Efficient Task Offloading and Load Balancing Approach in IRS‐Aided IoT Network Using Edge Node Allocation

ABSTRACT With the exponential growth of the Internet of Things (IoT), modern networks face significant challenges, including increased data traffic, high latency, elevated energy consumption, and unreliable communication. The limited computational capabilities of IoT devices in a resource‐constrained IoT network is another challenge. Offloading the heavy tasks to the edge servers is utmost required. This paper presents an intelligent reflecting surface (IRS)–aided edge computing framework for an IoT communication network. The IRSs help the IoT nodes to offload the computational tasks to the edge servers reducing data loss and latency. The paper presents two methods of IRS phase optimization that dynamically steer signals towards edge servers, enabling efficient task offloading and load balancing. Further, an algorithm for allocation of edge nodes to particular IoT nodes is proposed minimizing energy overhead and maximizing computational efficiency. Using the proposed association, a task offloading computational model is presented and evaluated for average task completion time and energy per task under different task size. The data rate and power levels are evaluated for different IRS system configurations under various phase optimization methods, user positions , IRS positions, number of antennas , and number of IRS elements . It is observed that the maximum data rate of 18 bps/Hz is achieved when the IRS with more is placed closer to the transmitter. The impact on the power utilization suggests that the power usage is minimum (14.84 dBm) in phase‐optimized IRS‐aided system with . The different system scenarios are compared for performance with optimal and random phase shifts. The practical deployment constraints and the open research directions are also discussed. In the end, the use case scenario of IRS‐aided task offloading in smart home automation is presented.