Privacy-Aware Task Assignment for IoT Audit Applications on Collaborative Edge Devices

Abstract

To meet the rapidly increasing demand for Internet of Things (IoT) applications, edge computing, as a novel computing paradigm, can combine devices at the edge of the network to collaboratively provide computing resources for IoT applications. However, the dynamic, heterogeneous, distributed, and resource-constrained nature of the edge computing paradigm also brings some problems, such as more serious privacy leakages and performance bottlenecks. Therefore, how to ensure that the resource requirements of the application are satisfied, while enhancing the protection of user privacy as much as possible, is a challenge for the task assignment of IoT applications. Aiming to address this challenge, we propose a privacy-aware IoT task assignment approach at the edge of the network. Firstly, we model the resource and privacy requirements for IoT applications and evaluate the resource satisfaction and privacy compatibility between edge devices and tasks. Secondly, we formulate the problem of privacy-aware IoT task assignment on edge devices (PITAE) and develop two solutions to the PITAE problem based on the greedy search algorithm and the Kuhn–Munkres (KM) algorithm. Finally, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the PITAE problem can be solved effectively and efficiently.