Joint Placement and Device Association of UAV Base Stations in IoT Networks

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 9; p. 2157
• Main Authors: Ahmed, Ashfaq, Awais, Muhammad, Akram, Tallha, Kulac, Selman, Alhussein, Musaed, Aurangzeb, Khursheed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09-05-2019; MDPI
• Subjects: Aerial base stations; Communication; Complexity; Computer simulation; Drones; Energy efficiency; Energy management; Engineering; Heuristic; Internet of Things; Internet of Things (IoTs); Optimization; Polynomials; Power management; Quality of service architectures; Radio equipment; resource management; Sensors; Unmanned aerial vehicles; Unmanned aerial vehicles (UAVs); User satisfaction; Vehicles; Wireless networks; United States > US; resource management; Internet of Things (IoTs); Unmanned aerial vehicles (UAVs); Aerial base stations
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19092157

Drone base stations (DBSs) have received significant research interest in recent years. They provide a flexible and cost-effective solution to improve the coverage, connectivity, quality of service (QoS), and energy efficiency of large-area Internet of Things (IoT) networks. However, as DBSs are costly and power-limited devices, they require an efficient scheme for their deployment in practical networks. This work proposes a realistic mathematical model for the joint optimization problem of DBS placement and IoT users' assignment in a massive IoT network scenario. The optimization goal is to maximize the connectivity of IoT users by utilizing the minimum number of DBS, while satisfying practical network constraints. Such an optimization problem is NP-hard, and the optimal solution has a complexity exponential to the number of DBSs and IoT users in the network. Furthermore, this work also proposes a linearization scheme and a low-complexity heuristic to solve the problem in polynomial time. The simulations are performed for a number of network scenarios, and demonstrate that the proposed heuristic is numerically accurate and performs close to the optimal solution.