UAV-Driven Sustainable and Quality-Aware Data Collection in Robotic Wireless Sensor Networks


GÜL Ö. M., ERKMEN A. M., Kantarci B.

IEEE Internet of Things Journal, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1109/jiot.2022.3195677
  • Journal Name: IEEE Internet of Things Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
  • Keywords: Batteries, cluster-based routing, Data collection, energy efficient routing, IoT, Robot kinematics, Robot sensing systems, robotic network, Robots, Sensors, UAV, Wireless sensor networks, WSN
  • Middle East Technical University Affiliated: Yes

Abstract

IEEEEnergy-aware data collection is of paramount importance for robotic and wireless sensor networks. Although static sink-aided cluster-based protocols provide energy-efficient solutions, UAV-aided approaches can be considered as better alternatives to reduce energy consumption while data acquisition compared with static sinks. Most of the existing UAV-driven solutions have not considered a limit on battery capacity of the UAV, which needs to be considered in a practical manner. This paper investigates energy-aware data collection in robot network clusters. In each cluster, a cluster head (CH) robot allocates one collaborative task to each cluster member (CM) robot and collects data from CMs whereas an unmanned aerial vehicle (UAV) collects data from CH robots by visiting a subset of them due to its battery limitation. To complement the state of the art, UAV decision for visiting the subset of CHs is constrained to multiple factors including residual battery capacity, as well as locations and data qualities of all CH robots. Nonvisited CH robots use CH robots as relay nodes for data forwarding. Following upon this, by considering the problem under data hopping constraints, the paper also presents a sensitivity analysis with respect to data hopping constraints. Simulations show that the proposed policy achieves zero total joint cost whereas the state of the art approaches result in significantly high total joint costs. Furthermore, the proposed policy reduces the total joint cost by up to 50% with respect to the conventional approaches.