@Incollection{Bic12b,
   joint-pub = {false},
   status = {public},
   task = {T3.1, T3.3},
   publisher = {Springer Verlag},
   booktitle = {Computational Intelligence in Sensor Networks},
   address = {Berlin (D)},
   reported = {year1},
   year = {2012},
   invited = {yes},
   timestamp = {2012.10.31},
   main = {no},
   accessible = {true},
   title = {{In-Network Aggregation of High-Level Sensorial Knowledge for Environment-aware Services and Ensembles}},
   editor = {Misra Bijan},
   author = {Nicola Bicocchi and Marco Mamei and Franco Zambonelli},
   period = {year1},
   abstract = {Emerging services and applications for mobile and pervasive computing require the availability of expressive knowledge about their environment to enable service components (or ensembles of them) to effectively adapt their behavior. With regard to sensor networks, proper mechanisms to effectively extract general-purpose high-level knowledge in a distributed way are still missing. This chapter presents a first step in in this direction. The basic idea is to have a sensor network partition- ing itself in correspondence of spatial regions characterized by similar patterns, en- abling distributed aggregation of sensorial data on a per-region basis. The result of this process is that a sensor network can be modeled as made up of virtual macro sensors, each associated to a well-characterized region of the physical environment, and each capable of reporting expressive information about the situations in its re- gion. This makes it possible for service components to easily access such global data, and for distributed service component ensembles to share coherent knowledge about the region. These properties are particularly useful in a number of emerging scenarios in which humans, robots, or vehicles, are in need of accessing knowledge about their current environment.},
   owner = {kroiss},
   ascens_ref = {true},
   partner = {UNIMORE},
   wp = {WP3}
}