Secure task offloading framework for industrial edge computing using reconfigurable intelligent surfaces and spectrum agility

Dense Industry 5.0 deployments require IIoT task offloading schemes that are simultaneously scalable, low latency, and resilient to wireless eavesdropping under harsh industrial blockage. This paper proposes a unified secure offloading framework that integrates reconfigurable intelligent surfaces (RIS), frequency hopping spread spectrum (FHSS), and uplink nonorthogonal multiple access (NOMA) within a multiple MEC architecture. We formulate a mixed integer nonconvex max min problem that jointly optimizes device server association, computation allocation, transmit power, FHSS carrier selection, and RIS phase shifts to maximize the minimum secrecy rate while satisfying strict end to end latency and minimum rate constraints. To solve the resulting coupled design, we develop a block coordinate descent algorithm with successive convex approximation, yielding tractable subproblems and a monotonic improvement of the objective. Simulations show that the proposed design improves the minimum secrecy rate by 18.75%, reduces average latency by 41%, and improves energy efficiency by up to 30% relative to five benchmarks, including traditional NOMA, random RIS phases, fixed power allocation, no FHSS, and conventional OMA. These results highlight the benefits of jointly exploiting spatial control (RIS), spectral agility (FHSS), and massive access (NOMA) for secure and reliable IIoT offloading in obstructed industrial environments. Furthermore, by ensuring robust connectivity in these harsh settings, this research directly supports the digital transformation of the mining sector, facilitating secure IoT safety monitoring and automated extraction systems in alignment with Saudi Vision 2030.