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Multiaccess edge computation (MEC) is a hotspot in 5G network. The problem of task offloading is one of the core problems in MEC. In this paper, a novel computation offloading model which partitions tasks into subtasksis proposed. This model takes communication and computing resources, energy consumption of intelligent mobile devices, and weight of tasks into account. We then transform the model into a multiobjective optimization problem based on Pareto that balances the task weight and time efficiency of the offloaded tasks. In addition, an algorithm based on hybrid immune and bat scheduling algorithm (HIBSA) is further designed to tackle the proposed multiobjective optimization problem. The experimental results show that HIBSA can meet the requirements of both the task execution deadline and the weight of the offloaded tasks.

Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes.

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