[Epsilon] -- DjC: A Disjoint Clique Approach for Data Driven Approximate Bounded Loss Data Collection in Sensor Networks

In this paper we present a data-driven approach for the problem of approximate data collection in sensor networks within the disjoint clique framework. The objective is to obtain estimations of attribute values from sensor nodes within certain error bounds while spending minimal amount of energy. Chu et al. [3] showed that a disjoint clique model is a natural and effective way of exploiting temporal and spatial correlations in the attribute values. However their approach assumes the prior knowledge of probabilistic models representing the sensor data. This often seems to be a tedious task in real world deployments. This paper presents a data driven solution using disjoint clique model which does not assume any such prior knowledge. Using a preliminary experimental evaluation on a real life and a synthetic dataset, we show that the proposed data driven approach is feasible and is easy to deploy.