Location Privacy-Aware Offloading for MEC-Enabled IoT: Optimality and Heuristics

Abstract

With the rapid development of 5G and the Internet of Things (IoT), cloud-based access mode is applied more and more in IoT scenarios. The emerging IoT applications put forward higher requirements on energy consumption and processing capacity of the network, as well as more strict privacy restrictions. The emergence of mobile edge computing solves the aforementioned user experience problems, which greatly improves execution efficiency and saves a lot of energy. However, existing studies pay more attention to issues such as energy consumption and delay, but ignore the privacy and imbalanced server load problems that mobile edge computing may cause. This paper made bandwidth resource allocation and task offloading decisions for multiple users via joint optimization, provided the privacy requirements of IoT terminals and load balancing requirements are satisfied. The objective was to minimize the total energy consumption of all terminal devices. By modeling the problem as an integer linear programming problem, the optimal solution is obtained. Considering the high computational complexity of solving integer linear programming problems, a more practical heuristic algorithm was proposed. The simulation results showed the effectiveness of the proposed optimal and heuristic schemes in reducing energy consumption, and demonstrated the importance of privacy protection.