Wireless Sensor Networks (WSNs) are used to monitor physical or environmental conditions. Due to energy and bandwidth constraints, wireless sensors are prone to packet loss during communication. To overcome the physical constraints ofWSNs, there is an extensive renewed interest in applying data-driven machine learningmethods. In this paper, we present amission-critical surveillance systemmodel for industrial environments. In our proposed system, a decision tree algorithm is installed on a centralized server to predict the wireless channel quality of the wireless sensors. Based on the machine-learning algorithm directives, wireless sensor nodes can proactively adapt their duty cycle to mobility, interference and hidden terminal. Extensive simulation results validate our proposed system. The prediction algorithm shows a classification accuracy exceeding 73%, which allows the duty cycle adaptation algorithm to significantly minimize the delay and energy cost compared to using pure TDMA or CSMA/CA protocols.

Distributed Information Retrieval (DIR) has been around for a while, yet in this talk I will present an approach to using uncooperative DIR in a mobile environment. We called this Unified Mobile Search (UMS). I will present how this concept extends that of DIR by considering fully the context in which the search is carried out and present our first approach to target application selection for mobile devices. I will then expand on this idea toward a conversational assistant for context-aware distributed mobile search, providing a view on what we should expect coming to our mobile phones very soon. Short Bio. Fabio Crestani is a full professor at the Faculty of Informatics of the Universita’ della Svizzera Italiana (USI) in Lugano, Switzerland, since 2007. Previously he was a professor at the University of Strathclyde in Glasgow, UK. He holds a degree in Statistics from the University of Padua (Italy) and a MSc and PhD in Computing Science from the University of Glasgow (UK). Prof. Crestani is an internationally recognized researcher in Information Retrieval, Text Mining, and Digital Libraries. In these areas he has published over two hundred refereed papers on both theoretical and experimental investigations. He has also served in the Organizing and Program Committees of several conferences and the editorial boards of several journals. Aiuto – ho troppi dati! Digital Library Users and Their Challenges With Big Data