Competitive Game Theoretic Clustering-Based Multiple UAV-Assisted NB-IoT Systems

• Main Authors: Chunghyun Lee, Gunhee Jang, Nhu-Ngoc Dao, Demeke Shumeye Lakew, Cheol Lee, Sungrae Cho
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Electronics
• Subjects: UAV communication; UAV placement; Stackelberg game theory; capacity optimization; energy efficient; Internet of Things
• Online Access: https://www.mdpi.com/2079-9292/10/3/356

Unmanned aerial vehicle (UAV) communication is regarded as a promising technology for lightweight Internet of Things (IoT) communications in narrowband-IoT (NB-IoT) systems deployed in rugged terrain. In such UAV-assisted NB-IoT systems, the optimal UAV placement and resource allocation play a critical role. Consequently, the joint optimization of the UAV placement and resource allocation is considered in this study to improve the system capacity. Because the considered optimization problem is an NP-hard problem and owing to its non-convex property, it is difficult to optimize both the UAV placement and resource allocation simultaneously. Therefore, a competitive clustering algorithm has been developed by exchanging strategies between the UAV and the adjacent IoT devices to optimize the UAV placement. With multiple iterations, the UAV and the IoT devices within the coverage area of the UAV, converge their clustering strategies, which are suboptimal, to satisfy both sides. The bordering IoT devices of the adjacent clusters are then migrated heuristically toward each other to obtain the optimal system capacity maximization. Finally, the transmission throughput is optimized using the Nash equilibrium. The simulation results demonstrate that the algorithms proposed in this study exhibit rapid convergence, within 10 iterations, even in a large environment. The performance evaluation demonstrates that the proposed scheme improves the system capacity of the existing schemes by approximately 28%.