Showing papers on "Data aggregator published in 2010"

Secure Information Aggregation for Smart Grids Using Homomorphic Encryption

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 neighborhood or any arbitrary set of designated nodes with minimum overhead. To protect user privacy, homomorphic encryption is used to secure the data en route. 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.

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 news's and highlights the challenges in clustering.

PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems

Abstract: People-centric urban sensing is a new paradigm gaining popularity. A main obstacle to its widespread deployment and adoption are the privacy concerns of participating individuals. To tackle this open challenge, this paper presents the design and evaluation of PriSense, a novel solution to privacy-preserving data aggregation in people- centric urban sensing systems. PriSense is based on the concept of data slicing and mixing and can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max/Min, Median, Histogram, and Percentile with accurate aggregation results. PriSense can support strong user privacy against a tunable threshold number of colluding users and aggregation servers. The efficacy and efficiency of PriSense are confirmed by thorough analytical and simulation results.

Integration of false data detection with data aggregation and confidential transmission in wireless sensor networks

Abstract: In wireless sensor networks, compromised sensor nodes can inject false data during both data aggregation and data forwarding. The existing false data detection techniques consider false data injections during data forwarding only and do not allow any change on the data by data aggregation. However, this paper presents a data aggregation and authentication protocol, called DAA, to integrate false data detection with data aggregation and confidentiality. To support data aggregation along with false data detection, the monitoring nodes of every data aggregator also conduct data aggregation and compute the corresponding small-size message authentication codes for data verification at their pairmates. To support confidential data transmission, the sensor nodes between two consecutive data aggregators verify the data integrity on the encrypted data rather than the plain data. Performance analysis shows that DAA detects any false data injected by up to T compromised nodes, and that the detected false data are not forwarded beyond the next data aggregator on the path. Despite that false data detection and data confidentiality increase the communication overhead, simulation results show that DAA can still reduce the amount of transmitted data by up to 60% with the help of data aggregation and early detection of false data.

Effective Determination of Mobile Agent Itineraries for Data Aggregation on Sensor Networks

Abstract: A key feature of wireless sensor networks (WSNs) is the collaborative processing, where the correlation existing over the local data of sensor nodes (SNs) is exploited so that the total data volume can be reduced (data aggregation). The use of Mobile Agents (MAs), i.e., software entities able of migrating among nodes and resuming execution naturally, fits in this scenario; the local data of an SN can be combined with the data collected by an MA from other SNs in a way that depends on the specific program code of the MA. In this paper, we consider the problem of calculating near-optimal routes for MAs that incrementally aggregate the data as they visit the nodes in a distributed sensor network. Our algorithm follows a greedy-like approach always selecting the next node to be included in an itinerary in such a way that the cost of the so far formed itineraries is kept minimum at each step. Simulation results confirm the high effectiveness of the proposed algorithm as well as its performance gain over alternative approaches. Also, with the use of proper data structures, the computational complexity of the algorithm is kept low as it is formally proved in the paper.

A General Framework for Using Aggregation in Visual Exploration of Movement Data

Abstract: To be able to explore visually large amounts of movement data, it is necessary to apply methods for aggregation and summarisation of the data. The goal of our research has been to systemize the possible approaches to aggregation of movement data into a framework clearly defining what kinds of exploratory tasks each approach is suitable for. On the basis of a formal model of movement of multiple entities, we consider two possible views of movement data, situation-oriented and trajectory-oriented. For each view, we discuss the appropriate methods of data aggregation and the visualisation techniques representing the results of aggregation and supporting data exploration. Special attention is given to dynamic aggregation working in combination with interactive filtering and classification of movement data (CR categories and subject descriptors: H.1·2 [user/machine systems]: human information processing - visual analytics; I.6·9 [visualisation]: information visualisation).

