Smart grid is a promising power infrastructure that is integrated with communication and information technologies. Nevertheless, privacy and security concerns arise simultaneously. Failure to address these issues will hinder the modernization of the existing power system. After critically reviewing the current status of smart grid deployment and its key cyber security concerns, the authors argue that accountability mechanisms should be involved in smart grid designs. We design two separate accountable communication protocols using the proposed architecture with certain reasonable assumptions under both home area network and neighborhood area network. Analysis and simulation results indicate that the design works well, and it may cause all power loads to become accountable. Index Terms-Accountability, advanced metering infrastructure (AMI), security, smart grid. I. INTRODUCTION W ITH THE increasing demand for electricity these years, conventional power grids present a number of inefficient and unreliable drawbacks due to out-of-date technologies that were originally designed decades ago. Many nations plan to modernize their current power grids due to events such as voltage sags, overloads, blackouts, large carbon emissions, etc. [22]. Most of these countries believe that it not only requires reliability, scalability, manageability, and extensibility but also should be secure, interoperable, and cost-effective. Such electric infrastructure is referred to as "smart grid." Generally, smart grid is a promising power delivery infrastructure integrated with bidirectional communication technologies which collects and analyzes data captured in near real time, including power consumption, distribution, and transmission [1]. According to these data, the smart grid can provide predictive information and relevant recommendations to all stakeholders, including utilities, suppliers, and consumers, regarding the optimization of their power utilization [1]. By two-way electrical flow, consumers are able to sell their surfeit energy back to utilities [2]. Smart grid is a complex system of systems. Deploying such a system has enormous and far-reaching technical and social benefits. Nevertheless, increased interconnection and integration also introduce cyber vulnerabilities into the grid. Based on experiences gained from developed information Manuscript
Private data in healthcare system require confidentiality protection while transmitting. Steganography is the art of concealing data into a cover media for conveying messages confidentially. In this paper, we propose a steganographic method which can provide private data in medical system with very secure protection. In our method, a cover image is first mapped into a 1D pixels sequence by Hilbert filling curve and then divided into non-overlapping embedding units with three consecutive pixels. We use adaptive pixel pair match (APPM) method to embed digits in the pixel value differences (PVD) of the three pixels and the base of embedded digits is dependent on the differences among the three pixels. By solving an optimization problem, minimal distortion of the pixel ternaries caused by data embedding can be obtained. The experimental results show our method is more suitable to privacy protection of healthcare system than prior steganographic works.
We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent transportation system. Real-time detection and traffic-flow statistics were realized by adjusting the network structure, optimizing the loss function, and introducing weight regularization. This model, which we call YOLO-UA, was initialized based on the weight of a YOLO model pre-trained while using the VOC2007 data set. The UA-CAR data set with complex weather conditions was used for training, and better model parameters were selected through tests and subsequent adjustments. The experimental results showed that, for different weather scenarios, the accuracy of the YOLO-UA was ~22% greater than that of the YOLO model before optimization, and the recall rate increased by about 21%. On both cloudy and sunny days, the accuracy, precision, and recall rate of the YOLO-UA model were more than 94% above the floating rate, which suggested that the precision and recall rate achieved a good balance. When used for video testing, the YOLO-UA model yielded traffic statistics with an accuracy of up to 100%; the time to count the vehicles in each frame was less than 30 ms and it was highly robust in response to changes in scenario and weather.
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