Results 1 - 10
of
54
Iso-Map: Energy-Efficient Contour Mapping in Wireless Sensor Networks
- in Proceedings of IEEE ICDCS, 2007. Energy (J) Maximum Sleep Time (s) NS PAS SAS
, 2007
"... Contour mapping is a crucial part of many wireless sensor network applications. Many efforts have been made to avoid collecting data from all the sensors in the network and producing maps at the sink, which is proven to be inefficient. The existing approaches (often aggregation based), however, suff ..."
Abstract
-
Cited by 28 (5 self)
- Add to MetaCart
(Show Context)
Contour mapping is a crucial part of many wireless sensor network applications. Many efforts have been made to avoid collecting data from all the sensors in the network and producing maps at the sink, which is proven to be inefficient. The existing approaches (often aggregation based), however, suffer from heavy transmission traffic and incur large computational overheads on each sensor node. We propose Iso-Map, an energy-efficient protocol for contour mapping, which builds contour maps based solely on the reports collected from intelligently selected “isoline nodes” in wireless sensor networks. Iso-Map achieves high-quality contour mapping while significantly reducing the generated traffic from O(n) to O ( n), where n is the total number of sensor nodes in the field. The per-node computation overhead is also restrained as a constant. We conduct comprehensive trace-driven simulations to verify this protocol, and demonstrate that Iso-Map outperforms the previous approaches in the sense that it produces contour maps of high fidelity with significantly reduced energy cost. 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 ..."
Abstract
-
Cited by 23 (0 self)
- Add to MetaCart
(Show Context)
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
A Survey on Topology issues in Wireless Sensor Network
"... Abstract: Topology issues have received more and more attentions in Wireless Sensor Networks (WSN). While WSN applications are normally optimized by the given underlying network topology, another trend is to optimize WSN by means of topology control. A number of approaches have been invested in this ..."
Abstract
-
Cited by 22 (0 self)
- Add to MetaCart
(Show Context)
Abstract: Topology issues have received more and more attentions in Wireless Sensor Networks (WSN). While WSN applications are normally optimized by the given underlying network topology, another trend is to optimize WSN by means of topology control. A number of approaches have been invested in this area, such as topology directed routing, cooperating schemes, sensor coverage based topology control and network connectivity based topology control. Most of the schemes have proven to be able to provide a better network monitoring and communication performance with prolonged system lifetime. In this survey paper, we provide a full view of the studies in this area. By summarizing previous achievements and analyzing existed problems, we also point out possible research directions for future work Keywords: Wireless sensor networks, Topology,
Indexable PLA for Efficient Similarity Search
"... Similarity-based search over time-series databases has been a hot research topic for a long history, which is widely used in many applications, including multimedia retrieval, data mining, web search and retrieval, and so on. However, due to high dimensionality (i.e. length) of the time series, the ..."
Abstract
-
Cited by 18 (0 self)
- Add to MetaCart
(Show Context)
Similarity-based search over time-series databases has been a hot research topic for a long history, which is widely used in many applications, including multimedia retrieval, data mining, web search and retrieval, and so on. However, due to high dimensionality (i.e. length) of the time series, the similarity search over directly indexed time series usually encounters a serious problem, known as the “dimensionality curse”. Thus, many dimensionality reduction techniques are proposed to break such curse by reducing the dimensionality of time series. Among all the proposed methods, only Piecewise Linear Approximation (PLA) does not have indexing mechanisms to support similarity queries, which prevents it from efficiently searching over very large timeseries databases. Our initial studies on the effectiveness of different reduction methods, however, show that PLA performs no worse than others. Motivated by this, in this paper, we re-investigate PLA for approximating and indexing time series. Specifically, we propose a novel distance function in the reduced PLA-space, and prove that this function indeed results in a lower bound of the Euclidean distance between the original time series, which can lead to no false dismissals during the similarity search. As a second step, we develop an effective approach to index these lower bounds to improve the search efficiency. Our extensive experiments over a wide spectrum of real and synthetic data sets have demonstrated the efficiency and effectiveness of PLA together with the newly proposed lower bound distance, in terms of both pruning power and wall clock time, compared with two stateof-the-art reduction methods, Adaptive Piecewise Constant Approximation (APCA) and Chebyshev Polynomials (CP).
Capturing data uncertainty in high-volume stream processing
- In CIDR
, 2009
"... We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous ran-dom variables such as many types of sensor data. To provide an end- ..."
Abstract
-
Cited by 14 (2 self)
- Add to MetaCart
(Show Context)
We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous ran-dom variables such as many types of sensor data. To provide an end-to-end solution, our system employs probabilistic modeling and inference to generate uncertainty description for raw data, and then a suite of statistical techniques to capture changes of uncertainty as data propagates through query operators. To cope with high-volume streams, we ex-plore advanced approximation techniques for both space and time efficiency. We are currently working with a group of scientists to evaluate our system using traces collected from real-world applications for hazardous weather monitoring and for object tracking and monitoring. 1.
