An Optimized Trajectory Planning of Mobile Sink for Network Coding based Data Aggregation Scheme in Wireless Sensor Network

Abstract

A Low-Duty-cycled Wireless Sensor Network (LDWSN) is configured for various purposes, including route finding, water quality monitoring, and system setup to distribute more data across the network. This network uses a Mobile Sink (MS) to collect and distribute data between each sensor node and the base station using various flooding protocols, resulting in reduced delay and energy consumption. Among these protocols, a Network Coding-based Flooding algorithm with MS (NCFMS) reduces overall latency by determining the route for the MS to be portable across various paths in the Flooding Tree (FT). However, this trajectory may not be optimal for data gathering. If the MS travels faster, the distance covered in a given time increases, potentially leading to a longer trajectory. In such cases, the MS may take inefficient paths that do not prioritize data gathering, resulting in lower network throughput. It is crucial to design an optimal trajectory for the MS to enhance data gathering ability and maximize overall network performance. Therefore, this article proposes an Optimized NCFMS (ONCFMS) that formulates the problem of designing a trajectory for the MS as an optimization problem and solves it using a metaheuristic algorithm. First, the problem of overall energy usage in each node and total flooding delay with an MS is modelled by considering each possible traveling order of paths. The fitness function and constraint criteria are determined. Then, an Orangutan Optimization Algorithm (OOA) is suggested to obtain an optimal trajectory for the MS and improve network efficiency. Findings demonstrates that the OOA-based ONCFMS model achieves an average power usage of 0.87 J, a network lifespan of 860 minutes, a throughput of 97.9%, and an End-to-End (E2E) delay of 8.4 ms for 500 nodes. These results outperform existing Data Collection Using Linear Programming (DCLP) and Optimal Clustering and Network Topology for MS (OCNTMS) algorithms.