Active RIS-assisted task partitioning and offloading for industrial edge computing

Abstract

In Industry 5.0, smart devices in intelligent factories will generate numerous computation-intensive tasks that require low latency. Due to the limited computation resources of local devices, it is required to partition and offload tasks to edge servers via wireless networks for end-edge collaborative computing. However, intelligent factories are usually located in low-rise buildings. The direct offloading paths between smart devices and edge servers are vulnerable to being obstructed by high-rise buildings and trees, leading to intolerable long task offloading delays and even failure in offloading. To tackle this problem, we develop an active reconfigurable intelligent surface (RIS)-assisted end-edge collaborative task partitioning and offloading model, which assists task offloading by reflecting communication signals through the active RIS. We propose to maximize the system utility by jointly optimizing the task partitioning and offloading decisions, reconfiguring the phase shift and amplification factor of the active RIS, and communication and computation resource allocation, aiming at energy-efficiently providing delay guarantee to industrial computation tasks. We formulate, decompose, and theoretically analyze the problem. The upper and lower bounds of offloading decisions, transmission powers, and computation resources constrained to delay bounds have been analyzed. Based on the analytical results, a two-stage heuristic algorithm, RISADA, has been proposed to address the problem. The results demonstrate the efficiency of our proposal for the delay guarantee while reducing energy consumption.