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108
Energy conservation in wireless sensor networks: A survey
"... In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifeti ..."
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Cited by 227 (11 self)
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In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.
MCDB: a Monte Carlo approach to managing uncertain data
, 2008
"... To deal with data uncertainty, existing probabilistic database sys-tems augment tuples with attribute-level or tuple-level probability values, which are loaded into the database along with the data itself. This approach can severely limit the system’s ability to gracefully handle complex or unforese ..."
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Cited by 110 (3 self)
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To deal with data uncertainty, existing probabilistic database sys-tems augment tuples with attribute-level or tuple-level probability values, which are loaded into the database along with the data itself. This approach can severely limit the system’s ability to gracefully handle complex or unforeseen types of uncertainty, and does not permit the uncertainty model to be dynamically parameterized ac-cording to the current state of the database. We introduce MCDB, a system for managing uncertain data that is based on a Monte Carlo approach. MCDB represents uncertainty via “VG functions,” which are used to pseudorandomly generate realized values for un-certain attributes. VG functions can be parameterized on the re-sults of SQL queries over “parameter tables ” that are stored in the database, facilitating what-if analyses. By storing parameters, and not probabilities, and by estimating, rather than exactly com-puting, the probability distribution over possible query answers, MCDB avoids many of the limitations of prior systems. For ex-ample, MCDB can easily handle arbitrary joint probability distri-butions over discrete or continuous attributes, arbitrarily complex SQL queries, and arbitrary functionals of the query-result distri-bution such as means, variances, and quantiles. To achieve good performance, MCDB uses novel query processing techniques, exe-cuting a query plan exactly once, but over “tuple bundles ” instead of ordinary tuples. Experiments indicate that our enhanced func-tionality can be obtained with acceptable overheads relative to tra-ditional systems.
Mobiscopes for human spaces
- IEEE Pervasive Computing
, 2007
"... The proliferation of affordable mobile devices with processing and sensing capabilities, together with the rapid growth in ubiquitous network connectivity, herald an era of Mobiscopes; networked sensing applications that rely on multiple mobile sensors to accomplish global tasks. These distributed s ..."
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Cited by 90 (11 self)
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The proliferation of affordable mobile devices with processing and sensing capabilities, together with the rapid growth in ubiquitous network connectivity, herald an era of Mobiscopes; networked sensing applications that rely on multiple mobile sensors to accomplish global tasks. These distributed sensing systems extend the model of traditional sensor networks, introducing challenges in data management, data integrity, privacy, and network system design. While several applications that fit the above description exist in prior literature, they provide tailored one-time solutions to what essentially is the same set of problems. It is time to work towards a general architecture that identifies common challenges and provides a generalizable methodology for the design of future Mobiscopes. Towards that end, this paper surveys a variety of current and emerging mobile, networked, sensing applications; articulates their common challenges; and provides architectural guidelines and design directions for this important
Infrastructure for data processing in large-scale interconnected sensor networks
- In Mobile Data Management (MDM
, 2007
"... sensor networks ..."
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Online filtering, smoothing and probabilistic modeling of streaming data
- in ICDE
, 2008
"... In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify ..."
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Cited by 69 (3 self)
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In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms commonly used to implement dynamic probabilistic models, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new readings arrive. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over sensor data from the Intel Lab dataset that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of such tight integration between dynamic probabilistic models and database systems. 1
BAYESSTORE: Managing Large, Uncertain Data Repositories with Probabilistic Graphical Models
"... Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications must constantly reason about volumes of noisy sensory readings for a variety of reasons, including motion prediction and hum ..."
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Cited by 60 (1 self)
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Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications must constantly reason about volumes of noisy sensory readings for a variety of reasons, including motion prediction and human behavior modeling. Such probabilistic data analyses require sophisticated machine-learning tools that can effectively model the complex spatio/temporal correlation patterns present in uncertain sensory data. Unfortunately, to date, most existing approaches to probabilistic database systems have relied on somewhat simplistic models of uncertainty that can be easily mapped onto existing relational architectures: Probabilistic information is typically associated with individual data tuples, with only limited or no support for effectively capturing and reasoning about complex data correlations. In this paper, we introduce BAYESSTORE, a novel probabilistic data management architecture built on the principle of handling statistical models and probabilistic inference tools as first-class citizens of the database system. Adopting a machine-learning view, BAYESSTORE employs concise statistical relational models to effectively encode the correlation patterns between uncertain data, and promotes probabilistic inference and statistical model manipulation as part of the standard DBMS operator repertoire to support efficient and sound query processing. We present BAYESSTORE’s uncertainty model based on a novel, first-order statistical model, and we redefine traditional query processing operators, to manipulate the data and the probabilistic models of the database in an efficient manner. Finally, we validate our approach, by demonstrating the value of exploiting data correlations during query processing, and by evaluating a number of optimizations which significantly accelerate query processing. 1
Database Support for Probabilistic Attributes and Tuples
- In IEEE 24th Intl. Conference on Data Engineering
, 2008
"... Abstract — The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous an ..."
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Cited by 33 (6 self)
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Abstract — The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous and discrete data domains. This paper presents a model for handling arbitrary probabilistic uncertain data (both discrete and continuous) natively at the database level. Our approach leads to a natural and efficient representation for probabilistic data. We develop a model that is consistent with possible worlds semantics and closed under basic relational operators. This is the first model that accurately and efficiently handles both continuous and discrete uncertainty. The model is implemented in a real database system (PostgreSQL) and the effectiveness and efficiency of our approach is validated experimentally. I.
Querying continuous functions in a database system
- In ACM SIGMOD
, 2008
"... Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applic ..."
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Cited by 26 (0 self)
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Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applications – e.g., in a sensor network, it is necessary to interpolate sensor readings to predict values at locations where sensors are not deployed. In other situations, raw data can be inaccurate owing to measurement errors, and it is useful to fit continuous functions to raw data and query the functions, rather than raw data itself – e.g., fitting a smooth curve to noisy sensor readings, or a smooth trajectory to GPS data containing gaps or outliers. Existing databases do not support storing or querying continuous functions, short of brute-force discretization of functions into a collection of tuples. We present FunctionDB, a novel database
Data Management in the Worldwide Sensor Web
"... Harvesting the benefits of a sensor-rich world presents many data management challenges. Recent advances in research and industry aim to address these challenges. ..."
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Cited by 24 (1 self)
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Harvesting the benefits of a sensor-rich world presents many data management challenges. Recent advances in research and industry aim to address these challenges.
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.
