Leveraging Reconfigurable Intelligent Surfaces for Task Offloading in Edge IoT Networks

• Published in: IEEE internet of things journal Vol. 12; no. 3; pp. 2422 - 2429
• Main Authors: Taneja, Ashu, Rani, Shalli, Rodrigues, Joel J. P. C.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Channel estimation; Computation offloading; Control algorithms; Control theory; Edge computing; Edge network; Fading channels; graph computing; Graph theory; Internet of Things; latency; Mobile computing; Network latency; Obstructions; Optimization; path selection; Performance evaluation; phase shift design; reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Reflection; resource control; Servers; Shortest-path problems; Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3464872

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. Multiaccess 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 article 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 the 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 <inline-formula> <tex-math notation="LaTeX">{}({\lambda }/{2})\times {}({\lambda }/{2}) </tex-math></inline-formula> with equal phase shifts achieve sum throughput gain of 25.2% over randomly chosen phase shifts. Further, the comparison of intelligent reflecting the surface-aided MEC system with the conventional MEC system and the clustered MEC system is performed.