Showing papers on "Data aggregator published in 2005"

Minimum data aggregation time problem in wireless sensor networks

Abstract: Wireless sensor networks promise a new paradigm for gathering data via collaboration among sensors spreading over a large geometrical region. Many real-time applications impose stringent delay requirements and ask for time-efficient schedules of data aggregations in which sensed data at sensors are combined at intermediate sensors along the way towards the data sink. The Minimum Data Aggregation Time (MDAT) problem is to find the schedule that routes data appropriately and has the shortest time for all requested data to be aggregated to the data sink. In this paper we study the MDAT problem with uniform transmission range of all sensors. We assume that, in each time round, data sent by a sensor reaches exactly all sensors within its transmission range, and a sensor receives data if it is the only data that reaches the sensor in this time round. We first prove that this problem is NP-hard even when all sensors are deployed a grid and data on all sensors are required to be aggregated to the data sink. We then design a (Δ–1)-approximation algorithm for MDAT problem, where Δ equals the maximum number of sensors within the transmission range of any sensor. We also simulate the proposed algorithm and compare it with the existing algorithm. The obtained results show that our algorithm has much better performance in practice than the theoretically proved guarantee and outperforms other algorithm.

CDA: concealed data aggregation for reverse multicast traffic in wireless sensor networks

Abstract: End-to-end encryption for wireless sensor networks is a challenging problem. To save the overall energy resources of the network, it is agreed that sensed data need to be consolidated and aggregated on their way to the final destination. We present an approach that (1) conceals sensed data end-to-end, by (2) still providing efficient in-network data aggregation. The aggregating intermediate nodes are not required to operate on the sensed plaintext data. We apply a particular class of encryption transformation and exemplarily discuss the approach on the basis of two aggregation functions. We use actual implementation to show that the approach is feasible and flexible and frequently even more energy efficient than hop-by-hop encryption.

Universal data aggregation

Abstract: A system for aggregating and managing PIM data from multiple sources is provided. By aggregating various sources of data, the present system allow for the bridging of networked communities and organizations. Limitations of data aggregation as a result of proprietary and/or protocol concerns are overcome through the development of trusted relationships amongst users of the data aggregation and management system.

A survey of Wireless Sensor Network technologies: research trends and middleware’s role

Abstract: Wireless Sensor Networks (WSNs) provide a new paradigm for sensing and disseminating information from various environments, with the potential to serve many and diverse applications. Current WSNs typically communicate directly with a centralized controller or satellite. On the other hand, a smart WSN consists of a number of sensors spread across a geographical area; each sensor has wireless communication capability and sufficient intelligence for signal processing and networking of the data. The structure of WSNs are tightly application-dependent, and many services are also dependent on application semantics (e.g. application-specific data processing combined with data routing). Thus, there is no single typical WSN application, and dependency on applications is higher than in traditional distributed applications. The application/middleware layer must provide functions that create effective new capabilities for efficient extraction, manipulation, transport, and representation of information derived from sensor data. In this paper, we report recent trends in wireless sensor network research including an overview of the various categories of WSN, a survey of WSN technologies and a discussion of existing research prototypes and industry applications. We focus on middleware technology, and describe details of some existing research prototypes, then address challenges and future perspectives on the middleware. This study highlights that middleware needs to provide a common interface for various functional components of WSN: detection and data collection, signal processing, data aggregation, and notification. By integrating sensing, signal processing, and communication functions, a WSN provides a natural platform for hierarchical information processing.

An experimental study of routing and data aggregation in sensor networks

Abstract: Several sensor network applications, such as environmental monitoring, require data aggregation to an observer. For this purpose, a data aggregation tree, rooted at the observer, is constructed in the network. Node clustering can be employed to further balance load among sensor nodes and prolong the network lifetime. In this paper, we design and implement a system, iHEED, in which node clustering is integrated with multi-hop routing for TinyOS. We consider simple data aggregation operators, such as AVG or MAX. We use a simple energy consumption model to keep track of the battery consumption of cluster heads and regular nodes. We perform experiments on a sensor network testbed to quantify the advantages of integrating hierarchical routing with data aggregation. Our results indicate that the network lifetime is prolonged by a factor of 2 to 4, and successful transmissions are almost doubled. Clustering plays a dominant role in delaying the first node death, while aggregation plays a dominant role in delaying the last node death

Data‐Centric Protocols for Wireless Sensor Networks

Abstract: This chapter reviews a number of emerging topics pertaining to a data-centric view of wireless sensor networks. These topics include data-driven routing, tracking mobile objects, constructing and maintaining reporting trees, dynamic evolution of a monitoring region for moving targets (mobicast), disseminating monitoring tasks, data gathering, receiving reports from a particular area of interest, and sending information and task assignment from a sink to all the sensors inside a geographic region (geocasting). The chapter also discusses various other issues, including sensor training options, data aggregation, data storage, as well as design guidelines for data aggregation and clustering, and rate-based data propagation in wireless sensor networks.

