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An Online Outlier Detection Technique for Wireless Sensor Networks using Unsupervised Quarter-Sphere Support Vector Machine
"... Abstract—The main challenge faced by outlier detection techniques designed for wireless sensor networks is achieving high detection rate and low false alarm rate while maintaining the resource consumption in the network to a minimum. In this paper, we propose an online outlier detection technique wi ..."
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Cited by 53 (2 self)
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Abstract—The main challenge faced by outlier detection techniques designed for wireless sensor networks is achieving high detection rate and low false alarm rate while maintaining the resource consumption in the network to a minimum. In this paper, we propose an online outlier detection technique with low computational complexity and memory usage based on an unsupervised centered quarter-sphere support vector machine for real-time environmental monitoring applications of wireless sensor networks. The proposed approach is completely local and thus saves communication overhead and scales well with increase of nodes deployed. We take advantage of spatial correlations that exist in sensor data of adjacent nodes to reduce the false alarm rate in real-time. Experiments with both synthetic and real data collected from the Intel Berkeley Research Laboratory show that our technique achieves better mining performance in terms of parameter selection using different kernel functions compared to an earlier offline outlier detection technique designed for wireless sensor networks. I.
Sensor Network Data Fault Types
"... This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scie ..."
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Cited by 32 (0 self)
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This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments. Categories and Subject Descriptors: B.8.1 [Reliability, Testing, and Fault-Tolerance]: Fault
Suelo: Human-assisted Sensing for Exploratory Soil Monitoring Studies
"... Soil contains vast ecosystems that play a key role in the Earth’s water and nutrient cycles, but scientists cannot currently collect the high-resolution data required to fully understand them. In this paper, we present Suelo, an embedded networked sensing system designed for soil monitoring. An impo ..."
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Cited by 14 (2 self)
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Soil contains vast ecosystems that play a key role in the Earth’s water and nutrient cycles, but scientists cannot currently collect the high-resolution data required to fully understand them. In this paper, we present Suelo, an embedded networked sensing system designed for soil monitoring. An important challenge for Suelo is that many soil sensors are inherently fragile and often produce invalid or uncalibrated data. Therefore Suelo is an assisted sensing system: it actively requests the help of a human when necessary to validate, calibrate, repair, or replace sensors. This approach allows us to use available sensors without sacrificing data integrity, while minimizing the human resources required. We tested our system in multiple real soil monitoring deployments and demonstrate that, using human assistance, Suelo produced 91 % fewer false negatives and false positives than common fault detection solutions on these datasets.
Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor Networks
"... Abstract. We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor networks. To address issues of non-stationarity in environmental data streams, each sensor-processor learns statistical dist ..."
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Cited by 10 (0 self)
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Abstract. We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor networks. To address issues of non-stationarity in environmental data streams, each sensor-processor learns statistical distributions of differences between its readings and those of its neighbors, as well as between its current and previous measurements. Scalar physical quantities such as air temperature, soil moisture, and light flux naturally display a large degree of spatiotemporal coherence, which gives a spectrum of fluctuations between adjacent or consecutive measurements with small variances. This feature permits stable estimation over a small state space. The resulting probability distributions of differences, estimated online in real time, are then used in statistical significance tests to identify rare events. Utilizing the spatio-temporal distributed nature of the measurements across the network, these events are classified as single mode failures- usually corresponding to measurement errors at a single sensor- or common mode events. The event structure also allows the network to automatically attribute potential measurement errors to specific sensors and to correct them in real time via a combination of current measurements at neighboring nodes and the statistics of differences between them. Compared to methods that use Bayesian classification of raw data streams at each sensor, this algorithm is more storage-efficient, learns faster, and is more robust in the face of non-stationary phenomena. Field results from a wireless sensor network (Sensor Web) deployed at Sevilleta National Wildlife Refuge are presented. 1
Distributed online simultaneous fault detection for multiple sensors
- in Proc. of 7th Int’l Conf. on Info. Proc. in Sensor Networks (IPSN
, 2008
"... Monitoring its health by detecting its failed sensors is es-sential to the reliable functioning of any sensor network. This paper presents a distributed, online, sequential algorithm for detecting multiple faults in a sensor network. The algorithm works by detecting change points in the correlation ..."
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Cited by 8 (2 self)
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Monitoring its health by detecting its failed sensors is es-sential to the reliable functioning of any sensor network. This paper presents a distributed, online, sequential algorithm for detecting multiple faults in a sensor network. The algorithm works by detecting change points in the correlation statistics of neighboring sensors, requiring only neighbors to exchange information. The algorithm provides guarantees on detection delay and false alarm probability. This appears to be the first work to offer such guarantees for a multiple sensor network. Based on the performance guarantees, we compute a tradeoff between sensor node density, detection delay and energy consumption. We also address synchronization, finite storage and data quantization. We validate our approach with some example applications. 1.
MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models
- VLDB ‘07, September 23-28, 2007, Vienna, Austria
, 2007
"... The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical model ..."
