Showing papers on "Data aggregator published in 2011"

A survey on clustering algorithms for wireless sensor networks

Abstract: A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks

Abstract: When wireless sensor networks accumulate sensitive or confidential data, privacy becomes an important concern. Sensors are often resource-limited and power-constrained, and data aggregation is commonly used to address these issues. However, providing privacy without disrupting in-network data aggregation is challenging. Although privacy-preserving data aggregation for additive and multiplicative aggregation functions has been studied, nonlinear aggregation functions such as maximum and minimum have not been well addressed. We present KIPDA, a privacy-preserving aggregation method, which we specialize for maximum and minimum aggregation functions. KIPDA obfuscates sensitive measurements by hiding them among a set of camouflage values, enabling k-indistinguishability for data aggregation. In principle, KIPDA can be used to hide a wide range of aggregation functions, although this paper considers only maximum and minimum. Because the sensitive data are not encrypted, it is easily and efficiently aggregated with minimal in-network processing delay. We quantify the efficiency of KIPDA in terms of power consumption and time delay, studying tradeoffs between the protocol's effectiveness and its resilience against collusion.

Secure and privacy-preserving information aggregation for smart grids

Abstract: In this paper, we present a distributed incremental data aggregation approach, in which data aggregation is performed at all smart meters involved in routing the data from the source meter to the collector unit. With a carefully constructed aggregation tree, the aggregation route covers the entire local neighbourhood or any arbitrary set of designated nodes with minimum overhead. To protect user privacy, homomorphic encryption is used to secure the data enroute. Therefore, all the meters participate in the aggregation, without seeing any intermediate or final result. In this way, our approach supports efficient data aggregation in smart grids, while fully protecting user privacy. This approach is especially suitable for smart grids with repetitive routine data aggregation tasks.

Efficient Cluster Head Selection Scheme for Data Aggregation in Wireless Sensor Network

Abstract: A wireless sensor network is a resource constraint network, in which all sensor nodes have limited resources. In order to save resources and energy, data must be aggregated, and avoid amounts of traffic in the network. The aim of data aggregation is that eliminates redundant data transmission and enhances the life time of energy in wireless sensor network. Data aggregation process has to be done with the help of effective clustering scheme .in this paper we gives new scheme related to clustering for data aggregation called “Efficient cluster head selection scheme for data aggregation in wireless sensor network” (ECHSSDA), also we compare our propose scheme to the LEACH clustering algorithm. Comparison is based on the energy consumption, cluster head selection and cluster formation. In which we predict that, our propose algorithm is better than LEACH in the case of consume less energy by the cluster node and cluster head sending data to the base station consume less energy as better then LEACH.

Data aggregation module supporting dynamic query responsive aggregation during the servicing of database query requests provided by one or more client machines

Abstract: Improved method of and apparatus for aggregating data elements in multidimensional databases (MDDB). In the preferred embodiment, the apparatus is realized in the form of a high-performance stand-alone (i.e. external) aggregation server which can be plugged-into conventional MOLAP systems to achieve significant improvements in system performance. In accordance with the principles of the present invention, the stand-alone aggregation server contains a scalable MDDB and a high-performance aggregation engine that are integrated into the modular architecture of the aggregation server. The stand-alone aggregation server of the present invention can uniformly distribute data elements among a plurality of processors, for balanced loading and processing, and therefore is highly scalable.

An event buffer flooding attack in DNP3 controlled SCADA systems

Abstract: The DNP3 protocol is widely used in SCADA systems (particularly electrical power) as a means of communicating observed sensor state information back to a control center. Typical architectures using DNP3 have a two level hierarchy, where a specialized data aggregator receives observed state from devices within a local region, and the control center collects the aggregated state from the data aggregator. The DNP3 communications are asynchronous across the two levels; this leads to the possibility of completely filling a data aggregator's buffer of pending events, when a compromised relay sends overly many (false) events to the data aggregator. This paper investigates the attack by implementing the attack using real SCADA system hardware and software. A Discrete-Time Markov Chain (DTMC) model is developed for understanding conditions under which the attack is successful and effective. The model is validated by a Mobius simulation model and data collected on a real SCADA testbed.

