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Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things technology and the promotion of embedded devices' computation performance, smart devices are probably open to security threats and attacks while connecting with rich and novel Internet. Attracting lots of attention in embedded system security community recently, Trusted Execution Environment (TEE), allows for the execution of arbitrary code within environments completely isolated from the rest of a system. However, existing memory protection methods in a TEE are inadequate. In general, the software-based formal methods are not practical and the hardware-based implementation approaches lack of theoretical proof. To address the memory isolation and protection problems in TEE, in this paper, we propose a practical memory integrity protection method on an ARM-based platform, called MIPE, to defend against security threats including kernel data attacks and direct memory access attacks. MIPE utilizes TrustZone technique to create a isolated execution environment, which can protect the sensitive code and data against attacks. To present the integrity protection strategies, we provide the design of MIPE using B method, which is a practical formal method. We also implement MIPE on the Xilinx Zynq ZC702 evaluation board. The evaluation results show that the automatic proof rate of machines using B method is about 78.32%, and the proposed method is effective and feasible in terms of both load time and overhead. [ABSTRACT FROM AUTHOR]