Mobility-Aware Semantic Offloading With NOMA in Edge-Enabled IoT Networks

The rapid development of Internet of Things (IoT) applications has imposed stringent demands on low-latency and energy-efficient task offloading. Although mobile edge computing (MEC) provides nearby computing capabilities, conventional data-driven offloading approaches suffer from redundant transmission and limited scalability under constrained wireless and computational resources. To address this, we propose a general semantic-aware offloading framework that integrates semantic communication, Non-Orthogonal Multiple Access (NOMA), MEC, and user mobility prediction in edge-enabled IoT networks. The offloading user first performs local semantic extraction based on a tunable semantic factor that influences both the local computation cost and the transmitted data volume, and theoretical modeling focuses on adjustable granularity to derive an approximate optimal theoretical bound. The resulting semantic information is then transmitted to the MEC server for task execution. We formulate a joint optimization problem to minimize total user-side energy consumption by optimizing offloading decisions, semantic factors, transmit power, and time-slot scheduling. To solve this, we develop a Mixed-Integer Linear Programming (MILP)-based solution via linearization and piecewise approximation of the original non-convex problem, and we further propose a low-complexity heuristic algorithm for practical feasibility.