Joint Sensing, Communication, and Computation for Status Update in Mobile Edge Computing With Nonorthogonal Multiple Access

Mobile edge computing (MEC) is considered as a promising solution for augmenting the computational capabilities of Internet of Things (IoT) devices by offloading tasks to nearby edge servers (ESs). However, finite communication and computational resources at both IoT devices and ESs, coupled with escalating congestion as more devices connect, present critical challenges in maintaining low latency and high efficiency. In this work, we jointly design the task sensing, communication, and resource allocation for MEC with nonorthogonal multiple access (NOMA). To address the urgent need for timely data processing in IoT applications, we utilize the Age of Information (AoI) metric as a measure of data freshness. With the objective to minimize the system cost, we propose to jointly optimize sensing sampling intervals, sensing frequencies, offloading decision, and power allocation. Recognizing that this problem is NP-hard, we decompose it incrementally and propose a high-dimensional progressive cost optimization (HDPCO) algorithm to reduce overall system cost. Simulation results confirm the effectiveness of HDPCO, showing significant improvements in minimizing overall system cost compared to other proposed schemes.