Secure Lossless Aggregation for Smart Grid M2M Networks

Abstract: Whilst security is generally perceived as an important constituent of communication systems, this paper offers a viable security-communication-tradeoff particularly tailored to Advanced Metering Infrastructures (AMIs) in Smart Grid systems. These systems, often composed of embedded nodes with highly constrained resources, require e.g.~metering data to be delivered efficiently whilst neither jeopardizing communication nor security. Data aggregation is a natural choice in such settings, where the challenge is to facilitate per-hop as well as end-to-end security. The prime contribution of this paper is to propose a secure aggregation protocol that meets the requirements of Smart Grids, and to analyze its efficiency considering various system configurations as well as the impact of the wireless channel through packet error rates. Relying on analysis and corroborative simulations, unprecedented design guidelines are derived which determine the operational point beyond which aggregation is useful as well quantifying the superiority of our protocol w.r.t. non-aggregated solutions.

A review on data aggregation techniques in wireless sensor network

Abstract: Wireless sensor networks (WSNs) consist of many sensor nodes. These networks have huge application in habitat monitoring, disaster management, security and military, etc. Wireless sensor nodes are very small in size and have limited processing capability with very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation may be effective technique in this context because it reduces the number of packets to be sent to sink by aggregating the similar packets. In this paper we put our attention into various data aggregation algorithms in wireless sensor network. Data aggregation technique increases the lifetime of sensor network by decreasing the number of packets to be sent to sink or base station. Here, we first explore the data aggregation algorithms on the basis of network topology, then we explore various trade offs in data aggregation algorithms and finally we highlight security issues in data aggregation.

Data aggregation in constructing composite indicators: A perspective of information loss

Abstract: Composite indicators (CIs) have been widely accepted as a useful tool for performance comparisons, public communication and decision support in a wide spectrum of fields, e.g. economy, environment and knowledge/information/innovation. The quality and reliability of a CI depend heavily on the underlying construction scheme where data aggregation is a major step. This paper analyzes the data aggregation problem in CI construction from the point of view of information loss. Based on the ''minimum information loss'' principle, the distance-based and entropy-based aggregation models for constructing CIs are presented. The entropy-based aggregation model has also been extended to deal with qualitative data. It is shown that the proposed aggregation models have close relationships with several popular MCDA aggregation methods in CI construction, although our proposed models seem to be more flexible while more complex in application. Two case studies are presented to illustrate the use of the proposed aggregation models.

Optimized Data Aggregation in WSNs Using Adaptive ARMA

Abstract: Wireless sensor networks (WSNs) are data centric networks to which data aggregation is a central mechanism. Nodes in such networks are known to be of low complexity and highly constrained in energy. This requires novel distributed algorithms to data aggregation, where accuracy, complexity and energy need to be optimized in the aggregation of the raw data as well as the communication process of the aggregated data. To this end, we propose in this work a distributed data aggregation scheme based on an adaptive Auto-Regression Moving Average (ARMA) model estimation using a moving window technique and running over suitable communications protocols. In our approach, we balance the complexity of the algorithm and the accuracy of the model so as to facilitate the implementation. Subsequent analysis shows that an aggregation efficiency of up to 60% can be achieved with a very fine accuracy of 0.03 degree. And simulation results confirm that this distributed algorithm provides significant energy savings (over 80%) for mass data collection applications.

Secure encrypted-data aggregation for wireless sensor networks

Abstract: This paper proposes a secure encrypted-data aggregation scheme for wireless sensor networks. Our design for data aggregation eliminates redundant sensor readings without using encryption and maintains data secrecy and privacy during transmission. Conventional aggregation functions operate when readings are received in plaintext. If readings are encrypted, aggregation requires decryption creating extra overhead and key management issues. In contrast to conventional schemes, our proposed scheme provides security and privacy, and duplicate instances of original readings will be aggregated into a single packet. Our scheme is resilient to known-plaintext attacks, chosen-plaintext attacks, ciphertext-only attacks and man-in-the-middle attacks. Our experiments show that our proposed aggregation method significantly reduces communication overhead and can be practically implemented in on-the-shelf sensor platforms.

