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.

中文翻译：

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移动物联网网络中移动边缘计算的高效、安全的多用户多任务计算卸载


移动边缘计算（MEC）是一种新范例，通过低延迟的计算卸载来缓解移动物联网网络的资源限制。本文提出了一种高效、安全的多用户多任务计算卸载模型，在移动边缘计算的延迟、能耗和安全性方面具有保证的性能。它不仅研究卸载策略，还考虑资源分配、压缩和安全问题。首先，为了保证多用户场景下共享资源的有效利用，无线电和计算资源被联合寻址。此外，还使用JPEG和MPEG4压缩算法来减少传输开销。为了满足安全要求，引入了安全层来保护传输的数据免受网络攻击。此外，资源分配、压缩和安全的集成模型被表述为整数非线性问题，其目标是在延迟约束下最小化能量的加权和。由于该问题被认为是 NP 难问题，因此应用线性化和松弛方法将问题转化为凸问题。最后，设计了一种高效的卸载算法和详细的流程，为移动用户的计算任务做出计算卸载决策。仿真结果表明，与本地执行相比，我们的模型不仅节省了约 46% 的系统开销消耗，而且对于大规模物联网网络也具有良好的扩展性。