A Secure Data Aggregation and Dispatch Scheme for Home Area Networks in Smart Grid

Abstract: Cyber security for smart grid communication systems is one of the most critical requirements need to be assured before smart grid can be operationally ready for the market. Privacy is one of very important security consideration. The customer information privacy in smart grid need to be protected. Smart grid data privacy encompasses confidentiality and anonymity of the information extracted from smart device metering transmission in smart grid communication system. In this paper, we consider a home area network as a basic reading data aggregation and dispatch unit in smart grid systems. Then, we propose a secure in-network data aggregation and dispatch scheme to keep the confidentiality and anonymity for collecting power usage information of home smart devices to the household smart meter and the reverse control message distributing procedure. Specifically, we introduce an orthogonal chip code to spread reading-data of different home smart devices into spread code, followed by a circuit shifting operation to coupling neighboring smart devices tightly. We adopt an in-network mechanism to further mask it with its spread data with its forwarding data. Finally, we analyze the cyber security protection levels using an information theoretic quantity, residual uncertainty. Simulation studies are conducted to test the performance on different metering datasets for the proposed scheme. This paper sets the ground for further research on optimizing of home power management systems with regarding to the privacy of customer power usage behaviors.

PDA: Privacy-Preserving Data Aggregation for Information Collection

Abstract: Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme---Cluster-based Private Data Aggregation (CPDA)---leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme---Slice-Mix-AggRegaTe (SMART)---builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

Efficient clustering-based data aggregation techniques for wireless sensor networks

Abstract: In wireless sensor network applications for surveillance and reconnaissance, large amounts of redundant sensing data are frequently generated. It is important to control these data with efficient data aggregation techniques to reduce energy consumption in the network. Several clustering methods were utilized in previous works to aggregate large amounts of data produced from sensors in target tracking applications (Park in A dissertation for Doctoral in North Carolina State University, 2006). However, such data aggregation algorithms show effectiveness only in restricted environments, while posing great problems when adapting to other various situations. To alleviate these problems, we propose two hybrid clustering based data aggregation mechanisms. The combined clustering-based data aggregation mechanism can apply multiple clustering techniques simultaneously in a single network depending on the network environment. The adaptive clustering-based data aggregation mechanism can adaptively choose a suitable clustering technique, depending on the status of the network. The proposed mechanisms can increase the data aggregation efficiency as well as improve energy efficiency and other important issues compared to previous works. Performance evaluation via mathematical analysis and simulation has been made to show the effectiveness of the proposed mechanisms.

A Security Architecture for Data Aggregation and Access Control in Smart Grids

Abstract: We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism gives selective access to consumer data stored in data repositories and used by different smart grid users. Users can be maintenance units, utility centers, pricing estimator units or analyzing and prediction groups. We solve this problem of access control using cryptographic technique of attribute-based encryption. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute keys. Bobba \emph{et al.} \cite{BKAA09} proposed an access control scheme, which relies on a centralized KDC and is thus prone to single-point failure. The other requirement is that the KDC has to be online, during data transfer which is not required in our scheme. Our access control scheme is collusion resistant, meaning that users cannot collude and gain access to data, when they are not authorized to access. We theoretically analyze our schemes and show that the computation overheads are low enough to be carried out in smart grids. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and presents an overall security architecture in smart grids.

Data aggregation for periodic sensor networks using sets similarity functions

Abstract: Energy is a major constraint in wireless sensor networks. Data Aggregation constitutes a fundamental mechanism for energy optimization. The idea is to minimize redundancy from the raw data captured by the sensors, minimizing the number of transmissions to the sink and thus saving energy. Since the data is often captured on a periodic basis, and sensor nodes detect common phenomena, a periodic based protocol that manages collected data sets can help to preserve the scarce energy. This paper proposes a new filtering technique for identifying duplicate sets of periodically captured data. We suggest a data aggregation model based on set joins similarity functions that conserves data integration while eliminating inherited redundancy. We show through the result that our approach offers significant data reduction by eliminating in-network redundancy and sending only necessary information to the sink.

Privacy Preserving Data Aggregation in Wireless Sensor Networks

Abstract: Privacy preservation is an important issue in today's context of extreme penetration of internet and mobile technologies. It is more important in the case of Wireless Sensor Networks (WSNs) where collected data often requires in-network processing and collaborative computing. Researches in this area are mostly concentrated in applying data mining techniques to preserve the privacy content of the data. These techniques are mostly computationally expensive and not suitable for resource limited WSN nodes. In this paper, a scheme is developed to provide privacy preservation in a much simpler way with the help of a secure key management scheme and randomized data perturbation technique. We consider a scenario in which two or more parties owning confidential data need to share only for aggregation purpose to a third party, without revealing the content of the data. Through simulation results the efficacy of our scheme and compare the result with one of the established scheme [1].

