Fault detection and data prediction for

In the last few years, Wireless Sensor Networks (WSNs) have been extensively used as a pervasive sensing module of Ambient Intelligence (AmI) systems in sev-eral application fields, thanks to their versatility and ability to monitor diverse environmental quantities. Although wireless sensor nodes are able to perform on-board computations and to share the sensed data, they are limited by the scarcity of energy resources which heavily influences the network lifetime; moreover, the design phase of a WSN requires testing the application scalability prior to actual deployment. In this regard, this dissertation focuses on data prediction to ad-dress such crucial tasks as prolonging the network lifetime and testing the WSN scalability. Nevertheless, the matter is particularly challenging as the real world measurements are influenced by unpredictable events that affect the sensor read-ings. To this aim, fault detection techniques help to identify corrupt measurements and to discard them before they are actually transmitted within the network, so they may be profitably used to improve the precision of the prediction models