Multicriteria UAV Base Stations Placement for Disaster Management

Tallha Akram, Muhammad Awais, Rameez Naqvi, Ashfaq Ahmed, Muhammad Naeem

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

In disaster situations, collapse of local communication infrastructure is a major issue due to destruction of buildings, antennas, power sources, and to name a few. Drones, as flying base stations, are a promising solution to restore essential communication services in emergency situations. The contribution of this article is twofold: First, an efficient computer vision technique is proposed to identify areas with high density of low mobility or stationary users. This is done using a multistep process, which includes image acquisition, classification, and crowd density estimation. Next, an accurate mathematical model is presented for joint optimization of drone base stations placement and user assignment. The goal here is to maximize the number of serviced users with minimum number of drones, while satisfying practical network constraints. An optimal solution to such a biobjective optimization problem has complexity exponential to the network size. Furthermore, a low complexity heuristic is proposed to solve the optimization problem. Complexity analysis of the proposed solution is then carried out. Simulation results for a number of practical network scenarios demonstrate that the proposed solution achieves a performance comparable to the optimal solution.

Original languageBritish English
Article number9007341
Pages (from-to)3475-3482
Number of pages8
JournalIEEE Systems Journal
Volume14
Issue number3
DOIs
StatePublished - Sep 2020

Keywords

  • Cellular networks
  • neural networks
  • optimization
  • unmanned aerial vehicles (UAVs)

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