VirtuRoute: Virtual Reality Enhanced Path Planning for Energy-Aware Mobile Sinks in IoT-Enabled Wireless Network
Surendra Kumar, Mridula Dwivedi, Arun Singh Yadav, Mohit Kumar, Sukhpal Singh Gill, Mohammed Atiquzzaman · 2024
Wireless sensor network (WSN) have gained significant popularity due to their extensive use in diverse domains such as environmental monitoring and disaster assistance.The issues of power consumption and ineffective data collection are of utmost importance in practical situations.In order to address these difficulties, IoT-Enabled WSN are progressively adopting mobile sinks (MSs) to enhance energy efficiency and reduce the number of redundant hops in the network's broadcasts.Nevertheless, there are two essential objectives that must be achieved in order for an MS deployment to be successful: identifying the optimal locations for the MS to rest and devising the most effective route for the MS.This paper presents a novel Rank-Based Path Planning (RPP) technique that utilizes the meta-heuristic algorithm Grey Wolf Optimizer (GWO) to choose the most efficient route for the MS.The MS connects to strategic hot spot nodes (HSNs) ranked by the RPP-GWO algorithm to gather data.The remaining nodes that do not match the criteria for Highly Sensitive Nodes (HSNs) effectively relay data to their nearest HSN.The MS then stops at every HSN node to reduce calculations and travel time.RPP-GWO is implemented in MATLAB.The experimental results demonstrate that the proposed RPP-GWO algorithm surpasses the other methods in terms of computing output, error rate, and MS transit distance in IoT-Enabled WSN.After conducting the algorithms over a hundred iterations, the findings indicate that RPP-GWO is the optimal choice for minimizing the distance traversed by MS.