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32
BSAVE: Sensor anomaly visualization engine
- in Proc. IEEE Conf. VAST
"... Figure 1: SAVE user interface visualizing sensor network topology (top-left, bottom-left), high-dimensional sensor measurements and statuses (top-right), dimension correlations (middle of bottom) and the dimension temporal trends (bottom-right). Various visual settings and analytic functionalities c ..."
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Figure 1: SAVE user interface visualizing sensor network topology (top-left, bottom-left), high-dimensional sensor measurements and statuses (top-right), dimension correlations (middle of bottom) and the dimension temporal trends (bottom-right). Various visual settings and analytic functionalities can be accessed from the menu. Diagnosing a large-scale sensor network is a crucial but challeng-ing task. Particular challenges include the resource and bandwidth constraints on sensor nodes, the spatiotemporally dynamic network behaviors, and the lack of accurate models to understand such be-haviors in a hostile environment. In this paper, we present the Sensor Anomaly Visualization Engine (SAVE), a system that fully leverages the power of both visualization and anomaly detection analytics to guide the user to quickly and accurately diagnose sen-sor network failures and faults. SAVE combines customized visu-alizations over separate sensor data facets as multiple coordinated views. Temporal expansion model, correlation graph and dynamic projection views are proposed to effectively interpret the topolog-ical, correlational and dimensional sensor data dynamics and their anomalies. Through a case study with real-world sensor network system and administrators, we demonstrate that SAVE is able to help better locate the system problem and further identify the root cause of major sensor network failure scenarios.
TriopusNet: Automating Wireless Sensor Network Deployment and Replacement in Pipeline Monitoring
"... This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet ..."
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This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet runs a sensor deployment algorithm to determine node placement. While a node is flowing inside the pipeline, it performs placement by extending its mechanical arms to latch itself onto the pipe’s inner surface. By continuously releasing nodes into pipes, the TriopusNet system builds a wireless network of interconnected sensor nodes. When a node runs at a low battery level or experiences a fault, the TriopusNet system releases a fresh node from the repository and performs a node replacement algorithm to replace the failed node with the fresh one. We have evaluated the TriopusNet system by creating and collecting real data from an experimental pipeline testbed. Comparing with the nonautomated static deployment, TriopusNet is able to use less sensor nodes to cover a sensing area in the pipes while maintaining network connectivity among nodes with high data collection rate. Experimental results also show that TriopusNet can recover from the network disconnection caused by a battery-depleted node and successfully replace the battery-depleted node with a fresh node.
Article An Adaptive Fault-Tolerant Event Detection Scheme for Wireless Sensor Networks
, 2010
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Trustworthiness Analysis of Sensor Data in Cyber-Physical Systems
"... A Cyber-Physical System (CPS) is an integration of sensor networks with informational devices. CPS can be used for many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One key issue in CPS research is trustworthiness analysis of sen ..."
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A Cyber-Physical System (CPS) is an integration of sensor networks with informational devices. CPS can be used for many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One key issue in CPS research is trustworthiness analysis of sensor data. Due to technology limitations and environmental influences, the sensor data collected by CPS are inherently noisy and may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this study, we propose a method called Tru-Alarm, which increases the capability of a CPS to recognize trustworthy alarms. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inference based on the graph links. The study also reveals that the alarm trustworthiness and sensor reliability could be mutually enhanced. The property is used to help prune the large search space of object-alarm graph, filter out the alarms generated by unreliable sensors and improve the algorithm’s efficiency. Extensive experiments are conducted on both real and synthetic datasets, and the results show that Tru-Alarm filters out noise and false information efficiently and effectively, while ensuring that no meaningful alarms are missed. Keywords: alarm
Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues
, 2013
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Being SMART About Failures: Assessing Repairs in Activity Detection
"... Abstract—One of the main challenges for activity recognition systems is that there are numerous sensors which are likely to move or exhibit failures due to hardware degradation, inaccurate readings, and environmental changes. In this paper, we propose a Simultaneous Multi-classifier Activity Recogni ..."