Collaborative, privacy-preserving data aggregation at scale

Abstract: Combining and analyzing data collected at multiple administrative locations is critical for a wide variety of applications, such as detecting malicious attacks or computing an accurate estimate of the popularity of Web sites. However, legitimate concerns about privacy often inhibit participation in collaborative data aggregation. In this paper, we design, implement, and evaluate a practical solution for privacy-preserving data aggregation (PDA) among a large number of participants. Scalability and efficiency is achieved through a "semi-centralized" architecture that divides responsibility between a proxy that obliviously blinds the client inputs and a database that aggregates values by (blinded) keywords and identifies those keywords whose values satisfy some evaluation function. Our solution leverages a novel cryptographic protocol that provably protects the privacy of both the participants and the keywords, provided that proxy and database do not collude, even if both parties may be individually malicious. Our prototype implementation can handle over a million suspect IP addresses per hour when deployed across only two quad-core servers, and its throughput scales linearly with additional computational resources.

MDPA: multidimensional privacy-preserving aggregation scheme for wireless sensor networks

Abstract: In this paper, we propose a novel multidimensional privacy-preserving data aggregation scheme for improving security and saving energy consumption in wireless sensor networks (WSNs). The proposed scheme integrates the super-increasing sequence and perturbation techniques into compressed data aggregation, and has the ability to combine more than one aggregated data into one. Compared with the traditional data aggregation schemes, the proposed scheme not only enhances the privacy preservation in data aggregation, but also is more efficient in terms of energy costs due to its unique multidimensional aggregation. Extensive analyses and experiments are given to demonstrate its energy efficiency and practicability. Copyright © 2009 John Wiley & Sons, Ltd. In this paper, we propose a novel multidimensional privacy-preserving data aggregation scheme for improving security and saving energy consumption in wireless sensor networks (WSNs). The proposed scheme integrates the super-increasing sequence and perturbation techniques into compressed data aggregation, and has the ability to combine more than one aggregated data into one. Compared with the traditional data aggregation schemes, the proposed scheme not only enhances the privacy preservation in data aggregation, but also is more efficient in terms of energy costs due to its unique multidimensional aggregation. Extensive analyses and experiments are given to demonstrate its energy efficiency and practicability.

Resilient secure aggregation for vehicular networks

Abstract: Innovative ways to use ad hoc networking between vehicles are an active research topic and numerous proposals have been made for applications that make use of it. Due to the bandwidth-limited wireless communication medium, scalability is one crucial factor for the success of these future protocols. Data aggregation is one solution to accomplish such scalability. The goal of aggregation is to semantically combine information and only disseminate this combined information in larger regions. However, the integrity of aggregated information cannot be easily verified anymore. Thus, attacks are possible resulting in lower user acceptance of applications using aggregation or, even worse, in accidents due to false information crafted by a malicious user. Therefore, it is necessary to design novel mechanisms to protect aggregation techniques. However, high vehicle mobility, as well as tight bandwidth constraints, pose strong requirements on the efficiency of such mechanisms. We present new security mechanisms for semantic data aggregation that are suitable for use in vehicular ad hoc networks. Resilience against both malicious users of the system and wrong information due to faulty sensors are taken into consideration. The presented mechanisms are evaluated with respect to their bandwidth overhead and their effectiveness against possible attacks.

Parameter-Based Data Aggregation for Statistical Information Extraction in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have a broad range of applications, such as battlefield surveillance, environmental monitoring, and disaster relief. These networks usually have stringent constraints on the system resources, making data-extraction and aggregation techniques critically important. However, accurate data extraction and aggregation is difficult, due to significant variations in sensor readings and frequent link and node failures. To address these challenges, we propose data-aggregation techniques based on statistical information extraction that capture the effects of aggregation over different scales. We also design, in this paper, an accurate estimation of the distribution parameters of sensory data using the expectation-maximization (EM) algorithm. We demonstrate that the proposed techniques not only greatly reduce the communication cost but also retain valuable statistical information that is otherwise lost in many existing data-aggregation approaches for sensor networks. Moreover, simulation results show that the proposed techniques are robust against link and node failures and perform consistently well in broad scenarios with various network configurations.

