Aiming at the diversified requirements of network application QoS (Quality of Service) in the terminal equipment of Internet of Vehicles, this paper proposes a distributed congestion control strategy based on harmony search algorithm and the Throughput Evaluation Priority Adjustment Model (TEPAM) to ensure real-time transmission of high-priority data messages related to security applications. Firstly, the channel usage rate is periodically detected and the congestion is judged; then, in order to minimize delay and delay jitter as the goal, harmony search algorithm is utilized to perform global search to obtain a better solution for the transmission range and transmission rate. Secondly, packet priority and the TEPAM are applied to indicate the sending right of each packet. The data message priority and throughput percentage factor are used to express the transmission weight of each data message. Besides, the real-time evaluation of path state in MPTCP is carried out by the batch estimation theory model, which realizes the on-demand dynamic adjustment of the network congestion time window. Finally, SUMO, MOVE, and NS2 tools are used to create a VANET-like environment to evaluate the performance of the proposed congestion control strategy. Experimental results show that the proposed method is superior to other three methods in the four indicators of average delay time, average transmission rate, number of retransmissions, and packet loss rate compared with other advanced methods.

This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi area multi-user multi MEC server system is designed, in which a MEC server is deployed in each area, and multiple vehicle user equipment in an area can offload computing tasks to MEC servers in different areas by a wireless channel. Then, considering the mobility of users in Internet of vehicles, a vehicle distance prediction based on Kalman filter is proposed to improve the accuracy of vehicle-to-vehicle distance. However, when the vehicle performs the task, it needs to submit the real location, which causes the problem of the location privacy disclosure of vehicle users. Finally, the total cost of communication delay, location privacy of vehicles and energy consumption of all users is formulated as the optimization goal, which take into account the system state, action strategy, reward and punishment function and other factors. Moreover, Double DQN algorithm is used to solve the optimal scheduling strategy for minimizing the total consumption cost of system. Simulation results show that proposed algorithm has the highest computing task completion rate and converges to about 80% after 8000 iterations, and its performance is more ideal compared with other algorithms in terms of system energy cost and task completion rate, which demonstrates the effectiveness of our proposed scheduling strategy.

The application of big data in the medical device industry mainly refers to the analysis and processing of various medical devices, so as to provide patients with better treatment and rehabilitation services. At present, our country already has a relatively mature and reliable large database system. This article studies the application of medical equipment in the big data information platform. The main methods used in this article are survey method, case analysis method, and interview method. The big data information platform and medical devices are studied from different aspects. The survey results show that 41% of people completely agree with the role of big data information platforms in medical devices.

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Aiming at the problem of scheduling computing resources for massive Internet of Things (IoT) devices, this paper proposes a scheduling strategy model based on mobile edge computing for massive IoT computing resources. First, the application scenarios are defined, the task offloading model and queue model are constructed. Then, task urgency and BS energy are considered to determine the optimization goal. Next, wolf colony algorithm is used to improve pheromone calculation so that the ant colony algorithm converges faster and is not easy to fall into local optimum when adjusting the computing resources of IoT devices, and then realizes the scheduling strategy of massive IoT devices. Finally, the experimental verification and comparative analysis of our proposed method are carried out. Experimental results show that proposed method is superior to the method based on Game Theory (GT) and the method based on Sub-Optimal Policy (SOP). Besides, the proposed method can offload more tasks under the same conditions. The average energy consumption of proposed method is lower in 60-240 GHz frequency band. Moreover, it appears to increase significantly in 60-120 GHz frequency band, and tends to be stable in 120-240 GHz frequency band.