Autonomous UAV Path Optimization Using Genetic and Multiobjective Evolutionary Algorithms for Effective Data Retrieval in Cache-Enabled Mobile Ad-Hoc WSNs - Umair B. Chaudhry; Chris Ian Phillips (Journal of Computer Networks and Communications)
Collecting data from nodes in mobile ad hoc wireless sensor networks is a persistent challenge. Traditional methods rely on specialized routing protocols designed for these environments, with research often aimed at improving efficiency in terms of throughput and energy consumption. However, these improvements are often interconnected, where gains in one area can lead to compromises in another. An alternative approach uses unmanned vehicles (UVs), particularly unmanned aerial vehicles (UAVs), due to their adaptability to various terrains. Unlike traditional methods, UAVs can collect data directly from mobile nodes, eliminating the need for routing. While most existing research focuses on static nodes, this paper introduces a multiple objective evolutionary approach “Strength Pareto Evolutionary Algorithm for Dynamic UAV Paths” (SPEA-DUP) for UAV data collection that predicts the future positions of caching-enabled mobile ad hoc wireless sensor network nodes. SPEA-DUP aims to maximize encounters with nodes and gather the most valuable data in a single trip. The proposed technique is tested across different simulation scenarios, movement models, and parameter configurations and is compared to our genetic algorithm ‘Genetic Algorithm-Aerial Paths’ (GA-AP) counterpart to evaluate its effectiveness.