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148
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.
Paq: Time series forecasting for approximate query answering in sensor networks
- In EWSN
"... Abstract. In this paper, we present a method for approximating the values of sensors in a wireless sensor network based on time series forecasting. More specifically, our approach relies on autoregressive models built at each sensor to predict local readings. Nodes transmit these local models to a s ..."
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Cited by 107 (2 self)
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Abstract. In this paper, we present a method for approximating the values of sensors in a wireless sensor network based on time series forecasting. More specifically, our approach relies on autoregressive models built at each sensor to predict local readings. Nodes transmit these local models to a sink node, which uses them to predict sensor values without directly communicating with sensors. When needed, nodes send information about outlier readings and model updates to the sink. We show that this approach can dramatically reduce the amount of communication required to monitor the readings of all sensors in a network, and demonstrate that our approach provides provablycorrect, user-controllable error bounds on the predicted values of each sensor. 1
Constraint-chaining: On energy-efficient continuous monitoring in sensor networks
- In SIGMOD
, 2006
"... Wireless sensor networks have created new opportunities for data collection in a variety of scenarios, such as environmental and industrial, where we expect data to be temporally and spatially correlated. Researchers may want to continuously collect all sensor data from the network for later analysi ..."
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Cited by 72 (4 self)
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Wireless sensor networks have created new opportunities for data collection in a variety of scenarios, such as environmental and industrial, where we expect data to be temporally and spatially correlated. Researchers may want to continuously collect all sensor data from the network for later analysis. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. We demonstrate how both types can be combined for maximal benefit. We frame the problem as one of monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes ’ values changes. The set of reports collected at the base station is used to derive all node values. We fully exploit the potential of this global inference in our algorithm, CONCH, short for constraint chaining. Constraint chaining builds a network of constraints that are maintained locally, but allow a global view of values to be maintained with minimal cost. Network failure complicates the use of suppression, since either causes an absence of reports. We add enhancements to CONCH to build in redundant constraints and provide a method to interpret the resulting reports in case of uncertainty. Using simulation we experimentally evaluate CONCH’s effectiveness against competing schemes in a number of interesting scenarios. 1
PRESTO: Feedback-driven Data Management in Sensor Networks
, 2006
"... This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of ..."
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Cited by 41 (10 self)
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This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to the proxy. Such a model-driven push approach is energyefficient, while ensuring that anomalous data trends are never missed. In addition to supporting queries on current data, PRESTO also supports queries on historical data using interpolation and local archival at sensors. PRESTO can adapt model and system parameters to data and query dynamics to further extract energy savings. We have implemented PRESTO on a sensor testbed comprising Intel Stargates and Telos Motes. Our experiments show that in a temperature monitoring application, PRESTO yields one to two orders of magnitude reduction in energy requirements over on-demand, proactive or model-driven pull approaches. PRESTO also results in an order of magnitude reduction in query latency in a 1 % duty-cycled five hop sensor network over a system that forwards all queries to remote sensor nodes.
ASAP: an adaptive sampling approach to data collection in sensor networks,”
- IEEE Transactions on Parallel and Distributed Systems,
, 2007
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Outlier Detection in Sensor Networks
- MobiHoc'07
, 2007
"... Outlier detection has many important applications in sensor networks, e.g., abnormal event detection, animal behavior change, etc. It is a difficult problem since global information about data distributions must be known to identify outliers. In this paper, we use a histogram-based method for outlie ..."
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Cited by 35 (1 self)
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Outlier detection has many important applications in sensor networks, e.g., abnormal event detection, animal behavior change, etc. It is a difficult problem since global information about data distributions must be known to identify outliers. In this paper, we use a histogram-based method for outlier detection to reduce communication cost. Rather than collecting all the data in one location for centralized processing, we propose collecting hints (in the form of a histogram) about the data distribution, and using the hints to filter out unnecessary data and identify potential outliers. We show that this method can be used for detecting outliers in terms of two different definitions. Our simulation results show that the histogram method can dramatically reduce the communication cost.
