RIS-Enabled Integrated Sensing, Computing, and Communication for Internet of Robotic Things

The Internet of Robotic Things (IoRT) thrives in extreme environments where human operation is often unfeasible, positioning it a pivotal innovation for future enhancements in quality of life. Nonetheless, IoRT faces substantial challenges, particularly in timely environmental sensing and decision making. This article introduces a novel reconfigurable intelligent surface (RIS)-enabled integrated sensing, computing, and communication (ISCC) system, specifically tailored for IoRT applications. The proposed system leverages RIS technology to enhance the transmission rates during critical task offloading processes from robots to mobile edge computing (MEC) platforms, addressing a significant bottleneck in current IoRT frameworks. Within our system, we address a complex optimization problem that aims to simultaneously boost computational speed, communication rate, and sensing accuracy, thereby maximizing the overall system performance. Given the nonconvex nature of this challenge, a block coordinate descent (BCD) algorithm is employed to decompose the problem into two manageable subproblems effectively. The first subproblem focuses on minimizing computing latency through strategic allocation of edge computing resources, while the second targets maximizing communication speed and improving sensing precision. To tackle these objectives, our approach integrates alternating optimization (AO) with objective function conversion techniques, crafting a robust methodology that adapts to the dynamic needs of IoRT environments. Our extensive simulations validate the proposed algorithm's effectiveness, showcasing significant enhancements in Quality of Service (QoS) and notable reductions in system latency.