In-network outlier cleaning for data collection in sensor networks
- In CleanDB, Workshop in VLDB 2006
, 2006
"... Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be don ..."
Abstract
-
Cited by 13 (1 self)
- Add to MetaCart
(Show Context)
Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be done innetwork to avoid spending energy on transmitting outliers. In this paper, we propose an in-network outlier cleaning approach, including wavelet based outlier correction and neighboring DTW(Dynamic Time Warping) distance-based outlier removal. The cleaning process is accomplished during multi-hop data forwarding process, and makes use of the neighboring relation in the hop-count based routing algorithm. Our approach guarantees that most of the outliers can be either corrected, or removed from further transmission within 2 hops. We have simulated a spatialtemporal correlated environmental area, and evaluated the outlier cleaning approach in it. The results show that our approach can effectively clean the sensing data and reduce outlier traffic. 1
Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks
"... Abstract. In this paper 1, we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show that the Bayesian Network model can learn cyclical baselines for gas concentrations, thus reducing false alarms ..."
Abstract
-
Cited by 12 (1 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper 1, we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show that the Bayesian Network model can learn cyclical baselines for gas concentrations, thus reducing false alarms usually caused by flatline thresholds. Further, we show that the system can learn dependencies between changes of concentration in different gases and at multiple locations. We define and identify new types of events that can occur in a sensor network. In particular, we analyse joint events in a group of sensors based on learning the Bayesian model of the system, contrasting these events with merely aggregating single events. We demonstrate that anomalous events in individual gas data might be explained if considered jointly with the changes in other gases. Vice versa, a network-wide spatiotemporal anomaly may be detected even if individual sensor readings were within their thresholds. The presented Bayesian approach to spatiotemporal anomaly detection is applicable to a wide range of sensor networks. 1
Top-k dominating queries in uncertain databases
- in EDBT, 2009
"... Due to the existence of uncertain data in a wide spectrum of real applications, uncertain query processing has become increasingly important, which dramatically differs from handling certain data in a traditional database. In this paper, we formulate and tackle an important query, namely probabilist ..."
Abstract
-
Cited by 12 (0 self)
- Add to MetaCart
(Show Context)
Due to the existence of uncertain data in a wide spectrum of real applications, uncertain query processing has become increasingly important, which dramatically differs from handling certain data in a traditional database. In this paper, we formulate and tackle an important query, namely probabilistic top-k dominating (PTD) query, in the uncertain database. In particular, a PTD query re-trieves k uncertain objects that are expected to dynamically domi-nate the largest number of uncertain objects. We propose an effec-tive pruning approach to reduce the PTD search space, and present an efficient query procedure to answer PTD queries. Furthermore, approximate PTD query processing and the case where the PTD query is issued from an uncertain query object are also discussed. Extensive experiments have demonstrated the efficiency and effec-tiveness of our proposed PTD query processing approaches. 1.
Processing Proximity Queries in Sensor Networks
, 2006
"... Sensor networks are often used to perform monitoring tasks, such as in animal or vehicle tracking and in surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries that allow us to report interesting events that are observed by nodes in t ..."
Abstract
-
Cited by 9 (4 self)
- Add to MetaCart
Sensor networks are often used to perform monitoring tasks, such as in animal or vehicle tracking and in surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries that allow us to report interesting events that are observed by nodes in the network that are within certain distance of each other. An event is triggered when a userprogrammable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks using existing communication protocols and then propose an efficient algorithm that can process multiple proximity queries, involving several different event types. Our solution utilizes a distributed routing index, maintained by the nodes in the network that is dynamically updated as new observations are obtained by the nodes. We present an extensive experimental study to show the benefits of our techniques under different scenarios. Our results demonstrate that our algorithms scale better and require orders of magnitude fewer messages compared to a straightforward computation of the queries.
Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields
"... Event detection is a critical task in sensor networks for a variety of real-world applications. Many realworld events often exhibit complex spatio-temporal patterns whereby they manifest themselves via observations over time and space proximities. These spatio-temporal events cannot be handled well ..."
Abstract
-
Cited by 7 (0 self)
- Add to MetaCart
Event detection is a critical task in sensor networks for a variety of real-world applications. Many realworld events often exhibit complex spatio-temporal patterns whereby they manifest themselves via observations over time and space proximities. These spatio-temporal events cannot be handled well by many of the previous approaches. In this paper, we propose a new Spatio-Temporal Event Detection (STED) algorithm in sensor networks based on a dynamic conditional random field (DCRF) model. Our STED method handles the uncertainty of sensor data explicitly and permits neighborhood interactions in both observations and event labels. Experiments on both real data and synthetic data demonstrate that our STED method can provide accurate event detection in near real time even for large-scale sensor networks. 1
