Secure Edge Offloading Strategy for IoT-Enabled Electromagnetic Sensing Systems

Abstract

Internet of Things (IoT) devices equipped with electromagnetic sensors, such as millimeter-wave radars, generate massive amounts of computation-intensive and latency-sensitive sensing data. However, their limited computational capability and energy supply hinder real-time local processing. Edge computing can alleviate this limitation by offloading electromagnetic sensing tasks to nearby edge servers. Nevertheless, the broadcast nature of wireless channels exposes sensing data to eavesdropping, while the heterogeneity of multi-source electromagnetic sensing data further complicates sensing-data offloading optimization. To address this issue, this paper develops secure edge-assisted sensing-data offloading framework for heterogeneous electromagnetic sensors deployed on IoT devices. Considering the energy consumed by electromagnetic sensing, wireless transmission, and edge computing, together with the end-to-end latency constraint, a secrecy energy efficiency maximization problem is formulated by jointly optimizing spectrum assignment, transmit power, and computing resource allocation. The simulation results demonstrate that the proposed algorithm improves secrecy energy efficiency at least 31% compared with benchmark schemes within a specific range, demonstrating its effectiveness for secure and energy-efficient electromagnetic sensing-data offloading.