A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

Abstract

Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach. INDEX TERMS Internet-of-things, data gathering, reinforcement learning, scheduling, unmanned areal vehicles

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

“…The UAVs can also be used for data collection to support the transmission of the data gathered by sensor nodes on the ground. In this case, the vehicle capacity to transmit data is limited by the power consumption in the data transmission, flight speed, the available energy on the UAVs, the deployment location, and various other factors investigated in the literature, as presented in [ 62 , 63 , 64 , 65 ].…”

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

“…In [24], the authors propose a multi-agent DQL algorithm to maximize the minimum throughput by the joint optimization of path design and channel resource assignment in a uav-enabled wireless powered communication network, where the UAVs can fly according to a discrete set of actions. References [25] and [26] aim to minimize the overall flight time of the UAVs under their individual energy constraints. The authors in [25] develop an option-based hybrid DRL method that allows the UAV to choose between two algorithms to handle deterministic and ambiguous boundary scenarios.…”

“…However, the impact of obstacles on the navigation of the UAVs is ignored. Reference [26] addresses the obstacle-aware navigation based on a joint learning approach which first obtains the shortest route for data collection in an obstacle-constrained scenario based on DDPG and then determines the best schedule of the UAVs based on Qlearning. However, the proposed approach does not consider the power consumption of the sensors.…”

Optimal path discovery for two moving sinks with a common junction in a wireless sensor network

“…( ) = Number of edges connected to node All the edges of node i may not participate in forming the MSP. We define the participating degree of a node (vertex) i , represented by ( ) as (17).…”

“…Mobile sinks physically move around the WSN and collect the data from the sensors. Mobile units carrying the sink can be ground based or airborne where unmanned aerial vehicles (UAVs) are utilized [14]- [17]. Here, we use multiple sinks (data collectors) mounted on ground based moving units controlled manually or by robots.…”