Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes.

In this paper we investigated the adaptive modulation strategies for Multicast service in orthogonal frequency division multiple access systems. We defined a Reward function as the performance optimization target and developed adaptive modulation strategies to maximize this Reward function. The proposed optimization algorithm varied the instantaneous BER constraint of each mobile Multicast receiver according to its individual cumulated BER, which resulted in a significant Reward gain.

Remote access to dynamically changing information elements is a required functionality for various network services, including routing and instances of context-sensitive networking. Three fundamentally different strategies for such access are investigated in this paper: (1) a reactive approach initiated by the requesting entity, and two versions of proactive approaches in which the entity that contains the information element actively propagates its changes to potential requesters, either (2) periodically or triggered by changes of the information element (3). This paper first develops a set of analytic models to compute different performance metrics for these approaches, with special focus on the so-called mismatch probability. The results of the analytic models allow for design decisions on which strategy to implement for specific input parameters (change rate of the information element, network delay characterization) and specific requirements on mismatch probability, traffic overhead, and access delay. Finally, the analysis is applied to the use-case of context-sensitive service discovery.

Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.