Collaborative Relay for Achieving Long-term and Low-AoI Data Collection in UAV-aided IoT Systems

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, the authors develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, they begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, they divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, they establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, they employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, they incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, the authors confirm the significant enhancement provided by their proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

• Record URL:
  • https://doi.org/10.1016/j.vehcom.2023.100719
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2214209623001493
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22142096
• Supplemental Notes:
  • © 2023 Elsevier Inc. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Fu, Xiuwen
  • Huang, Xiong
  • Pan, Qiongshan
• Publication Date: 2024-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 100719
• Serial:
  • Vehicular Communications
  • Volume: 45
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-2096
  • Serial URL: http://www.sciencedirect.com/science/journal/22142096/1/2

Subject/Index Terms

• TRT Terms: Data collection; Data communications; Drones; Environmental monitoring; Network nodes; Sensors
• Subject Areas: Aviation; Data and Information Technology; Energy; Environment; Vehicles and Equipment;

Filing Info

• Accession Number: 01904062
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 4 2024 10:49AM