An energy-efficient querying framework in sensor networks for detecting node similarities
- In: ACM Int. Symp. on Modeling, Analysis and Simulation of Wireless and Mobile Systems
, 2006
"... We propose an energy-efficient framework, called SAF, for approximate querying and clustering of nodes in a sensor network. SAF uses simple time series forecasting models to predict sensor readings. The idea is to build these local models at each node, transmit them to the root of the network (the ” ..."
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Cited by 26 (1 self)
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We propose an energy-efficient framework, called SAF, for approximate querying and clustering of nodes in a sensor network. SAF uses simple time series forecasting models to predict sensor readings. The idea is to build these local models at each node, transmit them to the root of the network (the ”sink”), and use them to approximately answer user queries. Our approach dramatically reduces communication relative to previous approaches for querying sensor networks by exploiting properties of these local models, since each sensor communicates with the sink only when its local model varies due to changes in the underlying data distribution. In our experimental results performed on a trace of real data, we observed on average about 150 message transmissions from each sensor over a week (including the learning phase) to correctly predict temperatures to within +/- 0.5 ◦ C. SAF also provides a mechanism to detect data similarities between nodes and organize nodes into clusters at the sink at no additional communication cost. This is again achieved by exploiting properties of our local time series models, and by means of a novel definition of data similarity between nodes that is based not on raw data but on the prediction values. Our clustering algorithm is both very efficient and provably optimal in the number of clusters. Our clusters have several interesting features: first, they can capture similarity between far away nodes that are not geographically adjacent; second, cluster membership to variations in sensors’ local models; third, nodes within a cluster are not required to track the membership of other nodes in the cluster. We present a number of simulation-based experimental results that demonstrate these properties of SAF.
Probabilistic data management for pervasive computing: The data furnace project
- IEEE Data Eng. Bull
, 2006
"... The wide deployment of wireless sensor and RFID (Radio Frequency IDentification) devices is one of the key enablers for next-generation pervasive computing applications, including large-scale environmental monitoring and control, context-aware computing, and “smart digital homes”. Sensory readings a ..."
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Cited by 22 (0 self)
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The wide deployment of wireless sensor and RFID (Radio Frequency IDentification) devices is one of the key enablers for next-generation pervasive computing applications, including large-scale environmental monitoring and control, context-aware computing, and “smart digital homes”. Sensory readings are inherently unreliable and typically exhibit strong temporal and spatial correlations (within and across different sensing devices); effective reasoning over such unreliable streams introduces a host of new data management challenges. The Data Furnace project at Intel Research and UC-Berkeley aims to build a probabilistic data management infrastructure for pervasive computing environments that handles the uncertain nature of such data as a first-class citizen through a principled framework grounded in probabilistic models and inference techniques. 1
Data-Driven Processing in Sensor Networks
, 2007
"... Wireless sensor networks are poised to enable continuous data collection on unprecedented scales, in terms of area location and size, and frequency. This is a great boon to fields such as ecological modeling. We are collaborating with researchers to build sophisticated temporal and spatial models of ..."
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Cited by 19 (2 self)
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Wireless sensor networks are poised to enable continuous data collection on unprecedented scales, in terms of area location and size, and frequency. This is a great boon to fields such as ecological modeling. We are collaborating with researchers to build sophisticated temporal and spatial models of forest growth, utilizing a variety of measurements. There exists a crucial challenge in supporting this activity: network nodes have limited battery life, and radio communication is the dominant energy consumer. The straightforward solution of instructing all nodes to report their measurements as they are taken to a base station will quickly consume the network’s energy. On the other hand, the solution of building models for node behavior and substituting these in place of the actual measurements is in conflict with the end goal of constructing models. To address this dilemma, we propose data-driven processing, the goal of which is to provide continuous data without continuous reporting, but with checks against the actual data. Our primary strategy for this is suppression, which uses in-network monitoring to limit the amount of communication to the base station. Suppression employs models for optimization of data collection, but not at the risk of correctness. We discuss techniques for designing data-driven collection, such as building suppression schemes and incorporating models into them. We then present and address some of the major challenges to making this approach practical, such as handling failure and avoiding the need to co-design the network application and communication layers.