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Abstract—One of the main challenges for activity recognition systems is that there are numerous sensors which are likely to move or exhibit failures due to hardware degradation, inaccurate readings, and environmental changes. In this paper, we propose a Simultaneous Multi-classifier Activity Recognition Technique (SMART) that uses application-level semantics to detect sensor node failures and improve the detection accuracy under those failures. Once a node failure is detected, instead of immediately dispatching maintenance, SMART evaluates the severity of the failure by using data replay analysis. Maintenance is dispatched only if the severity analysis indicates that the node failure would have caused an application-level failure in the past and the system could not have recovered from it by updating the classifier ensemble it is using. Evaluation of SMART on a set of activities from two public datasets shows that SMART decreases the number of maintenance dispatches by 45 % on average and almost triples the mean time to failure of the application. SMART identifies all applicationlevel failures at run time and improves the activity detection accuracy under node failures by more than 70%. Keywords-wireless sensor networks; activity detection; machine learning; failure analysis I.
Data discrimination in fault-prone sensor networks
"... While sensor networks have been used in various applications because of the automatic sensing capability and ad-hoc organization of sensor nodes, the fault-prone characteristic of sensor networks has challenged the event detection and the anomaly detection which, to some extent, have neglected the i ..."
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While sensor networks have been used in various applications because of the automatic sensing capability and ad-hoc organization of sensor nodes, the fault-prone characteristic of sensor networks has challenged the event detection and the anomaly detection which, to some extent, have neglected the importance of discriminating events and errors. Considering data uncertainty, in this article, we present the problem of data discrimination in fault-prone sensor networks, analyze the similarities and the differences between events and errors, and design a multi-level systematic discrimination framework. In each step, the framework filters erroneous data from the raw data and marks potential event samples for the next-step processing. The raw data set D is finally partitioned into three subsets, Devent, Derror and Dordinary. Both the scenario-based simulations and the experiments on real-sensed data are carried out. The statistical results of various discrimination metrics demonstrate high distinction ratio as well as the robustness in different cases of the network.
Visualizing Anomalies in Sensor Networks
"... Diagnosing a large-scale sensor network is a crucial but challenging task due to the spatiotemporally dynamic network behaviors of sensor nodes. In this demo, we present Sensor Anomaly Visualization Engine (SAVE), an integrated system that tackles the sensor network diagnosis problem using both visu ..."
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Diagnosing a large-scale sensor network is a crucial but challenging task due to the spatiotemporally dynamic network behaviors of sensor nodes. In this demo, we present Sensor Anomaly Visualization Engine (SAVE), an integrated system that tackles the sensor network diagnosis problem using both visualization and anomaly detection analytics to guide the user quickly and accurately diagnose sensor network failures. Temporal expansion model, correlation graphs and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a real-world large-scale wireless sensor network deployment (GreenOrbs), we demonstrate that SAVE is able to help better locate the problem and further identify the root cause of major sensor network failures.
An exploration of the life cycle of escience collaboratory data
- In iConference ’08
, 2008
"... The success of eScience research depends not only upon effective collaboration between scientists and technologists but also upon the active involvement of information scientists. Archivists rarely receive scientific data until findings are published, by which time important information about their ..."
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The success of eScience research depends not only upon effective collaboration between scientists and technologists but also upon the active involvement of information scientists. Archivists rarely receive scientific data until findings are published, by which time important information about their origins, context, and provenance may be lost. Research reported here addresses the lifecycles of data from ecological research with embedded networked sensing technologies. A better understanding of these processes will enable information scientists to participate in earlier stages of the life cycle and to improve curation of these types of scientific data. Evidence from our interview study and field research yields a nine lifecycle phases, and three types of lifecycle depending on the research goal. Findings include highlighting the impact of collaboration on the research processes and potential phases during which the integrity of the captured data is compromised.
In-network Sensor Data Modelling Methods for Fault Detection
"... Abstract. Wireless sensor networks are attracting increasing interest but su↵er from severe challenges such as low data reliability. To improve the data reliability, many sensor fault detection techniques have been proposed. Behind these methods, mathematical models are usually em-ployed to serve as ..."
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Abstract. Wireless sensor networks are attracting increasing interest but su↵er from severe challenges such as low data reliability. To improve the data reliability, many sensor fault detection techniques have been proposed. Behind these methods, mathematical models are usually em-ployed to serve as comparing metric to find faulty data in the absence of ground truth. In this paper, we firstly discuss sensor data features and their relevance to fault detection. Criteria that should be met to become a competent data model for the purpose of fault detection is summarised. Some existing sensor data modelling methods for fault detection are pre-sented and qualitatively compared. 1
