Trend Cluster based Kriging Interpolation in Sensor Data Networks

Spatio-temporal data collected in sensor networks are often affected by faults due to power outage at nodes, wrong time synchronizations, interference, network transmission failures, sensor hardware issues or high energy consumption during communications. Therefore, acquisition of information by wireless sensor networks is a challenging step in monitoring physical ubiquitous phenomena (e.g. weather, pollution, traffic). This issue gives raise to a fundamental trade-off: higher density of sensors provides more data, higher resolution and better accuracy, but requires more communications and processing. A data mining approach to reduce communication and energy requirements is investigated: the number of transmitting sensors is decreased as much as possible, even keeping a reasonable degree of data accuracy. Kriging techniques and trend cluster discovery are employed to estimate unknown data in any un-sampled location of the space and at any time point of the past. Kriging is a statistical interpolation group of techniques, suited for spatial data, which estimates the unknown data in any space location by a proper weighted mean of nearby observed data. The trend clusters are stream patterns which compactly represent sensor data by means of spatial clusters having prominent data trends in time. Kriging is here applied to estimate unknown data taking into account a spatial correlation model of the sensor network. Trends are used as a guideline to transfer this model across the time horizon of the trend itself. Experiments are performed with a real sensor data network, in order to evaluate this interpolation technique and demonstrate that Kriging and trend clusters outperform, in terms of accuracy, interpolation competitors like Nearest Neighbor or Inverse Distance Weightin