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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
On the prevalence of sensor faults in real-world deployments
- In In Proc. of SECON
, 2007
"... Abstract—Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different c ..."
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Cited by 23 (4 self)
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Abstract—Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict “normal ” sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Finally, learning-based methods are trained to statistically identify classes of faults. We find that these three classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. We apply these techniques to four real-world sensor data sets and find that the prevalence of faults as well as their type varies with data sets. All three methods are qualitatively consistent in identifying sensor faults in real world data sets, lending credence to our observations. Our work is a first-step towards automated on-line fault detection and classification. I.
Context-aware sensors
- In EWSN
, 2004
"... Abstract. Wireless sensor networks typically consist of a large number of sensor nodes embedded in a physical space. Such sensors are low-power devices that are primarily used for monitoring several physical phenomena, potentially in remote harsh environments. Spatial and temporal dependencies betwe ..."
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Cited by 23 (0 self)
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Abstract. Wireless sensor networks typically consist of a large number of sensor nodes embedded in a physical space. Such sensors are low-power devices that are primarily used for monitoring several physical phenomena, potentially in remote harsh environments. Spatial and temporal dependencies between the readings at these nodes highly exist in such scenarios. Statistical contextual information encodes these spatio-temporal dependencies. It enables the sensors to locally predict their current readings based on their own past readings and the current readings of their neighbors. In this paper, we introduce context-aware sensors. Specifically, we propose a technique for modeling and learning statistical contextual information in sensor networks. Our approach is based on Bayesian classifiers; we map the problem of learning and utilizing contextual information to the problem of learning the parameters of a Bayes classifier, and then making inferences, respectively. We propose a scalable and energy-efficient procedure for online learning of these parameters in-network, in a distributed fashion. We discuss applications of our approach in discovering outliers and detection of faulty sensors, approximation of missing values, and in-network sampling. We experimentally analyze our approach in two applications, tracking and monitoring. 1
A weighted moving average-based approach for cleaning sensor data
- in IEEE International Conference on Distributed Computing Systems
, 2007
"... Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environment, however, it is well known that the collected sensor data usually contain noises. Therefore, it is very critical to clean the sensor data ..."
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Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environment, however, it is well known that the collected sensor data usually contain noises. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confident data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide a higher confidence weight for this value, such that this important value can be quickly reflected in the mon-itoring values computed from moving average. Based on our extensive 1 simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer fast response time. 1
Supporting generalized context interactions
- In: Proceedings of the 4 th International Workshop on Software Engineering for Middleware
, 2004
"... Abstract. Context-awareness refers to a computing model where application behavior is driven by a continually-changing environment. Mobile computing poses unique challenges to context-sensitive applications and middleware, including the ability to run on resource-poor devices like PDAs and the neces ..."
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Cited by 11 (8 self)
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Abstract. Context-awareness refers to a computing model where application behavior is driven by a continually-changing environment. Mobile computing poses unique challenges to context-sensitive applications and middleware, including the ability to run on resource-poor devices like PDAs and the necessity to limit assumptions about the underlying network. Though middleware exists to provide context-awareness to applications, they have not been designed with the limitations inherent in dynamic mobile environments in mind. This paper discusses a lightweight approach to context-sensitivity that takes into account these considerations. We explore the use of modularization to tailor service discovery policies for specific applications, as well as leveraging existing language constructs for simplifying the creation and aggregation of different context types. We also discuss a Java implementation of these concepts, along with three sample applications that can automatically propagate changes in context to clients running on devices varying from mobile phones to desktop computers. 1
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
Sensor node selection for execution of continuous probabilistic queries in wireless sensor networks
- in Proceedings of ACM Workshop on Video Surveillance and Sensor Networks
, 2004
"... Due to the error-prone properties of sensors, it is important to use multiple low-cost sensors to improve the reliability of query results. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are execute ..."
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Cited by 9 (2 self)
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Due to the error-prone properties of sensors, it is important to use multiple low-cost sensors to improve the reliability of query results. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are executed. Further, we need to distinguish effectively which sensors are not working properly and discard them from being used. In this paper, we propose a probabilistic approach to decide what sensor nodes to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensor nodes, the query type, and the probabilistic requirement on the query result, we derive a simple method to select an appropriate set of sensors to provide reliable answers. We examine a wide range of common aggregate queries: average, sum, minimum, maximum, and range count query, but we believe our method can be extended to other query types. Our goal is to minimize sensor data aggregation workload in a network environment and at the same time meet the probabilistic requirement of the CPQ.