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Cited by 7 (0 self)
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The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical models—Markov Chains (MCs) and Hidden Markov Models (HMMs). We consider the problem of distributed indexing and semantic querying over such sensor models. Specifically, we are interested in efficiently answering (i) range queries: return all sensors that have observed an unusual sequence of symbols with a high likelihood, (ii) top-1 queries: return the sensor that has the maximum probability of observing a given sequence, and (iii) 1-NN queries: return the sensor (model) which is most similar to a query model. All the above queries can be answered at the centralized base station, if each sensor transmits its model to the base station. However, this is communicationintensive. We present a much more efficient alternative—a distributed index structure, MIST (Model-based Index STructure), and accompanying algorithms for answering the above queries. MIST aggregates two or more constituent models into a single composite model, and constructs an in-network hierarchy over such composite models. We develop two kinds of composite models: the first kind captures the average behavior of the underlying models and the second kind captures the extreme behaviors of the underlying models. Using the index parameters maintained at the root of a subtree, we bound the probability of observation of a query sequence from a sensor in the subtree. We also bound the distance of a query model to a sensor model using these parameters. Extensive experimental evaluation on both real-world and synthetic data sets show that the MIST schemes scale well in terms of network size and number of model states. We also show its superior performance over the centralized schemes in terms of update, query, and total communication costs.
TIERED ARCHITECTURE FOR ON-LINE DETECTION, ISOLATION AND REPAIR OF FAULTS IN WIRELESS SENSOR NETWORKS
"... Abstract—Wireless sensor networks fuse data from a multiplicity of sensors of different modalities and spatiotemporal scales to provide information for reconnaissance, surveillance, and situational awareness in many defense applications. For decisions to be based on information returned by sensor ne ..."
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Abstract—Wireless sensor networks fuse data from a multiplicity of sensors of different modalities and spatiotemporal scales to provide information for reconnaissance, surveillance, and situational awareness in many defense applications. For decisions to be based on information returned by sensor networks it is crucial that such information be of sustained high quality. While the Quality of Information (QoI) depends on many factors, perhaps the most crucial is the integrity of the sensor data sources themselves. Even ignoring malicious subversion, sensor data quality may be compromised by nonmalicious causes such as noise, drifts, calibration, and faults. On-line detection and isolation of such misbehaviors is crucial not only for assuring QoI delivered to the end-user, but also for efficient operation and management by avoiding wasted energy and bandwidth in carrying poor quality data and enabling
In-Situ Data Quality Assurance for Environmental Applications of Wireless Sensor Networks
"... We present a local, distributed algorithm to detect measurement errors and infer missing readings in environmental applications of wireless sensor networks. To bypass issues of non-stationarity in environmental data streams, each sensor-processor node learns statistical distributions of differences ..."
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Cited by 4 (0 self)
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We present a local, distributed algorithm to detect measurement errors and infer missing readings in environmental applications of wireless sensor networks. To bypass issues of non-stationarity in environmental data streams, each sensor-processor node learns statistical distributions of differences between its readings and the readings of its neighbors, as well as differences between its current and previous readings. Scalar physical quantities such as air temperature, soil moisture, and light flux naturally display a great degree of spatiotemporal coherence, which in turn leads to a spectrum of fluctuations between adjacent or consecutive measurements characterized by small variances. This permits stable estimation over a small state space. The estimated distributions of differences are then used in statistical significance tests that exclude rare random errors in measurements at any single sensor, and to infer missing readings. Compared to an alternative method based on Bayesian classifiers, our algorithm is more storage-efficient, learns faster, and is more robust in the face of non-stationary phenomena. Field results from a wireless sensor network (Sensor Web) deployed at Sevilleta National Wildlife Refuge are presented.
Article A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
, 2012
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Simultaneous sequential detection of multiple interacting faults,” http://arxiv.org/abs/1012.1258
, 2010
"... Single fault sequential change point problems have become important in modeling for various phenomena in large distributed systems, such as sensor networks. But such systems in many situations present multiple interacting faults. For example, individual sensors in a network may fail and detection is ..."
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Cited by 3 (2 self)
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Single fault sequential change point problems have become important in modeling for various phenomena in large distributed systems, such as sensor networks. But such systems in many situations present multiple interacting faults. For example, individual sensors in a network may fail and detection is performed by comparing measurements between sensors, resulting in statistical dependency among faults. We present a new formulation for multiple interacting faults in a distributed system. The formulation includes specifications of how individual subsystems composing the large system may fail, the information that can be shared among these subsystems and the interaction pattern between faults. We then specify a new sequential algorithm for detecting these faults. The main feature of the algorithm is that it uses composite stopping rules for a subsystem that depend on the decision of other subsystems. We provide asymptotic false alarm and detection delay analysis for this algorithm in the Bayesian setting and show that under certain conditions the algorithm is optimal. The analysis methodology relies on novel detailed comparison techniques between stopping times. We validate the approach with some simulations. I.
