Smart grid technologies : communication technologies and standards

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=942

A survey on smart grid potential applications and communication requirements

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424hb2016Electrical, Electronic and Computer Engineerin

Immersive interconnected virtual and augmented reality : a 5G and IoT perspective

Despite remarkable advances, current augmented and virtual reality (AR/VR) applications are a largely individual and local experience. Interconnected AR/VR, where participants can virtually interact across vast distances, remains a distant dream. The great barrier that stands between current technology and such applications is the stringent end-to-end latency requirement, which should not exceed 20 ms in order to avoid motion sickness and other discomforts. Bringing AR/VR to the next level to enable immersive interconnected AR/VR will require significant advances towards 5G ultra-reliable low-latency communication (URLLC) and a Tactile Internet of Things (IoT). In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality

QoE evaluation in adaptive streaming: enhanced MDT with deep learning

We propose an architecture for performing virtual drive tests for mobile network performance evaluation by facilitating radio signal strength data from user equipment. Our architecture comprises three main components: (i) pattern recognizer that learns a typical (nominal) behavior for application KPIs (key performance indicators); (ii) predictor that maps from network KPIs to application KPIs; (iii) anomaly detector that compares predicted application performance with said typical pattern. To simulate user-traces, we utilize a commercial state-of-the-art network optimization tool, which collects application and network KPIs at different geographical locations at various times of the day, to train an initial learning model. Although the collected data is related to an adaptive video streaming application, the proposed architecture is flexible, autonomous and can be used for other applications. We perform extensive numerical analysis to demonstrate key parameters impacting video quality prediction and anomaly detection. Playback time is shown to be the most important parameter affecting video quality, most likely due to video packet buffering during playback. We additionally observe that network KPIs, which characterize the cellular connection strength, improve QoE (quality of experience) estimation in anomalous cases diverging from the nominal. The efficacy of our approach is demonstrated with a mean-maximum F1-score of 77%

Predicting the Next Location Change and Time of Change for Mobile Phone Users

Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results