Optimized scheduling for data aggregation in wireless sensor networks

Abstract: The most important issue in wireless sensor networks is energy consumption. To this end, many networking schemes attempt to minimize the amount of data transmitted by using data aggregation. This trades off data freshness for a savings in energy, because reports from sensor nodes that arrive at an aggregating node may have to be held there for some period of time before being reported so that additional reports may reach the aggregator from slower nodes. We propose to use an intelligent timer and some high-level knowledge of the network to implement an efficient aggregation timing control protocol. Our protocol aims to dynamically change the data aggregation period according to the aggregation quality. A request from the data sink will include the maximum latency for a certain number of reports. If this number of reports can be returned in less time than the maximum, then the maximum time will not be reached. If, however, the specified number of responses can be returned in anywhere between zero-time (instantaneous) and the maximum latency, the timing scheme will find the minimum aggregation period that satisfies the sink's request.

Efficient aggregation of delay-constrained data in wireless sensor networks

Abstract: Recent years have witnessed a growing interest in the application of wireless sensor networks in unattended environments. Nodes in such applications are equipped with limited energy supply and need careful management in order to extend their lifetime. In order to conserve energy, many of the routing protocols proposed for wireless sensor networks reduce the number of transmitted packets by pursuing in-network data aggregation. Almost all of the aggregation schemes presented in the literature strive to save sensor’s energy while considering unconstrained data traffic. However, aggregation extends the queuing delay at the relay nodes and can thus complicate the handling of latency-constrained data. In this paper, we analyze the conditions for effective aggregation of data traffic that is subject to end-to-end delay constraints. We present an algorithm for achieving maximal possible energy saving through data aggregation while meeting the desired level of timeliness. A Weighted Fair Queuing based mechanism for packet scheduling is employed at each node in order to perform service differentiation and ensure bounded delay for constrained traffic. The performance of the proposed approach is qualified via simulation. The simulation results have demonstrated that the proposed approach provides a balance between energy consumption and timeliness level.

Energy-Efficient Data Aggregation Hierarchy for Wireless Sensor Networks

Abstract: A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, is often aggregated (or compressed). This compression is done by a subset of the sensors called aggregators. Since sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, we first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS). Then, we generalize it to an aggregation hierarchy and extend EPAS to a Hierarchical Energy-Efficient Protocol for Aggregator Selection (hEPAS). We derive the optimal number of aggregators with generalized compression and power-consumption models, and present fully distributed algorithms for aggregator deployment. Simulation results show that our algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols, and are thus applicable to a broad spectrum of application environments.

An adaptive modular approach to the mining of sensor network data

Abstract: This paper proposes a two-layer modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task.

A scalable correlation aware aggregation strategy for wireless sensor networks

Abstract: Sensors-to-sink data in wireless sensor networks (WSNs) are typically correlated with each other. Exploiting such correlation when performing data aggregation can result in considerable improvements in the bandwidth and energy performance of WSNs. In order to exploit such correlation, we present a scalable and distributed correlation-aware aggregation structure that addresses the practical challenges in the context of aggregation in WSNs. Through simulations and analysis, we evaluate the performance of the proposed approach with centralized and distributed correlation aware and unaware structures.

Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information

Abstract: Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature-based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on s...

Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualization of Large Datasets

Abstract: Many of the traditional data visualization techniques, which proved to be supportive for exploratory analysis of datasets of moderate sizes, fail to fulfil their function when applied to large datasets. There are two approaches to coping with large amounts of data: data selection, when only a portion of data is displayed, and data aggregation, i.e. grouping data items and considering the groups instead of the original data. None of these approaches alone suits the needs of exploratory data analysis, which requires consideration of data on all levels: overall (considering a dataset as a whole), intermediate (viewing and comparing collective characteristics of arbitrary data subsets, or classes), and elementary (accessing individual data items). Therefore, it is necessary to combine these approaches, i.e. build a tool showing the whole set and arbitrarily defined subsets (object classes) in an aggregated way and superimposing this with a representation of arbitrarily selected individual data items. We have achieved such a combination of approaches by modifying the technique of parallel coordinate plot. These modifications are described and analysed in the paper.

