TY - JOUR
T1 - Multicriteria UAV Base Stations Placement for Disaster Management
AU - Akram, Tallha
AU - Awais, Muhammad
AU - Naqvi, Rameez
AU - Ahmed, Ashfaq
AU - Naeem, Muhammad
N1 - Publisher Copyright:
© 2007-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Cellular networks
KW - neural networks
KW - optimization
KW - unmanned aerial vehicles (UAVs)
UR - http://www.scopus.com/inward/record.url?scp=85088890231&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2020.2970157
DO - 10.1109/JSYST.2020.2970157
M3 - Article
AN - SCOPUS:85088890231
SN - 1932-8184
VL - 14
SP - 3475
EP - 3482
JO - IEEE Systems Journal
JF - IEEE Systems Journal
IS - 3
M1 - 9007341
ER -