Model based error correction for wireless sensor networks
- In Proceedings of the First IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON). IEEE Computer Society
, 2004
"... Abstract — One of the main challenges in wireless sensor networks is to provide low-cost, low-energy reliable data collection. Reliability against transient errors in sensor data can be provided using the model-based error correction described in [1], in which temporal correlation in the data is use ..."
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Cited by 8 (1 self)
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Abstract — One of the main challenges in wireless sensor networks is to provide low-cost, low-energy reliable data collection. Reliability against transient errors in sensor data can be provided using the model-based error correction described in [1], in which temporal correlation in the data is used to correct errors without any overheads at the sensor nodes. In the above work it is assumed that a perfect model of the data is available. However, as variations in the physical process are context-dependent and time-varying in a real sensor network, it is infeasible to have an accurate model of the data properties a priori, thus leading to reduced correction efficiency. In this paper, we address this issue by presenting a scalable methodology for improving the accuracy of data modeling through on-line estimation and model updates. Additionally, we propose enhancements to the data correction algorithm to incorporate robustness against dynamic model changes and potential modeling errors. We evaluate our system through simulations using real sensor data collected from different sources. Experimental results demonstrate that the proposed enhancements lead to an improvement of up to a factor of 10 over the earlier approach. I.
Model-driven accuracy bounds for noisy sensor readings
- in: Proceedings of the 9th International Conference on Distributed Computing in Sensor Systems (DCOSS), IEEE
"... Abstract—Wireless sensor networks are increasingly used in application scenarios where a high data quality is inevitable, e.g., the control of industrial production areas. Nevertheless, many deployments must live with strict constraints regarding the sensing hardware and may not employ newest sensin ..."
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Cited by 7 (3 self)
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Abstract—Wireless sensor networks are increasingly used in application scenarios where a high data quality is inevitable, e.g., the control of industrial production areas. Nevertheless, many deployments must live with strict constraints regarding the sensing hardware and may not employ newest sensing tech-nologies, e.g., due to limited energy budget, size, and bandwidth. Additionally, many applications would benefit from not only gathering absolute sensor readings but also knowing the quality of their low-cost sensor measurements. In this paper, we introduce a model-driven approach that (i) provides reliable accuracy bounds for individual noisy sensor readings and (ii) detects systematic and transient sensor errors. We apply our method to static and mobile real-world deployments of noisy and unstable low-cost sensors by analyzing large sets of urban temperature and ozone measurements. We find that the proposed algorithm successfully calculates precise accuracy bounds. We compare them to measurements of high-quality instruments and show that up to 96 % of the reference measurements are inside the computed accuracy bounds in the static scenario and up to 94 % in the mobile scenario. This is surprisingly high for the used low-cost sensors. By analyzing data from our static long-term deployment, we reveal that the ozone sensor’s reliability is dependent on seasonal weather conditions. I.
Research Directions in Sensor Data Streams: Solutions and Challenges
"... A typical framework of sensor streams is data obtained from wireless networks of sensors, embedded in a physical space, continuously communicating a stream of data to a database. These wireless networks typically consist of large number of low-power and limited-bandwidth devices. They are primarily ..."
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Cited by 5 (0 self)
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A typical framework of sensor streams is data obtained from wireless networks of sensors, embedded in a physical space, continuously communicating a stream of data to a database. These wireless networks typically consist of large number of low-power and limited-bandwidth devices. They are primarily used for monitoring of several physical phenomenon such as, contamination, climate, building structure, etc., potentially in remote harsh environments. Research in sensor streaming has been generally focused on ultimate utilization of such devices given their limited resources and unattended deployment. This paper surveys current research directions in sensor data streams. In particular, it emphasizes existing work on storage and gathering of sensor data, architectures for querying sensor streams, and handling of erroneous sensors. It also highlights some open problems and discusses research paths to pursue in this exciting research area. 1
