Multi-UAV Data Collection Framework for Wireless Sensor Networks

Abstract

In this paper, we propose a framework design for wireless sensor networks based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to minimize deployment and operational costs, with respect to budget and power constraints. To this end, we first optimize the number and locations of cluster heads (CHs) guaranteeing data collection from all sensors. Then, to minimize the data collection flight time, we optimize the number and trajectories of UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV hovers exactly above the visited CH; and 2) where a UAV hovers within a range of the CH. The results of this include guidelines for data collection design. The characteristics of sensor nodes' K-means clustering are then discussed. Next, we illustrate the performance of optimal and heuristic solutions for trajectory planning. The genetic algorithm is shown to be near-optimal with only 3.5% degradation. The impacts of the trajectory approach, environment, and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is discussed.

Increasing the Efficiency of Data Gathering in Clustered Wireless Sensor Networks Using Uav

“…Construction of routes considering various target functions for overflight of collection points: the shortest (target function -minimum time of data collection); routes with overflight of critical nodes (according to the availability of battery energy, data volume, efficiency of information), etc. [6,7,11]. 5.…”

“…To maximize the throughput between the main nodes of clusters and the UAV in its coverage area it is proposed to calculate its position depending on their load, using the appropriate mathematical model [11].…”

“…UAV flight along the calculated route and data collection. For this purpose, the altitude (the distance between the UAV and the main node is set to ensure a given data transmission speed) and the flight speed are calculated to ensure sufficient time to ensure the quality of all monitoring data transmission [6,11]. The transmission power level between the UAV and the main unit is kept to a minimum.…”

Deep Reinforcement Learning Based Energy Efficient Multi-UAV Data Collection for IoT Networks

“…Existing approaches have exploited various optimization techniques. References [13], [14] focus on minimizing the task completion time in multi-UAV systems. Reference [13] solves the completion time minimization problem for a multi-UAV system by jointly optimizing the UAVs' collection position and speed and sensors' transmit power, subject to the load requirement and energy constraint.…”

“…Reference [13] solves the completion time minimization problem for a multi-UAV system by jointly optimizing the UAVs' collection position and speed and sensors' transmit power, subject to the load requirement and energy constraint. The authors in [14] adopt a twostep approach that first optimizes the number and locations of cluster heads which aggregate the data from all sensors then determines the trajectories of the UAVs. Additionally, a heuristic genetic algorithm is designed to achieve a suboptimal solution with low complexity.…”