Wireless powered Public Safety IoT: A UAV-assisted adaptive-learning approach towards energy efficiency

Abstract

Public Safety Networks (PSN) provide resilient communication paradigms under disaster recovery scenarios. In this context, the increased integration of Internet of Things (IoT) architectures can further support critical and massive information flows. In this paper, we propose a framework that combines Unmanned Aerial Vehicle (UAV)-support with wireless powered communication (WPC) techniques to further improve energy efficiency in a distributed non-orthogonal multiple access (NOMA) PSN. The IoT devices form coalitions by initially choosing their role (coalition head or coalition member) in the network independently and in a distributed fashion, following the theory of Minority Games (MG). Subsequently, the member nodes act as stochastic learning automata to associate with a coalition head using a reinforcement learning technique. Towards extending the PSN's lifetime, we utilize a harvest-transmit-store WPC mechanism, where the IoT nodes harvest energy from the mobile UAV before transmitting their information. The UAV optimal positioning in the Euclidean 3D space is determined through an optimization problem of maximizing the coalition head's total energy availability, as these nodes play a critical role within the PSN acting as emergency gateways. Finally, a non-cooperative game-theoretic approach is adopted to determine the optimal uplink transmission power of each IoT node in a distributed manner and the existence of a unique Nash equilibrium is shown. The performance evaluation of the proposed framework is achieved via modeling and simulation, and the numerical results demonstrate its energy efficiency, robustness, and scalability.