Optimization of future location prediction in mobile sensor network using Particle Swarm Optimization
Rupam Some, Subhojit Malik · 2024
In modern times, developing infrastructures, such as surveillance network, structural health monitoring, smart grid, environmental or habitat monitoring, depends on wireless sensor network (WSN)-based applications to ascertain the exact positioning of targets, laying the groundwork for subsequent sophisticated operations like automation and control. Hence, it is evident that there is a clear demand for effective location prediction techniques within WSNs. It is a common exercise in surveillance application to drop sensor nodes form airborne devices which may be applicable where the environment is hostile [ 1 ]. Contemporary applications especially surveillance-centric applications may be benefitted using unmanned aerial vehicle (UAV). Systems for independent deployment through UAVs are currently available [ 2 ]. Contemporary UAVs are equipped with advanced control and perception systems, endowing them with the capability to execute coordinated deployment missions [ 3 ]. Upon deployment of the sensor nodes embedded with UAVs in the target area, it is very much desirable to track the optimal target location of the same in order to deliver the acquired data to an aggregator node for final delivery. Especially, for a surveillance network where such numerous UAVs are positioned computing the optimal location is a challenging task for maintaining accurate delivery of data [ 4 , 5 ]. Prediction of location of the application node (AN) module curtails the practice of transmission of data in terms of determining the future locality of the AN thus minimizing the cost of transmission. Numerous research activities were carried out on the aforesaid area using different means, but a mere number of these approaches concentrates on location prediction as well as optimization of the location [ 4 , 6 ].