Resource-Efficient Coverage Path Planning for UAV-Based Aerial IoT Gateway

Abstract

Unmanned Aerial Vehicles (UAVs) have a lot of potential for developing new applications in a variety of fields, such as traffic monitoring, security, and military applications. In the vast nature of the Internet of Things (IoT) network, UAVs could work as Aerial Gateway (AG) for communications among low-powered and distributed ground IoT Devices (IDs). This research concentrates on the path planning and deployment system that may facilitate decisionmaking and guaranteed resource-efficient UAV mission assignment in serving ground IDs. Due to limited resources, it is essential to take into account several factors when designing such a system, including the AG flight time, the coverage radius, and the ground-to-air system's achievable data rate. As a result, the Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. The EECPP is composed of two algorithms: the Stop Point Prediction Algorithm using K-Means, and Path Planning Algorithm using Particle Swarm Optimization. The outcome demonstrates that, in terms of total flight distance, EECPP outperforms Close Enough Traveling Salesman Problem (CETSP) by 19.99%. EECPP reduced energy usage by an average of 56.15% as opposed to Energy-Efficient Path Planning (E2PP). Due to its mobility nature with the addition of effective path planning, the AG is able to hover at each stop point, making it ideal for usage in crowded regions with high demand, emergency circumstances, and distant locations with no access to fixed base stations.