Sensor-based early activity recognition inside buildings to support energy and comfort management systems

Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of 81%. Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about 60%

An adaptive social-aware device-to-device communication mechanism for wireless networks

Device-to-Device (D2D) communication is an essential element in 5G networks, which enables users to communicate either directly without network assistance or with minimum signaling through a base station. For an effective D2D communication, related problems in mode and peer selection need to be addressed. In mode selection, the problem is how to guarantee selection always chooses the best available mode. In peer selection, the problem is how to select optimum peers among surrounding peers in terms of connection conditions and social relationships between peers. The main objectives of this research are to identify mode selection between devices and establishing a connection with the best D2D pair connection without privacy leakage. Multi-Attribute Decision Making and Social Choice theories are applied to achieve the objectives. Mode selection scheme is based on Received Signal Strength (RSS), delay and bandwidth attributes to choose and switch among the available modes intelligently based on the highest ranking. Then, the peering selection scheme is proposed using RSS, delay, bandwidth and power attribute to find an optimum peer with concerning social relationship, by evaluating trust level between peers and excluding the untrusted peers from ranking which will increase the optimum quality of D2D connection. The proposed schemes are validated and tested using MATLAB. Two main scenarios, namely crowded network and user speed were considered to evaluate the proposed mechanism with three existing approaches where the selection is based on a single attribute. The obtained results showed that the proposed mechanism outperforms other approaches in terms of delay, signal to noise ratio, delivery ratio and throughput with better performance up to 70%. The proposed mechanism provides a smooth switching between different modes and employs an automatic peering selection with trusted peers only. It can be applied in different types of network that serves the massive number of users with different movement speed of the user