Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks

• Published in: IEEE eTransactions on network and service management Vol. 17; no. 4; pp. 2410 - 2422
• Main Authors: Elgendy, Ibrahim A., Zhang, Wei-Zhe, Zeng, Yiming, He, Hui, Tian, Yu-Chu, Yang, Yuanyuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; compression; Computation offloading; Computational efficiency; Computational modeling; Cybersecurity; Edge computing; Energy consumption; Image compression; Internet of Things (IoT); Mobile computing; Mobile handsets; mobile-edge computing; optimization; Resource allocation; Resource management; Security; Task analysis; Wireless networks
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2020.3020249

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.