-based localization schemes use radio signal strength and distance from anchor nodes for estimating the unknown node location. Several swarm intelligence algorithms are used for reducing the noise while optimizing localization and distance estimation while using these schemes. In this paper, we propose an enhanced swarm intelligence scheme that provides better performance when compared to the existing algorithms in terms of noise level, signal strength, number of anchors, number of nodes, radio signal strength and localization error. Surrogate based optimization (SBO), firefly algorithm (FA), butterfly optimization algorithm (BOA), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are compared with the proposed scheme.
Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks
