Energy-aware models for sensor network data acquisition

摘要

Sensor networks are currently exploited to effectively monitor wide areas. To collect measurements describing the state of the monitored environment, different queries are frequently broadcasted to all sensors, providing the best possible approximation of the considered phenomenon. However, when querying a large number of sensors, the collection activity is characterized by high communication cost and energy consumption. Since sensors and sensor data are correlated both in time and space, a subset of nodes may be selected to model the network state. This paper thoroughly describes the SeReNe framework which provides high quality models for sensor networks. The models can be exploited to efficiently acquire sensor data by minimizing communication cost. SeReNe exploits clustering techniques to discover spatial and temporal correlations which allow the identification of sets of correlated sensors and sensor data streams. Given clusters of correlated sensors, a subset of representative sensors, which best model each cluster, has been identified. Representative sensors are then queried instead of the whole network thus reducing communication cost and extending the sensor lifetime. Experiments performed on a real dataset demonstrate the adaptability and the effectiveness of the SeReNe framework in providing energy-aware sensor network models.