Completion Time Minimization for UAV-UGV-Enabled Data Collection

Abstract

In unmanned aerial vehicle (UAV)-enabled data collection systems, situations where sensor nodes (SNs) cannot upload their data successfully to the UAV may exist, due to factors such as SNs’ insufficient energy and the UAV’s minimum flight altitude. In this paper, an unmanned ground vehicle (UGV)-UAV-enabled data collection system is studied, where data collection missions are conducted by a UAV and a UGV cooperatively. Two cooperative strategies are proposed, i.e., collaboration without information interaction, and collaboration with information interaction. In the first strategy, the UGV collects data from remote SNs (i.e., the SNs that cannot upload data to the UAV) as well as some normal SNs (i.e., the SNs that can upload data to the UAV), while the UAV only collects data from some normal SNs. Then, they carry the data back to the data center (DC) without interacting with each other. In the second strategy, the UGV only collects data from remote SNs, while transmitting the collected data to the UAV at a data interaction point, then the data are carried back to the DC by the UAV. There are mobile data collection nodes on the ground and in the air, and the task is to find trajectories to minimize the data collection time in the data center. A collaborative strategy selection algorithm, combining a multi-stage-based SN association and UAV-UGV path optimization algorithm, is proposed to solve the problem effectively, where techniques including convex optimization and genetic algorithm are adopted. The simulation result shows that the proposed scheme reduces the mission completion time by 36% compared with the benchmark scheme.

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

“…Reference [19] compares the performance of different computational intelligence methods to realize UAV path planning in offline and online computing and two-dimensional and three-dimensional environments, and it verifies that the obstacle avoidance path planning process of the UAV based on Dubins curve reduces network delay and improves data transmission rate. Reference [20] firstly solves the initial path of traversing the sensing node through the traveling salesman problem and then uses the communication range of the sensing node, so that the UAV does not have to fly directly above the node but only needs to be within its communication range, predetermining the task point, shortening the length of the whole data collection path, and reducing the energy consumption of the UAV. Reference [21] proposes a fast cruise path algorithm for UAVs.…”

Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things

“…In the paper, the authors focused on improving key performance indicators, such as the Age of Information, latency, and reliability through optimizing short-packet structures in 6G URLLC communication. A cooperative strategy involving an unmanned ground vehicle and UAV is proposed in [32] to collect data from sensor nodes (SNs) in UAV-enabled data collection systems when SNs may not be able to upload their data because of factors such as insufficient energy and low flight altitude. A collaborative strategy selection algorithm that combines multistage-based SN association and UAV-UGV path optimization algorithms was used to determine trajectories for mobile data collection nodes on the ground and in the air to minimize mission completion time.…”