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20
Adaptive cleaning for rfid data streams
, 2006
"... ABSTRACT To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID ..."
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Cited by 101 (0 self)
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ABSTRACT To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.
Mining Uncertain Data with Probabilistic Guarantees
"... Data uncertainty is inherent in applications such as sensor monitoring systems, location-based services, and biological databases. To manage this vast amount of imprecise information, probabilistic databases have been recently developed. In this paper, we study the discovery of frequent patterns and ..."
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Cited by 25 (0 self)
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Data uncertainty is inherent in applications such as sensor monitoring systems, location-based services, and biological databases. To manage this vast amount of imprecise information, probabilistic databases have been recently developed. In this paper, we study the discovery of frequent patterns and association rules from probabilistic data under the Possible World Semantics. This is technically challenging, since a probabilistic database can have an exponential number of possible worlds. We propose two efficient algorithms, which discover frequent patterns in bottom-up and top-down manners. Both algorithms can be easily extended to discover maximal frequent patterns. We also explain how to use these patterns to generate association rules. Extensive experiments, using real and synthetic datasets, were conducted to validate the performance of our methods. Source codes and data are available at:
Probabilistic inference over rfid streams in mobile environments
- In ICDE
, 2009
"... Abstract — Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobil ..."
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Cited by 23 (4 self)
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Abstract — Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements— particle factorization, spatial indexing, and belief compression— for scalable inference over large numbers of objects and highvolume streams. Our experiments show that our approach can offer 54 % error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient. I.
PODS: a new model and processing algorithms for uncertain data streams
- In SIGMOD
, 2010
"... Uncertain data streams, where data is incomplete, imprecise, and even misleading, have been observed in a variety of environments. Feeding uncertain data streams to existing stream systems can produce results of unknown quality, which is of paramount concern to monitoring applications. In this paper ..."
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Cited by 12 (0 self)
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Uncertain data streams, where data is incomplete, imprecise, and even misleading, have been observed in a variety of environments. Feeding uncertain data streams to existing stream systems can produce results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the Pods system that supports uncertain data stream processing for data that is naturally captured using continuous random variables. The Pods system employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for complex relational operators, including aggregates and joins, by exploring advanced statistical theory and approximation techniques. Our evaluation results show that our techniques can achieve high performance in stream processing while satisfying accuracy requirements, and these techniques significantly outperform a state-of-the-art sampling-based method. Furthermore, initial results of a case study show that our modeling and aggregation techniques can allow a tornado detection system to produce better quality results yet with lower execution time. 1.
Issues in Wireless Sensor Networks
"... Abstract—In recent years there has been a growing interest in Wireless Sensor Networks (WSN). Recent advancements in the field of sensing, computing and communications have attracted research efforts and huge investments from various quarters in the field of WSN. Also sensing networks will reveal pr ..."
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Cited by 8 (0 self)
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Abstract—In recent years there has been a growing interest in Wireless Sensor Networks (WSN). Recent advancements in the field of sensing, computing and communications have attracted research efforts and huge investments from various quarters in the field of WSN. Also sensing networks will reveal previously unobserved phenomena. The various areas where major research activities going on in the field of WSN are deployment, localization, synchronization, data aggregation, dissemination, database querying, architecture, middleware, security, designing less power consuming devices, abstractions and higher level algorithms for sensor specific issues. This paper provides an overview of ongoing research activities, various design issues involved and possible solutions incorporating these issues. This paper provides a cursory look at each and every topic in WSN and our main aim is to introduce a newbie to the field of WSN and make him understand the various topics of interest available for research. Index Terms—Sensor networks, issues, challenges, research areas, sensor problems.
Efficient Data Interpretation and Compression over RFID Streams
"... challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply-chain environments. Our results show that our ..."
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Cited by 8 (2 self)
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challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply-chain environments. Our results show that our inference techniques provide good accuracy while retaining efficiency, and our compression algorithm yields significant reduction in data volume. I.
Resource-efficient and Reliable Long Term Wireless Monitoring of the Photoplethysmographic Signal
- In submitted to Wireless Health 2011. IEEE
"... Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse ..."
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Cited by 6 (3 self)
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Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse wireless channel with human body-induced path loss and mobility-caused fading. In this paper, we take a generative model-based data collection approach towards achieving energy-efficient and reliable PPG monitoring. We develop two models that can generate synthetic PPG signals given a set of input parameters. These generative models are then used to design and implement a resource-efficient, reliable data reporting method for wireless PPG sensors. We investigate the performance of our method under realistic wireless channel error models and provide methods to improve accuracy at a marginal energy cost. We implement the proposed technique using existing sensor platforms and evaluate its performance on two datasets: MIMIC database and data collected using commercial wearable sensors. Results show significant bandwidth and energy savings due to data transmission reduction. The average reduction in data size for wearable sensor-based data is 300:1, while maintaining a diagnostic accuracy above 94%. 1.
Declarative support for . . .
"... Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development ..."
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Cited by 4 (0 self)
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Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP’s effectiveness and ease of use through three real-world scenarios.
An Adaptive and Composite Spatio-Temporal Data Compression Approach for Wireless Sensor Networks ∗
"... Wireless Sensor Networks (WSN)are often deployedto sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data v ..."
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Cited by 3 (0 self)
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Wireless Sensor Networks (WSN)are often deployedto sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data volumes. Sensor nodes are battery powered and sending the requested large amount of datarapidly depletes their energy. Fortunately,theenvironmentalattributes(e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific measurement and forensics tolerate high latencies for sensor data collection. Accordingly, we develop a fully distributed adaptive technique for spatial and temporal innetwork datacompression with accuracyguarantees. We exploit thespatio-temporal correlation ofsensor readings while benefiting from possible data delivery latency tolerance to further minimize the amount of data to be transported to the sink. Using real data, we demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data. In our simulations, we achieved data compression of up to 95% on the raw data requiring around 5 % of the original data to be transported to the sink.
Effective design of WSNs: from the lab to the real world
"... Abstract — Distributed environmental monitoring with wireless sensor networks (WSNs) is one of the most challenging research activities faced by the embedded system community in the last decade. Here, the need for pervasive, reliable and accurate monitoring systems has pushed the research towards th ..."
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Cited by 2 (0 self)
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Abstract — Distributed environmental monitoring with wireless sensor networks (WSNs) is one of the most challenging research activities faced by the embedded system community in the last decade. Here, the need for pervasive, reliable and accurate monitoring systems has pushed the research towards the realization of credible deployments able to survive in harsh environments for long time. Design an effective WSN requires a good piece of engineer work, not to mention the research contribution needed to provide a credible deployment. As a matter of fact, to solve our application, we are looking for a monitoring framework scalable, adaptive with respect to topological changes in the network, power-aware in its middleware components and endowed with energy harvesting mechanisms to grant a long lifetime for the network. The paper addresses all main aspects related to the design of a WSN ranging from the –possible- need of an ad-hoc embedded system, to sensing, local and remote transmission, data storage and visualization; particular attention will be devoted to energy harvesting and management aspects at the unit and network level. Two applications, namely monitoring the marine environment and forecasting the collapse of rock faces in mountaineering areas will be the experimental leitmotiv of the presentation.