Executing incoherency bounded continuous queries at web data aggregators

Abstract: Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some function over distributed data items, for example, to determine when and whether (a) the traffic entering a highway from multiple feed roads will result in congestion in a thoroughfare or (b) the value of a stock portfolio exceeds a threshold. Using the standard Web infrastructure for these applications will increase the reach of the underlying information. But, since these queries involve data from multiple sources, with sources supporting standard HTTP (pull-based) interfaces, special query processing techniques are needed. Also, these applications often have the flexibility to tolerate some incoherency, i.e., some differences between the results reported to the user and that produced from the virtual database made up of the distributed data sources.In this paper, we develop and evaluate client-pull-based techniques for refreshing data so that the results of the queries over distributed data can be correctly reported, conforming to the limited incoherency acceptable to the users.We model as well as estimate the dynamics of the data items using a probabilistic approach based on Markov Chains. Depending on the dynamics of data we adapt the data refresh times to deliver query results with the desired coherency. The commonality of data needs of multiple queries is exploited to further reduce refresh overheads. Effectiveness of our approach is demonstrated using live sources of dynamic data: the number of refreshes it requires is (a) an order of magnitude less than what we would need if every potential update is pulled from the sources, and (b) comparable to the number of messages needed by an ideal algorithm, one that knows how to optimally refresh the data from distributed data sources. Our evaluations also bring out a very practical and attractive tradeoff property of pull based approaches, e.g., a small increase in tolerable incoherency leads to a large decrease in message overheads.

Inter-query data aggregation in wireless sensor networks

Abstract: In this paper, we classify data aggregation into two kinds: single-query data aggregation, inter-query data aggregation. As we know, single-query data aggregation is a hot issue in sensor networks, e.g. TAG (S. Madden, et al., 2002) makes big contribution to single-query data aggregation. But less attention are paid to inter-query data aggregation. We analyze problems existing in inter-query data aggregation as it is more complicated than single-query data aggregation, and then we resolve these problems and evaluate efficiency of our work by computing consumed energy.

Method and apparatus for configurable data aggregation in a data warehouse

Abstract: A computer implemented method, apparatus, and computer usable program code to identify a policy for managing data in a data storage system. Raw data is located in the data storage system for processing to form located data. The located data is aggregated based on the policy to form aggregated data. The aggregated data is stored in the data storage system.

Location-Based Hierarchical Data Aggregation in Vehicular Ad Hoc Networks

Abstract: Existing road traffic information systems rely on fixed infrastructures to manage traffic data. The deployment and maintenance cost of such infrastructures, however, is very high. With on-board sensors and wireless short range communication, infrastructure-less traffic data management based on inter-vehicle communication will be viable in the near future, with data aggregation being a key issue for data management. In this paper, we review some of the existing data aggregation strategies in wireless ad hoc networks and discuss new data aggregation challenges. Finally, we present a novel location-based hierarchical data aggregation approach, which is scalable and robust with regard to high vehicle mobility.

D3: data-centric data dissemination in wireless sensor networks

Abstract: This paper presents a novel method to disseminate sensor data in a wireless sensor network, called D3 (data-centric data dissemination). The method combines the advantages of data-centric routing like SPIN and directed diffusion and energy-efficient MAC protocols such as S-MAC and T-MAC. The protocol's strengths are its energy-efficiency and its simplicity. Messages are transmitted using broadcasting only, reaching as many nodes as possible with the least energy. Furthermore, D3 easily accommodates energy-dependent traffic balancing and data aggregation, crucial to prolong the lifetime of a sensor network

Bloom filters for data aggregation and discovery: a hierarchical clustering approach

Abstract: This work addresses the data aggregation problem by tightly integrating the processes of building and maintaining distributed data structures while optimally serving queries over the network Importantly, the approach goes beyond existing clustering techniques by addressing the incremental problem at the same time as the query problem

Sensor data aggregation for resource inventory applications

Abstract: With the creative use of sensors, in the future, sensor networks could impact on a wide variety of applications from national security to consumer electronics. Sharing the vision and emphasizing the energy efficiency of data dissemination for resource inventory applications, we propose a novel data aggregation strategy and algorithms to obtain the population of the objects (i.e., the resource) to be sensed in the networks. With a set of cautiously validated simulations, we show that the energy consumption in applying our solution is only one third of the original case and scalable to the object population. Experimental results also show that we are able to obtain consistent lower and upper bounds at 70% and 180% of the exact population. While we show the solution works effectively for resource inventory applications, this work suggests more profoundly that there is yet design space to explore in application-specific data aggregation for energy conservation.

