Hybrid Heuristic-Based Multi-UAV Route Planning for Time-Dependent Data Collection

Unmanned Aerial Vehicle (UAV) has been increasingly adopted for IoT data collection in large-scale scenarios that are with less or even no network coverage. Efficient UAV route planning is a vital part of such a UAV-based data collection process, which is recognized to be complex and challenging, especially considering that the amount of data collected is dependent on UAV visit time and service time and many coupled decisions are involved. Taking these challenges into consideration, this paper proposes a new hybrid heuristics-based UAV route planning method for IoT data collection. Specifically, the relationships among UAV service time, data amount, and data collection time windows of IoT devices are analyzed first, then an integrated route planning model for multiple UAVs is developed. After that, an innovative Hybrid Tabu Search-Variable Neighborhood Descent (HTS-VND) algorithm is developed, with six effective operators that could further improve computing efficiency and solution quality. Finally, extensive experimental case studies are conducted. The proposed method can efficiently improve the collected data amount compared to existing methods in medium-scale and large-scale scenarios.