Privacy-preserving data aggregation protocols for wireless sensor networks: a survey.

Abstract: Many wireless sensor network (WSN) applications require privacy-preserving aggregation of sensor data during transmission from the source nodes to the sink node. In this paper, we explore several existing privacy-preserving data aggregation (PPDA) protocols for WSNs in order to provide some insights on their current status. For this, we evaluate the PPDA protocols on the basis of such metrics as communication and computation costs in order to demonstrate their potential for supporting privacy-preserving data aggregation in WSNs. In addition, based on the existing research, we enumerate some important future research directions in the field of privacy-preserving data aggregation for WSNs.

READA: Redundancy Elimination for Accurate Data Aggregation in Wireless Sensor Networks

Abstract: In monitoring systems, multiple sensor nodes can detect a single target of interest simultaneously and the data collected are usually highly correlated and redundant. If each node sends data to the base station, energy will be wasted and thus the network energy will be depleted quickly. Data aggregation is an important paradigm for compressing data so that the energy of the network is spent efficiently. In this paper, a novel data aggregation algorithm called Redundancy Elimination for Accurate Data Aggregation (READA) has been proposed. By exploiting the range of spatial correlations of data in the network, READA applies a grouping and compression mechanism to remove duplicate data in the aggregated set of data to be sent to the base station without largely losing the accuracy of the final aggregated data. One peculiarity of READA is that it uses a prediction model derived from cached values to confirm whether any outlier is actually an event which has occurred. From the various simulations conducted, it was observed that in READA the accuracy of data has been highly preserved taking into consideration the energy dissipated for aggregating the data.

Tradeoffs among Delay, Energy and Accuracy of Partial Data Aggregation in Wireless Sensor Networks

Abstract: Due to the Recent development in wireless technology, wireless sensor networks attract researchers’ attention because of their applicability in many fields for effective collection of sensing data with low cost. Wireless sensor networks have many applications; some of the applications are military application, environmental application and flood detection. For example, in an environmental application for forest fire detection, the sensor nodes sense the fire information, then transmit or relay the information to base station in a multi-hop way. In wireless sensor networks, energy saving is critical issue as sensor nodes are battery-powered. Here we propose, partial data aggregation as one of the energy saving technique. In this paper, we analyze the tradeoffs among communication delay, energy consumption, and data accuracy of the partial data aggregation technique and discuss the results. First, we analyze the partial data aggregation with Markovian chain; analytical result shows that, non-aggregation method suffers large energy consumption while full aggregation suffers long transmission delay. From the analysis results, we find that the proposed partial aggregation method WRP (Waterfalls Random partial aggregation) can trade off energy consumption and transmission delay. Thus, we discuss the tradeoffs among data accuracy, transmission delay and energy consumption with different criteria and parameters. The results show that we could control the significance of transmission delay, energy consumption and data accuracy by tradeoffs index (TOI). We also analyze the several applications of wireless sensor networks with different significance based on the TOI. From the observed results, we found that we could set the significance of transmission delay, energy consumption and data accuracy for different applications based on different criteria TOI. Thus, by evaluating and comparing the criteria with different data generation rate as well as aggregation factor, we get the least TOI value, which denotes the desired tradeoffs among them.