Data dissemination using gossiping in wireless sensor networks

Abstract: Disseminating data among sensors is a fundamental operation in energy-constrained wireless sensor networks. We present a gossip-based adaptive protocol for data dissemination to improve energy efficiency of this operation. To overcome the data implosion problems associated with dissemination operation, our protocol uses meta-data to name the data using high-level data descriptors and negotiation to eliminate redundant transmissions of duplicate data in the network. Further, we adapt the gossiping with data aggregation possibilities in sensor networks. We simulated our data dissemination protocol, and compared it to the SPIN protocol. We find that our protocol improves on the energy consumption by about 20% over others, while improving significantly over the data dissemination rate of gossiping.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Event buffering model for instrumentation providers

Abstract: Systems and methods for intermediate buffering of data for the purpose of controlling its delivery to the consumer. The systems and methods for buffering data can arbitrate between the incoming data flow from the generating component and the outgoing data flow to the consumer. In doing so, the systems and methods for buffering of data seek to honor the delivery demands and/or constraints of the consumer, while avoiding the loss of the data generated by the producer. The delivery demands of the consumer may include requirements pertaining to maximum acceptable incoming data rate, the desired incoming data rate, incoming data aggregation, the desired freshness of the data, and tolerance for event loss. The generation component constraints may include the space limitations on buffering data within the data buffer.

Clustering-based correlation aware data aggregation for distributed sensor networks

Abstract: Temporal and spatial correlation in the sensed data in wireless distributed sensor networks gives room for better energy efficiency in the network. Several data aggregation schemes have been suggested in the literature. However a clear-cut solution which quantitatively describes most energy-efficient routing scheme is still lacking. In this paper, we propose a novel, generalized clustering-based aggregation scheme, called "annular slicing-based clustering (ASC)" and show that by varying the cluster size and the distribution of clusters in the deployment area, one can approach the most energy-efficient aggregation scheme. Analytical expressions for the optimal cluster size and distribution have been arrived at, for a specific correlation model and a cost function based on the Euclidean distance traversed by the transmitted data. With the help of numerical simulation, it has been found that the proposed aggregation technique can achieve optimality over a wide range of correlation

Topology knowledge range control for lifetime maximization in sensor networks with data aggregation

Abstract: Prolonging lifetime is a key challenge in wireless sensor networks comprising unattended, limited resource sensor nodes. Since sensor networks are usually large in size, simple and scalable routing schemes are essential. Geographical routing is attractive due to its simplicity and scalability. At the same time geographical routing is not efficient from the perspectives of energy consumption and energy balancing. By appropriately selecting topology knowledge range for the sensor nodes energy efficiency can be improved and energy usage of various sensor nodes can be more balanced, leading to longer network lifetime. Different from the former work on knowledge range control where the objective is to minimize the total energy consumption in the network, our work aims at lifetime maximization which needs a tradeoff between energy efficiency and balanced energy consumption. Further, we study the problem for the networks wherein data aggregation is carried out at the nodes. Data aggregation is a commonly used operation in several sensor network applications. While data aggregation reduces load in the network leading to improved lifetime, developing efficient solutions is not trivial. We develop Mixed Integer Linear Programming (MILP) formulations for the maximum lifetime knowledge range decision problem in networks with data aggregation, and then develop a scalable distributed protocol. We verify the effectiveness of this protocol through extensive simulations. We also study the impact of data aggregation on lifetime. We make several useful observations from the numerical results obtained from CPLEX and the simulation results.

XG: a data-driven computation grid for enterprise-scale mining

Abstract: In this paper we introduce a novel architecture for data processing, based on a functional fusion between a data and a computation layer. We show how such an architecture can be leveraged to offer significant speedups for data processing jobs such as data analysis and mining over large data sets. One novel contribution of our solution is its data-driven approach. The computation infrastructure is controlled from within the data layer. Grid compute job submission events are based within the query processor on the DBMS side and in effect controlled by the data processing job to be performed. This allows the early deployment of on-the-fly data aggregation techniques, minimizing the amount of data to be transfered to/from compute nodes and is in stark contrast to existing Grid solutions that interact with data layers mainly as external “storage”. We validate this in a scenario derived from a real business deployment, involving financial customer profiling using common types of data analytics (e.g., linear regression analysis). Experimental results show significant speedups. For example, using a grid of only 12 non-dedicated nodes, we observed a speedup of approximately 1000% in a scenario involving complex linear regression analysis data mining computations for commercial customer profiling.

