Cooperative Computing for Mobile Crowdsensing: Design and Optimization

With the increasing number of mobile devices, mobile crowdsensing (MCS) has garnered significant attention in research. However, computing infrastructures such as edge/cloud nodes, which are necessary for processing sensor data, are not always readily available. To address this issue, we propose a cooperative computing framework that enables the offloading of sensor data to nearby mobile devices with unused computational resources (known as helpers) for processing. Our approach considers a scenario with multiple sources and multiple helpers, where computational tasks can be partially offloaded to several helpers. We jointly optimize task offloading strategy, communication resources, and computational resources to minimize the weighted sum energy consumption of mobile devices. We model the optimization problem as a mixed-integer nonlinear programming (MINLP), with the source-helper assignment solved using a distributed algorithm based on matching theory, and the joint task partition and resource allocation problem solved using an alternating optimization (AO) method. Simulation results demonstrate the efficacy of our cooperative computing framework and scheduling scheme, which offer significant advantages over local computing in terms of reducing the weighted sum energy consumption and improving the task completion ratio.