Integrating Trend Clusters for Spatio-temporal Interpolation of Missing Sensor Data

Information acquisition in a pervasive sensor network is often affected by faults due to power outage at nodes, wrong time synchronizations, interference, network transmission failures, sensor hardware issues or excessive energy consumption for communications. These issues impose a trade-off between the precision of the measurements and the costs of communication and processing which are directly proportional to the number of sensors and/or transmissions. We present a spatio-temporal interpolation technique which allows an accurate estimation of sensor network missing data by computing the inverse distance weighting of the trend cluster representation of the transmitted data. The trend-cluster interpolation has been evaluated in a real climate sensor network in order to prove the efficacy of our solution in reducing the amount of transmissions by guaranteeing accurate estimation of missing data.