Outlier-Aware Data Aggregation in Sensor Networks

Abstract- In this paper we discuss a robust aggregation framework that can detect spurious measurements and refrain from incorporating them in the computed aggregate values. Our framework can consider different definitions of an outlier node, based on a specified minimum support. Our experimental evaluation demonstrates the benefits of our approach. I

Another Outlier Bites the Dust: Computing Meaningful Aggregates in Sensor Networks

Abstract — Recent work has demonstrated that readings pro-vided by commodity sensor nodes are often of poor quality. In order to provide a valuable sensory infrastructure for monitoring applications, we first need to devise techniques that can withstand “dirty ” and unreliable data during query processing. In this paper we present a novel aggregation framework that detects suspicious measurements by outlier nodes and refrains from incorporating such measurements in the computed aggregate values. We consider different definitions of an outlier node, based on the notion of a user-specified minimum support, and discuss techniques for properly routing messages in the network in order to reduce the bandwidth consumption and the energy drain during the query evaluation. In our experiments using real and synthetic traces we demonstrate that: (i) a straightfor-ward evaluation of a user aggregate query leads to practically meaningless results due to the existence of outliers; (ii) our techniques can detect and eliminate spurious readings without any application specific knowledge of what constitutes normal behavior; (iii) the identification of outliers, when performed inside the network, significantly reduces bandwidth and energy drain compared to alternative methods that centrally collect and analyze all sensory data; and (iv) we can significantly reduce the cost of the aggregation process by utilizing simple statistics on outlier nodes and reorganizing accordingly the collection tree. I

Relation-Based Similarity

: Similarity queries constitute an active area in spatial query processing. The paper addresses the problem of qualitative similarity based on spatial relations. On the one hand spatial similarity entails mechanisms for representing and reasoning on spatial relations, while on the other it introduces a high level of uncertainty. Several spatial queries can be rather fuzzy and user or application dependent. Moreover, relations such as near, northeast etc. lack universally accepted semantics, and as a result their processing in Spatial Databases and GIS has to provide a high level of flexibility in order to satisfy real-life needs. Our work extends the notion of conceptual neighbourhood (originally defined for 1D space) to include higher dimensions and proposes a unified multiresolution framework for the handling of topological, directional and distance relations. We discuss how object and image similarity queries can be effectively handled and show how uncertainty can be seamlessly inco..

A Provably Efficient Computational Model For Approximate Spatiotemporal Retrieval

The paper is concerned with the effective and efficient processing of spatiotemporal selection queries under varying degrees of approximation. Such queries may employ operators like overlaps, north, during, etc., and their result is a set of entities standing approximately in some spatiotemporal relation with respect to a query object X. The contribution of the present work is twofold: i) it presents a formal mathematical framework for representing multidimensional relations at varying granularity levels, modelling relation approximation through the concept of relation convexity, ii) it subsequently exploits the proposed framework for developing approximate spatiotemporal retrieval mechanisms, combining a set of existing as well as new main memory and secondary memory data structures that achieve either optimal or the best known performance in terms of time and space complexity, for both the static and the dynamic setting