Flexible On-Board Stream Processing for Automotive Sensor Data

Abstract: Vehicle testing and diagnosis requires huge amounts of data to be gathered and analyzed. Not all possibly interesting data can be stored because of the limited memory available in a tested vehicle. On-board preprocessing of data and decisions about which information has to be kept or omitted is thus vital for vehicle testing routines. This paper introduces a method for flexible on-board processing of sensor data of a vehicle. The approach is motivated by sensor network ideas and makes use of stream processing techniques. A processing graph model for automotive applications is proposed, which consists of operator nodes and connecting data streams. This model supplies both recording and processing functionality together. To account for dynamic changes of conditions within a vehicle-most of the time only a small portion of the vehicle states are interesting for diagnosis-both the model and actual software are built in such a way that the whole system can automatically be adapted at runtime whenever certain conditions are detected. The proposed stream processing model has been implemented in a proof-of-concept industrial application, that was deployed to an automotive on-board unit. Results show that this approach effectively trades a little more on-board processing power for a large data volume, that does not need to be saved and transmitted for off-board usage anymore.

Method for collecting data and system for accomplishing the same

Abstract: A method for collecting data is disclosed herein. The method involves selecting, via a processor associated with a telematics service center, a mobile vehicle to collect data from a sensor configured to wirelessly communicate with one or more selected vehicles and, via a telematics unit disposed in the selected mobile vehicle, receiving data collected by the sensor. The method further involves, via the telematics unit, transmitting the data from the telematics unit to a data aggregator and reporting the data from the data aggregator to a facility. Also disclosed herein is a system for accomplishing the same.

SEEDA: Secure end-to-end data aggregation in Wireless Sensor Networks

Abstract: Wireless Sensor Network (WSN) consists of a large number of nodes with limited sensing, computation and communication capabilities. In such network consisting of resource constrained nodes, data transmission is a energy-consuming operation. Hence to increase the lifetime of the network it is essential to reduce the number of bits transmitted. One widely used method for reducing the data transmission is data aggregation. The security issues such as data integrity, confidentiality and freshness in data aggregation become crucial when the WSN is deployed in a remote or hostile environment where sensors are prone to node failures and compromises. Secure data aggregation schemes are suitable to achieve security in data aggregation. In this paper we propose a Secure Data Aggregation scheme which provides end-to-end data privacy. The average number of bits transmitted per node is reduced by 30%–50% compared to the scheme in [16].

Constructing Maximum-Lifetime Data Gathering Trees in Sensor Networks with Data Aggregation

Abstract: This paper studies the problem of constructing maximum-lifetime data gathering trees in sensor networks in which the power levels of sensors are heterogeneous and adjustable. In-network data aggregation is also employed to aggregate sensor data while they are being forwarded toward the base station. For sensor networks in which sensors have fixed and the same transmission power level, Wu et al. has derived an upper bound on the lifetime of the optimal data gathering tree and developed an approximation algorithm for constructing data gathering trees. The model considered in this paper is more general than that considered by Wu et al. in that the transmission power levels of sensors are heterogeneous and adjustable. For this more general model, this paper derives an upper bound on the lifetime of the optimal data gathering tree. Given an initial tree, an algorithm is developed to construct a data gathering tree by iteratively rearranging the current tree and improving the lifetime of the current tree. The worst-case computational complexity of the algorithm is shown to be polynomial.

Data collection from utility meters over advanced metering infrastructure

Abstract: Communicating data via an advanced metering infrastructure (AMI). An infrastructure is disclosed that includes: a plurality of communication modules incorporated into a plurality of associated utility meters; a data aggregator configured for communicating with each of the plurality of communication modules, wherein the data aggregator includes a system for translating meter specific data formats into an aggregated format that includes data quality attributes and a timestamp, and includes a system for synchronizing aggregated data over a back haul; and a head end system having a communication management system that receives and processes synchronization messages from the data aggregator received over the back haul, wherein the head end system includes a metering system for requesting and obtaining meter data from the associated utility meters via the data aggregator, and issuing signals to individual meters and groups of meters.