Robust security in large-scale wireless actuator and sensor networks: a low energy two-level implementation

Abstract: Our work aims to address some challenging security issues in an important information infrastructure - large-scale and low-energy wireless actuator and sensor networks (WASN). Instead of purely focusing on security research itself as in most of the current literature, we argue that WASNs have specific network constraints and data transmission requirements compared to general ad hoc networks and other wireless/wired networks. We propose to seamlessly integrate WASN security with a promising routing architecture that proves to be scalable and energy-efficient. To protect from active attacks in mobile sensor networks, we propose two-level re-keying/re-routing schemes that can not only adapt to a dynamic network topology but also securely update keys for each data transmission session. Moreover, due to the importance of secure in-networking processing such as data aggregation in WASNs, we define a multiple-key management scheme closely related to the proposed tree-ripple-zone (TRZ) routing architecture.

Resilient Data Aggregation in Wireless Sensor Networks

Abstract: ANANTHARAJU, SRINATH. Resilient Data Aggregation in Wireless Sensor Networks. (Under the direction of Assistant Professor Peng Ning). Sensor nodes are low-cost and low-power devices that are prone to node compromises, communication failures and malfunctioning of sensing hardware. As a result, some nodes may report outlying data values, introducing significant deviations in the aggregated sensor readings. This thesis presents a practical resilient outlier detection technique to filter out the influence of the outlying data reported by faulty or compromised nodes. The proposed outlier detection algorithm is based on event localization using minimum mean squared error (MMSE) estimation combined with threshold-based consistency checking to detect outliers. Data aggregation is one of the key techniques commonly used to develop lightweight communication protocols applicable to wireless sensor networks. The proposed approach handles localization of multiple events by grouping the sensor readings into spatially correlated clusters and performing an event-centric detection of outliers. In the entire process of data aggregation, the outlier detection technique fits as a preprocessing stage for reducing the effect of outliers on the aggregated result. Suitable extensions to the basic outlier detection algorithm are proposed to effectively apply the algorithm to both centralized and decentralized sensor network architectures. This thesis further includes studies that test the effectiveness of the proposed approach, including the detection rate, the false positive rate, degree of damage and the resilience to malicious readings introduced by the attackers. The experimental results show that on average the proposed approach detects as high as 80-90% of the outliers while resulting in 5-15% false positive rate when the network consists of 40-45% outliers. The experiments also show that the extent of damage on the aggregated result is below 50% due to the elimination of outliers before aggregation. Finally, the resilient data aggregation process requires modest computational and memory requirements with zero communication overhead in the centralized case and about 20% overhead in the decentralized settings. Resilient Data Aggregation in Wireless Sensor Networks

Local data protection for in-network processing in sensor networks

Abstract: Sensor networks are playing a more and more important role in monitoring problems such as surveillance and tracking moving objects. In-network processing has been shown to improve scalability, prolong sensor network lifetimes, and diminish computational demands. However, securely querying past data becomes a challenge. An unimportant event in the past could become interesting later; therefore, methods for securely placing a query on a past event should be available. This paper focuses on the challenges associated with securing queries on past data in a sensor network, and proposes methods for past data aggregation using in-network processing. Two different methods are proposed for securing queries on past data; both use forward secure cryptography and provide forward secure data authentication. We compare their security and performance, and also compare them with previous schemes.

Modeling in-network processing and aggregation in sensor networks: algorithms and evaluation

Abstract: The rapid advances in processor, memory and radio technology have enabled the development of distributed networks of sensor nodes capable of sensing and communicating using wireless media The basic operation in sensor networks is the systematic gathering and transmission of sensed data to the end-user The severe energy constraints and limited computing capabilities of the sensors present major challenges to sensor network design We propose two new protocols for in-network processing and data aggregation, DEEPADS (distributed energy-efficient protocol for aggregation of data in sensor networks) and C-DEEPADS (clustered-DEEPADS) that maximize the lifetime of the sensor network Simulation results show that our protocols perform better than these existing approaches: directed diffusion (Intanagonwiwat, C et al, 2003), LEACH (Heinzelman, W et al, 2002), PEDAP and PEDAP-PA (Tan, HO and Korpeoglu, I, 2003) The two-tier hierarchical approach of C-DEEPADS is optimal in terms of maximizing the system lifetime as well as reducing-the end-to-end latency