Long-Term Secrecy Throughput and Fairness Tradeoff for Wireless Powered Secure IoT Networks

Abstract

This paper focuses on the interplay between secrecy throughput and fairness for a wireless powered secure Internet of Things (IoT) network, where IoT devices harvest energy from an access point (AP), and securely upload their data to the AP by exploiting physical layer security (PLS) technology. To achieve it, a multi-objective stochastic optimization problem is formulated to simultaneously maximize long-term averaged secrecy sum-rate and minimum rate (min-rate) of devices, subject to devices’ energy sustainability and AP’s energy budget constraints, where energy beamforming, power allocation, and time scheduling policies are jointly optimized. In order to ensure network fairness, we construct a virtual fairness queue for each device to characterize the gap between its averaged secrecy rate and the expected secrecy min-rate, and incorporate its status into the transmission design. Due to the non-convexity of original problem and massive number of optimization variables, Lyapunov optimization framework is leveraged to decompose it into multiple online resource allocation problems, each of which is dependent on the system status of current slot. Accordingly, a fairness-energy-aware secure transmission (FEaST) scheme is proposed, where the closed-form energy beamforming, power allocation and time scheduling are derived according to the instantaneous status of fairness queue, energy consumption and wireless channels. Finally, simulation results validate that the gain of secrecy min-rate is over 84% and 123% under linear and nonlinear energy harvesting models respectively, and that of the weighted sum of secrecy sum-rate and min-rate is over 30% and 50% respectively, when compared with fixed scheduling scheme. Moreover, the inherent tradeoff between long-term secrecy sum-rate and min-rate is unveiled.