Localization of Nodes in Wireless Sensor Networks using a Combination of Krill Herd Algorithm with Ant Colony Optimization

The industrial revolution and the spread of electronic technologies and wireless communications have led to the production of small smart sensors with low consumption and low-cost benefits. Sensor nodes work as autonomous systems with low cost, small size, and wireless communication media but work with low resources. The most significant item in the operation of Wireless Sensor Networks (WSNs) is finding the spatial information of objects, including retrieval and identification of events, routing according to geometric position, monitoring and tracking. Localization in WSNs divides into two range-based and range-free categories. In this paper, to overcome the weaknesses of DV-Hop, a hybrid model based on the Krill Herd Algorithm and Ant Colony Optimization called KHAACO was proposed for locating unknown nodes. The aim of this study is to provide an approach for estimating the location of sensor nodes with minimal error and using KHAACO to estimate the location of unknown nodes and using the motion characteristics of other krill, foraging, and spatial dispersion of the KHA and optimizing it with ACO. The evaluation of the hybrid model in the MATLAB environment has been done based on error criteria and energy consumption. The results showed that the hybrid model compared to DV-Hop, DV-Hop-ACO, and DV-Hop-PSO reduced the Localization error. The value of reduction of localization error for 90 anchor nodes and 450 sensor nodes was equal to 9.95%.