A Cross Pruning Framework for Top-k Data Collection in Wireless Sensor Networks

Abstract: Energy conservation is a key issue for algorithm designs in wireless sensor networks. In this paper, we explore in-network aggregation techniques for answering top-k queries in wireless sensor networks. A top-k query retrieves the k data objects with the highest scores evaluated by a scoring function on interested features of sensor readings. Our study shows that existing techniques for processing top-k query, e.g., Tiny AGgregation Service (TAG), are not energy efficient due to deficiencies in their routing structures and data aggregation mechanisms. To address these deficiencies, we propose to develop a new cross pruning (XP) aggregation framework for top-k data collection in wireless sensor networks. The XP framework incorporates several novel ideas to facilitate efficient in-network aggregation and filtering, including 1) building a cluster-tree routing structure to aggregate more objects locally; 2) adopting a broadcast-then-filter approach for efficiently suppressing redundant data transmissions; and 3) providing a cross pruning technique to enhance in-network filtering effectiveness. An extensive set of experiments based on simulation has been conducted to evaluate the performance of TAG and the proposed XP framework. The experimental results validate our proposals and show that XP significantly outperforms TAG in energy cost.

Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks

Abstract: Extending the lifetime of wireless sensor networks remains the most challenging and demanding requirement that impedes large-scale deployments. The basic operation in WSNs is the systematic gathering and transmission of sensed data to a base station for further processing. During data gathering, the amount of data can be large sometimes, due to redundant data combined from different sensing nodes in the neighborhood. Thus the data gathered need to be processed before being transmitted, in order to detect and remove redundancy, which can impact the communication traffic and energy consumption of the network in a negative way. In this paper, we propose an algorithm to measure similarity between the data collected toward the base station(relative to a specific event monitoring), so that an aggregator sensor sends a minimum amount of information to the base station in a way that the latter can deduce the source information of sensing neighbors nodes. Further, our experimental results demonstrate that the communication traffic and the number of bits transmitted can be minimized while preserving accuracy on the base station estimations.

Effect of data aggregation on wireless sensor network performance

Abstract: Wireless Sensor Network has limited battery power and computational power, leads to increased complexity A better data aggregation framework on wireless sensor networks gives an efficient battery power and improved lifetime This can be achieved by making the framework as middleware for aggregating data, measured by a number of nodes within a network In this paper the performance of TAG in terms of energy efficiency in comparison with and without data aggregation in wireless sensor networks is presented

SAS: A Secure Data Aggregation Scheme in Vehicular Sensing Networks

Abstract: Vehicular ad hoc networks support a wide range of promising applications including vehicular sensing networks, which enable vehicles to cooperatively collect and transmit the aggregated traffic data for the purpose of traffic monitoring. The reported literatures mainly focus on how to achieve the data aggregation in dynamic vehicular environment while the security issue especially on the authenticity and integrity of aggregation results receive less attention. In this study, we introduce a secure probabilistic data aggregation scheme based on Flajolet-Martin sketch and \emph{sketch proof} technique. We also discuss the tradeoff between the bandwidth efficiency and the estimation accuracy. Extensive simulations and analysis demonstrate the efficiency and effectiveness of the proposed scheme.

RDAS: Reputation-Based Resilient Data Aggregation in Sensor Network

Abstract: Data aggregation in wireless sensor networks is vulnerable to security attacks and natural failures. A few nodes can drastically alter the result of the aggregation by reporting erroneous data. In this paper we present RDAS, a robust data aggregation protocol that uses a reputation based approach to identify and isolate malicious nodes in a sensor network. RDAS is based on a hierarchical clustering arrangement of nodes, where a cluster head analyzes data from the cluster nodes to determine the location of an event. It uses the redundancy of multiple nodes sensing an event to determine what data should have been reported by each node. Nodes form part of a distributed reputation system, where they share information about other node's performance in reporting accurate data and use the reputation ratings to suppress reports from malicious nodes. RDAS is able to perform accurate data aggregation in the presence of individually malicious and colluding nodes, as well as nodes that try to compromise the integrity of the reputation system by lying about other nodes' behavior. We show that RDAS is more resilient to security attacks with respect to accuracy of event localization than the baseline data aggregation protocol with no security feature.