Optimizing distance vector-hop localization in wireless sensor networks using the grasshopper optimization algorithm

Abstract

In scenarios involving mobile sensors within distributed sensor systems, such as those often encountered in wireless sensor networks (WSNs) or the internet of things (IoT), the ability to ascertain the origin of sensor data holds significant importance. Range-free Monte Carlo Localization methods offer an energy-efficient solution that eliminates the need for extra hardware, as they solely rely on the radio hardware already present on sensor nodes. But there are certain disadvantages when implemented, as it occupies more amount of power and some inaccuracies might happen in accessing the data from the sensor node. In this paper, we suggest the grasshopper optimization algorithm (GOA) strategy, which incorporates the distance-vector hop (DVHop) and three-anchor methods. It displays its usefulness in terms of both overall localization accuracy and resistance to hostile attacks or malfunctioning nodes. Nonetheless, the incorporation of dead reckoning based on motion sensor data significantly enhances the precision of location estimates and bolsters the network's robustness against both faulty components and malicious agents.