Leveraging Reconfigurable Intelligent Surfaces for Task Offloading in Edge IoT Networks

There is an explosive growth of intelligent devices in the IoT ecosystem over the years. Owing to the massive multiple access at the network edge, there is increased latency and transmission overhead. Multi-access edge computing (MEC) is a key technology used to offload the wireless devices from the computational tasks. But the wireless signal propagation is subject to fading, attenuation, obstructions and other disturbances thereby affecting the performance of edge network. Reconfigurable intelligent surface (RIS) technology improves the quality of wireless propagation links through controlled reflection. This paper presents an RIS-aided framework for a heterogenous edge network to offload the computation tasks of the resource constraint user equipment to the small access points (APs). A resource control algorithm is proposed which enables selection of an RIS-AP pair for each node in the edge network. The proposed algorithm selects the RIS-AP pair using maximum channel gain criteria such that the system sum throughput is maximized. Also, enabling reflection through the multiple RISs, the shortest path is selected using graph theory to obtain the tradeoff between latency and reflection loss. It is observed that the proposed approach improves the achieved sum throughput of the system by 21.7% and the latency is reduced by 13.8%. The network performance is evaluated for varied RIS size and number of reflecting elements under different RIS phase shift design. It is shown that RIS with 1000 reflecting elements each of size λ/2 x λ/2 with equal phase shifts achieve sum throughput gain of 25.2% over randomly chosen phase shifts. Further, the comparison of IRS-aided MEC system with conventional MEC system and clustered MEC system is performed.