Optimal Energy and Delay Tradeoff in UAV-Enabled Wireless Sensor Networks

Abstract

Unmanned aerial vehicles (UAVs) are promising in large-area data collection due to their flexibility and easy maintenance. In this work, we study a UAV-enabled wireless sensor network (WSN), where K UAVs are dispatched to collect a certain amount of data from each node on the ground. Most existing works assume that the flight energy is either distance-related or duration-related, which may not suit the practical scenario. Given the practical speed-related flight energy model, we focus on deriving the optimal energy and delay tradeoff for the K UAVs such that each node can successfully upload a certain amount of data to one of the K UAVs. Intuitively, the higher flight speed of the UAV results in the shorter completion time of the data collection task, which may however cause the higher flight energy consumption of UAVs during the task. Specifically, we first model the total energy consumption of the UAV during the flight for collecting data within the WSN and then design the flight speed as well as the flight trajectory of each UAV for achieving different Pareto-optimal tradeoffs between the maximum single-UAV energy consumption among all UAVs and the task completion time. To achieve this goal, we propose a novel multi-objective ant colony optimization framework based on the adaptive coordinate method (MOACO-ACM). Firstly, the adaptive coordinate method is developed to decide the nodes visited by each of the K UAVs, respectively. Secondly, the ant colony algorithm is incorporated to optimize the visiting order of nodes for each UAV. Finally, we discuss the impact of UAVs’ speeds scheduling on the tradeoff between the task completion time and the maximum single-UAV energy consumption among all UAVs. Extensive simulations validate the effectiveness of our designed algorithm and further highlight the importance of UAVs’ flight speeds in achieving both energy-efficient and time-efficient data collection.

Keywords

• UAV-enabled WSN
• trajectory design
• multi-objective optimization