Latency Minimization for Sensing-Assisted Secure Mobile Edge Computing Systems

In this letter, we investigate a sensing-assisted secure mobile edge computing (MEC) system against an aerial eavesdropper (AE), where a full-duplex base station equipped with an MEC server simultaneously provides edge computing services to Internet of Things (IoT) devices and transmits sensing signals to track and jam the AE. Specifically, we jointly optimize the offloading data ratio, computational resource allocation for both the MEC server and devices, devices’ transmission power, sensing signal covariance matrix, and receive beamforming to minimize computation task completion latency, while satisfying the Cramér-Rao bound (CRB) for AE’s 2-dimensional angle estimation and the eavesdropping rate constraints. To address this intricate problem involving variable coupling and non-convexity, we propose an alternating optimization algorithm based on fractional programming and generalized Rayleigh entropy, with the Schur complement lemma used to handle the non-convex constraints. Simulation results demonstrate that the proposed scheme can effectively reduce task completion latency while ensuring both sensing performance and secure communication.