Towards traffic load balancing in drone-assisted communications for IoT

Qiang Fan, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


Edge computing enables data collected by Internet of Things (IoT) devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users [i.e., user equipments (UEs)], drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs and 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a traffic load balancing scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two subproblems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme.

Original languageEnglish (US)
Article number8588325
Pages (from-to)3633-3640
Number of pages8
JournalIEEE Internet of Things Journal
Issue number2
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Drone base station (DBS)
  • Internet of Things (IoT)
  • edge computing
  • traffic load allocation


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