Spatial anomaly detection in sensor networks using neighborhood information

Hedde HWJ Bosman, Giovanni Iacca, Arturo Tejada, Heinrich J. Wörtche, Antonio Liotta
2017 Information Fusion  
The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challenge now is to extract meaningful information from the ever-increasing amount of sensor data collected by WSNs. In particular, there is strong interest in algorithms capable of automatic detection of patterns, events or other out-of-the order,
more » ... s system behavior. Data anomalies may indicate states of the system that require further analysis or prompt actions. Traditionally, anomaly detection techniques are executed in a central processing facility, which requires the collection of all measurement data at a central location, an obvious limitation for WSNs due to the high data communication costs involved. In this paper we explore the extent by which one may depart from this classical centralized paradigm, looking at decentralized anomaly detection based on unsupervised machine learning. Our aim is to detect anomalies at the sensor nodes, as opposed to centrally, to reduce energy and spectrum consumption. We study the information gain coming from aggregate neighborhood data, in comparison to performing simple, in-node anomaly detection. We evaluate the effects of neighborhood size and spatio-temporal correlation on the performance of our new neighborhood-based approach using a range of real-world network deployments and datasets. We find the conditions that make neighborhood data fusion advantageous, identifying also the cases in which this approach does not lead to detectable improvements. Improvements are linked to the diffusive properties of data (spatio-temporal correlations) but also to the type of sensors, anomalies and network topological features. Overall, when a dataset stems from a similar mixture of diffusive processes precision tends to benefit, particularly in terms of recall. Our work paves the way towards understanding how distributed data fusion methods may help managing the complexity of wireless sensor networks, for instance in massive Internet of Things scenarios. (H.H. Bosman). applications of these systems can be found, for instance, in home automation, automated transportation, or large scale environmental data collection [1] . While at present white goods, smart cities and buildings are being equipped with IoT technology [2] , one of the earliest IoT related systems were (and are) wireless sensor networks (WSNs), with typical applications in environmental monitoring [3] and tracking of mobile agents [4] . Such applications usually require numerous sensor nodes to be deployed in remote locations. To make such systems affordable, costs are saved by reducing the quality of the sensors and the hardware resources available on each node (such http://dx.
doi:10.1016/j.inffus.2016.04.007 fatcat:ra4jdegmanfydczn34g43doqvu