Abstract

One of the key issues in mobile communication system is how to predict the number of calls per each cell. It is an important parameter and usually assumed as random Poisson value. For effective management of cellular network, the average number of calls should be traced and the changes in the numbers need to be promptly detected. In this paper we propose an algorithm detecting the changes in the behavior of the users using the technique proposed for point-of-change problem based only on the number of call arrivals. Computer simulation reveals that the proposed method can effectively detect the discord, and the developed model is very accurate as showing mostly less than 1% differences. Document type: Part of book or chapter of book

Abstract

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models) (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques. Document type: Article