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Omnibus outlier detection in sensor networks using windowed locality sensitive hashing

Giatrakos Nikolaos, Deligiannakis Antonios, Garofalakis Minos, Kotidis, Yannis

Πλήρης Εγγραφή

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Περίληψη

Wireless Sensor Networks (WSNs) have become an integral part of cutting edge technological paradigms such as the Internet-of-Things (IoT) which incorporates a variety of smart application scenarios. WSNs include tiny sensors (motes), with constrained hardware capabilities and limited power supply that can collaboratively function in an unsupervised manner for a long period of time. Their purpose is to continuously monitor quantities of interest and provide answers to application queries. Sensor data streams are inherently spatiotemporal in nature, both because mote measurements form multidimensional time series and due to the spatial reference on the data based on the realm sensed by a mote. Motes are designed to be inexpensive, and thus sensory hardware is prone to temporary or permanent failures yielding faulty measurements. Such measurements may unpredictably forge a query answer, while truthful but abnormal mote samples may indicate undergoing phenomena. Therefore, outlier detection in sensor networks is of utmost importance. With limited power supply and communication being by far the main culprit in energy drain, outlier detection techniques in WSNs should achieve appropriate balance between reducing communication and providing real-time, continuously updated outlier reports. Prior works employ probabilistic or best effort approaches to accomplish the task, which either unpredictably compromise outlier detection accuracy or fail to explicitly tune the amount of communicated data. In this work, we introduce an omnibus outlier detection solution over spatiotemporally referenced sensor data that is capable of: (a) directly trading communication reduction for outlier detection quality with predictable accuracy guarantees, (b) accommodating both uni- and multi-dimensional outlier definitions, (c) operating under various streaming window models and (d) incorporating a wide variety of similarity measures to judge outliers.

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