EIRDA: An Energy Efficient Interest based Reliable Data Aggregation Protocol for Wireless Sensor Networks

Abstract: When designing a protocol for data aggregation two things need to be considered; data reliability and energy efficiency. A good data aggregation protocol is one that achieves high data reliability using the least amount of overhead as possible. In case of wireless sensor networks (WSNs), data aggregation is widely accepted as an essential pattern for energy efficiency. In this paper, we propose An Energy Efficient Interest Based Reliable Data Aggregation (EIRDA) Protocol for WSNs. EIRDA effectively delivers the data to the sink. In EIRDA, we consider static clustering scheme for the uniform distribution of sensor nodes (SNs) in each cluster. Simulation result shows that EIRDA is efficient in terms of energy and achieves higher reliability.

Energy-efficient framework for data gathering in wireless sensor networks via the combination of sleeping MAC and data aggregation strategies

Abstract: In this paper, we introduce and evaluate a data collection and transmission paradigm in Wireless Sensor Networks (WSNs), where the primary objective is energy efficiency, while at the same time it is flexible and adaptive to support various Quality of Service (QoS) application constraints. The proposed framework combines two different energy-saving methods, one at the application layer, namely data aggregation, and one at the Medium Access Control (MAC) layer by appropriately adopting sleeping mechanisms. This is achieved by following a cross-layer approach where local information about the status of each sensor, including its sleep/awake schedules, feeds a subsequent data aggregation phase, which gathers correlated data and aggregates it to a single packet towards its way to the sink by applying a fully localised, distributed, probabilistic method. Simulation results demonstrate the significant energy savings achieved by our proposed framework, especially when compared to conventional MAC layer approaches and/or data aggregation approaches.

Methods and systems for data aggregation and reporting

Abstract: The present application is generally directed to mediums, methods, and systems for providing a data aggregator and reporting engine for utilities data. Exemplary embodiments provide procedures for aggregating and reporting data. According to exemplary embodiments, a base configuration for a data aggregator is provided. A variety of characteristics and functionality are built into the base configuration of the aggregator. Each characteristic may include a plurality of built-in options. By selecting one or more relevant options for each characteristic, a utility provider can build an aggregator that applies a custom profile to aggregate and report utilities data. The options may be selected prior to run-time, or may be selected at run-time. A dynamic reporting framework is also provided. The dynamic reporting framework allows for data aggregations to be calculated during the aggregation process without requiring that either the aggregator or the reporting framework be rebuilt or redesigned.

Survey on secure data aggregation for wireless sensor networks

Abstract: Data aggregation can significantly reduce the amount of data transmitted to the base station, therefore improve the energy efficiency and prolong the wireless network lifetime. However the sensor nodes may be deployed in remote and hostile environments where attackers may inject false information or forge aggregation values without being detected. Thus, security issue becomes an important research field in data aggregation for wireless sensor networks (WSNs). In this paper, the secure data aggregation schemes are categorized into hop by hop aggregation and end to end aggregation, and then the secure data aggregation schemes are reviewed and analyzed based on four phases: bootstrapping, data aggregation, verification and remedy. In addition, some topics for future search directions are pointed out.

Evolutionary game-based data aggregation model for wireless sensor networks

Abstract: Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes. Since multi-source data obtained from different nodes represent redundancy or complement property, as an effective tool to deal with the conflicts, the use of game theory for WSNs is provided. The authors propose a common aggregation model, which is independent of the specific application environments, based on the evolutionary game theory called evolutionary game-based data aggregation model (EGDAM) in WSNs. EGDAM made up of formal definition, functional model and general process is defined to map the competition and cooperation in aggregation procedure into games, and well-avoid perfect rationality. The authors then put the theoretic model into application. Guided by our model, an evolutionary game-based adaptive weighting algorithm named EGWDA is provided for the pixel-level data aggregation with homogeneous sensors. Reasonable weights distribution of sensors can be achieved during the aggregation in WSNs. The experiments on both the self-constructed data and the one from reference made satisfied performances.

