Biblio

The development of mobile internet has brought convenience to people, but the openness and diversity of mobile Internet make it face the security threat of communication privacy data disclosure. In this paper, a trusted android device security communication method based on TrustZone is proposed. Firstly, Elliptic Curve Diffie-Hellman (ECDH) key agreement algorithm is used to make both parties negotiate the session key in the Trusted Execution Environment (TEE), and then, we stored the key safely in the TEE. Finally, TEE completes the encryption and decryption of the transmitted data. This paper constructs a secure communication between mobile devices without a trusted third party and analyzes the feasibility of the method from time efficiency and security. The experimental results show that the method can resist malicious application monitoring in the process of data encryption and ensures the security of the session key. Compared with the traditional scheme, it is found that the performance of the scheme is not significantly reduced.

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.