Integrated Sensing-Communication-Computation Design for Energy Efficient Data Processing

To support the unprecedented growth of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Internet of Things</i> (IoT) networks, tremendous data need to be collected by the IoT devices and processed. Due to the limited computing capabilities of IoT devices, part of collected data need to be offloaded to the edge server for further processing. The conventional designs separately considering sensing, communication and computation processes lead to severe wastes of radio, energy, and computation resources. To overcome this drawback, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated sensing-communication-computation</i> (ISCC) design is proposed in this paper, which aims at realizing energy efficient data processing by jointly determining the data offloading ratio together with the sensing and offloading rates according to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">central processing units</i> (CPU) profiles of IoT devices and servers. To deal with the resultant non-trivial optimization problem, the case with full-utilization of the server's CPU resource is considered, where the data offloading ratio is directly determined by the server's CPU profile, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">string-pulling</i> (SP) algorithms are proposed to obtain the optimal sensing and offloading rates. As for the case with under-utilization of the server's CPU resource, a master-slave solving approach is proposed, where the master problem for optimizing the data offloading ratio can be solved by sub-gradient method, while the slave problem for optimizing the sensing and offloading rates can be solved by SP algorithms. The above solving approaches are further modified to account for the case with sensing busy time at which no data can be sensed by the IoT device. Simulations are conducted to verify the effectiveness of the proposed algorithms.