A cluster-based data aggregation architecture in WSN for Structural Health Monitoring

Abstract: Limited energy is one of the principal challenges in Wireless Sensor Networks (WSNs). In the application of Structural Health Monitoring (SHM), overwhelming data provision is another big problem. Data aggregation condenses raw data into useful information and reduces redundant data transmissions. Consequently, significant energy and data storage are saved, and tasks can be completed more efficiently. However, it is a nontrivial problem to organize the various data aggregation techniques into an integrated architecture on a distributed WSN. In this paper, we propose a cluster-based data aggregation architecture to facilitate application development for efficient SHM. We developed a role-based data aggregation middleware and SQL-like user interface to support flexible task configurations. We also implemented a prototype to evaluate operational flexibility and energy efficiency. Our experimental results indicate that a cluster-based data aggregation mechanism can save energy and optimize the distribution of computing tasks.

Data merge: A data aggregation technique for wireless sensor networks

Abstract: The Wireless Sensor Networks (WSNs) have limited power and communication capabilities, combined with the requirement for long network lifetime. To increase it, methods to reduce energy consumption are highly required. To achieve this goal, we study a data aggregation technique without size reduction, i.e. data merge. It is a generic technique, since it is also usable in applications with heterogeneous data and requirements for high accuracy. This study presents the impact of the data merge technique on WSNs applications executed under various realistic data flow scenarios, traffic loads and wait time intervals. Our results show significant reductions in both packet loss and radio energy consumption.

A Data Aggregation Transfer Protocol Based on Clustering and Data Prediction in Wireless Sensor Networks

Abstract: The energy of wireless sensor networks is limited and the energy consumption of wireless communication takes most of the total energy consumption of sensor nodes. This paper proposes an energy-efficient data aggregation transfer protocol based on clustering and data prediction called DACP. In the initialization phase sensor network nodes send messages to sink node, then sink node divides entire networks into several clusters and elects cluster head nodes for each cluster. In the prediction phase sensor member nodes receive predicted data and compare it with sensed data to decide whether send it or not. In the data aggregation phase cluster head nodes aggregate sensed data received from cluster member nodes and decide to send it to sink node or not according to receiving predicted data. It is effective to reduce data transmission and improve the efficiency of data aggregation by data prediction. Simulation results demonstrate that the proposed protocol can significantly provide longer lifetime and prolong the life of every node to survive.

A Survey of Distributed Data Aggregation Algorithms

Abstract: Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the distributed computation of functions like COUNT, SUM and AVERAGE. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Polynomial Regression Based Secure Data Aggregation for Wireless Sensor Networks

Abstract: In wireless sensor networks, data aggregation protocols are employed to prolong the network lifetime. However, performing data aggregation while preserving security is a challenging problem. This paper presents a polynomial regression based secure data aggregation protocol in which sensor nodes represent their sensed data as polynomial functions. Instead of their original data, sensor nodes secretly send coefficients of these polynomial functions to data aggregators. Data aggregation is performed based on these coefficients and the base station is able to extract a good approximation of the network data from the aggregation result. The security analysis and simulation results show that the proposed scheme is able to reduce the amount of data transmission in the network without compromising data confidentiality.

Energy Efficient Data Aggregation Scheduling in Wireless Sensor Networks

Abstract: Data aggregation is a critical functionality in wireless sensor networks. This paper focuses on enhancing energy and delay efficiency for the data aggregation scheduling problem. In the existing data aggregation scheduling algorithms, a sensor node has to start its radio numerous times to receive all data from its children nodes in a period, and this will waste lots of extra energy and time due to the transceiver turning on and off. According to interference of multiple data transmission and competitor sets of the sensor nodes, we propose an energy and delay efficient algorithm to generate a collision-free schedule called contiguous data aggregation scheduling. Since the sensor nodes consume energy and time for state transitions(transceiver turns on and off), this algorithm assigns to sensor nodes of the same parent node consecutive time slots to reduce the frequency of state transitions. Theoretical analysis and simulation results are used to demonstrate the efficiency of our proposed algorithm. Specifically, compared to the existing data aggregation scheduling algorithms, our proposed algorithm achieves a good tradeoff between energy consumption and delay performance.

Efficient algorithm for maximum lifetime many-to-one data aggregation in wireless sensor networks

Abstract: Data aggregation problem with maximising network lifetime is one of important issues in wireless sensor networks. In this paper, we study the maximum lifetime many-to-one data gathering with aggregation (MLMTODA) problem: given locations of sensors and a base station together with available energy of each sensor, and a set of sources, find a schedule in which data should be gathered from all the sources and transmitted to the base station, such that the lifetime of the network is maximised. We propose efficient algorithms to solve the MLMTODA problem. Our simulations demonstrate that the proposed algorithm has a good performance for the MLMTODA problem.