Showing papers on "Data aggregator published in 2007"

PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks

Abstract: Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive 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. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

SIA: Secure information aggregation in sensor networks

Abstract: In sensor networks, data aggregation is a vital primitive enabling efficient data queries. An on-site aggregator device collects data from sensor nodes and produces a condensed summary which is forwarded to the off-site querier, thus reducing the communication cost of the query. Since the aggregator is on-site, it is vulnerable to physical compromise attacks. A compromised aggregator may report false aggregation results. Hence, it is essential that techniques are available to allow the querier to verify the integrity of the result returned by the aggregator node. We propose a novel framework for secure information aggregation in sensor networks. By constructing efficient random sampling mechanisms and interactive proofs, we enable the querier to verify that the answer given by the aggregator is a good approximation of the true value, even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and average of the measurements, for the estimation of the network size, for finding the minimum and maximum sensor reading, and for random sampling and leader election. Our protocols require only sublinear communication between the aggregator and the user.

Structure-Free Data Aggregation in Sensor Networks

Abstract: Data aggregation protocols can reduce the communication cost, thereby extending the lifetime of sensor networks. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms - data-aware anycast at the MAC layer and randomized waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structure-free data aggregation.

Sharing of Data Utilizing Push Functionality and Privacy Settings

Abstract: A system and method for linking information of one user to other users via a data aggregation server is provided. Various security settings govern the linking of information. Various methodologies for initiating a link of PIM data are also provided including automatic linking, targeted linking and requested linking. Implementations utilizing group profiles are also provided.

Preserving data privacy in outsourcing data aggregation services

Abstract: Advances in distributed service-oriented computing and Internet technology have formed a strong technology push for outsourcing and information sharing. There is an increasing need for organizations to share their data across organization boundaries both within the country and with countries that may have lesser privacy and security standards. Ideally, we wish to share certain statistical data and extract the knowledge from the private databases without revealing any additional information of each individual database apart from the aggregate result that is permitted. In this article, we describe two scenarios for outsourcing data aggregation services and present a set of decentralized peer-to-peer protocols for supporting data sharing across multiple private databases while minimizing the data disclosure among individual parties. Our basic protocols include a set of novel probabilistic computation mechanisms for important primitive data aggregation operations across multiple private databases such as max, min, and top k selection. We provide an analytical study of our basic protocols in terms of precision, efficiency, and privacy characteristics. Our advanced protocols implement an efficient algorithm for performing kNN classification across multiple private databases. We provide a set of experiments to evaluate the proposed protocols in terms of their correctness, efficiency, and privacy characteristics.

Secure and reliable data aggregation for wireless sensor networks

Abstract: This paper presents a data aggregation protocol that ensures security and reliability of aggregated data in the presence of compromised sensor nodes. The proposed protocol relies on a novel trust development algorithm which is used by data aggregators and sensor nodes to ensure the reliability of aggregated data and to select secure and reliable paths. Simulation results show that the proposed protocol improves the security and reliability of aggregated data significantly.

Optimal Policies for Distributed Data Aggregation in Wireless Sensor Networks

Abstract: We consider the scenario of distributed data aggregation in wireless sensor networks, where each sensor can obtain and estimate the information of the whole sensing field through local data exchange and aggregation. The intrinsic trade-off between energy and delay in aggregation operations imposes a crucial question on nodes to decide optimal instants for forwarding their samples. The samples could be composed of the information from their own sensor readings or an aggregation of information with other samples forwarded from neighboring nodes. By considering the randomness of the sample arrival instants and the uncertainty of the availability of the multiaccess communication channel due to the asynchronous nature of information exchange among neighboring nodes, we propose a decision process model to analyze this problem and determine the optimal decision policies at nodes with local information. We show that, once the statistics of the sample arrival and the availability of the channel satisfy certain conditions, there exist optimal control-limit type policies which are easy to implement in practice. In the case that the required conditions are not satisfied, we provide two learning algorithms to solve a finite-state approximation model of the decision problem. Simulations on a practical distributed data aggregation scenario demonstrate the effectiveness of the developed policies, which can also achieve a desired energy-delay tradeoff.

Method and system for secure data aggregation in wireless sensor networks

Abstract: A method for transmitting sensed data in a wireless sensor network including multiple sensors, includes: encrypting the sensed data with an encryption key and a verification key to generate encrypted data in each of the multiple sensors that senses data; wirelessly receiving the encrypted data from the multiple sensors; determining that the sensed data from one of the multiple sensors is different from the sensed data from others of the multiple sensors without decrypting the encrypted data; and transmitting the encrypted sensed data determined to be different.

Concealed Data Aggregation in Heterogeneous Sensor Networks using Privacy Homomorphism

Abstract: Data aggregation is implemented in wireless sensor networks to reduce data redundancy and to summarize relevant and necessary information without requiring all pieces of the data. The benefit of data aggregation can be maximized by implementing it at every data aggregator on the path to the base station. However, data confidentiality requires sensor nodes to encrypt their data prior to transmission. Moreover, once data is encrypted by a sensor node, it should be decrypted at the base station to maintain end-to-end security. This makes the implementation of data aggregation very difficult because data aggregation algorithms require encrypted data to be decrypted. Consequently, data aggregation and secure communication have conflicts in their implementation. To achieve data aggregation and secure communication together, this paper employs privacy homomorphism which offers end-to-end concealment of data and ability to operate on ciphertexts. In the proposed protocol, the computational overhead imposed by the privacy homomorphic encryption functions is tolerated by employing a set of powerful nodes, called AGGNODEs.

A Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Aggregation in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage rather than injecting snap-shot queries into the network that contain data-reducing operators (e.g. MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this paper we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We present theoretical performance bounds of our algorithm and include simulation results which indicate energy savings of up to 80\% when compared to collecting raw data.

Toward Optimal Data Aggregation in Random Wireless Sensor Networks

Abstract: Data gathering is one of the most important services provided by wireless sensor networks (WSNs). Since the predominant traffic pattern in data gathering services is many-to-one communication, it is critical to understand the limitations of many-to-one information flows and devise efficient data aggregation protocols to support prolonged operations in WSNs. In this paper, we provide a theoretical characterization of data aggregation processes under different communication modalities in WSNs. We demonstrate that data aggregation rates of Theta(log(n)/n) and Theta(1) are optimal when operating in fading environments with power path-loss exponents that satisfy 2 4, respectively. Furthermore, the optimal rate can be achieved using a generalization of cooperative beam-forming called cooperative time-reversal communication. In contrast, the non-cooperative multihop relay strategies widely adopted in literature are shown to be suboptimal in the low-to-medium attenuation regime (for 2 < alpha < 4).

Secure data aggregation with multiple encryption

Abstract: Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. Data aggregation consists of processing data collected by source nodes at each intermediate node enroute to the sink in order to reduce redundancy and minimize bandwidth usage. The deployment of sensor networks in hostile environments call for security measures such as data encryption and authentication to prevent data tampering by intruders or disclosure by compromised nodes. Aggregation of encrypted and/or integrity-protected data by intermediate nodes that are not necessarily trusted due to potential node compromise is a challenging problem. We propose a secure data aggregation scheme that ensures that sensors participating to the aggregation mechanism do not have access to the content of the data while adding their sensed values thanks to the use of an efficient homomorphic encryption scheme. We provide a layered secure aggregation mechanism and the related key attribution algorithm that limits the impact of security threats such as node compromises. We also evaluate the robustness of the scheme against node failures and show that such failures are efficiently recovered by a small subset of nodes that are at most m hops away from the failure.

Time-decaying sketches for sensor data aggregation

Abstract: We present a new sketch for summarizing network data. The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks: the sketch is duplicate-insensitive, i.e. re-insertions of the same data will not affect the sketch, and hence the estimates of aggregates. Unlike previous duplicate-insensitive sketches for sensor data aggregation [26,12], it is also time-decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function. The sketch can give provably approximate guarantees for various aggregates of data, including the sum, median, quantiles, and frequent elements. The size of the sketch and the time taken to update it are both polylogarithmic in the size of the relevant data. Further, multiple sketches computed over distributed data can be combined without losing the accuracy guarantees. To our knowledge, this is the first sketch that combines all the above properties.

Distributed Data Agregation with Geographical Routing in Wireless Sensor Networks

Abstract: In wireless sensor networking applications, gathering sensed information and relaying it to the sink node using multi-hop communication in an energy efficient manner is of paramount importance. In this paper we present the idea of using aggregator nodes in order to decrease the amount of packets sent, hence reducing the energy required for communication. We introduce DDAP, a self-organizing distributed data aggregation protocol that uses randomly chosen aggregator nodes (ANs). We propose an extension of GOAFR, a robust geographical routing algorithm, collaborating with DDAP called geographical routing with aggregation nodes (GRAN). Simulation results evaluating the performance of DDAP and GRAN are presented. We show that by using DDAP and GRAN significant reduction in data traffic can be achieved, resulting in power saving and thus network lifetime prolongation.

SEDAN: Secure and Efficient protocol for Data Aggregation in wireless sensor Networks

Abstract: Energy is a scarce resource in Wireless Sensor Networks. Some studies show that more than 70% of energy is consumed in data transmission. Since most of the time, the sensed information is redundant due to geographically collocated sensors, most of this energy can be saved through data aggregation. Furthermore, data aggregation improves bandwidth usage. Unfortunately, while aggregation eliminates redundancy, it makes data integrity verification more complicated since the received data is unique. In this paper, we present a new protocol that provides secure aggregation for wireless sensor networks. Our protocol is based on a two hops verification mechanism of data integrity. Our solution is essentially different from existing solutions in that it does not require referring to the base station for verifying and detecting faulty aggregated readings, thus providing a totally distributed scheme to guarantee data integrity. We carried out simulations using TinyOS environment. Simulation results show that the proposed protocol yields significant savings in energy consumption while preserving data integrity.

Financial Privacy: An International Comparison of Credit Reporting Systems

Abstract: Theory of Information and Privacy.- Overview of Credit Reporting Systems.- Regulation of Credit Reporting.- Economic Effects of Credit Reporting.- Conclusions.

Accurate data aggregation for VANETs

Abstract: Data aggregation is an important issue for vehicular ad-hoc networks (VANETs). Congestion notification applications are built to warn drivers of traffic slowdowns far enough in advance that the drivers may take alternate routes. Data that is broadcast should be self-contained and fit into a single MAC-layer frame. With dense traffic, aggregation is needed to represent a large number of vehicles in relatively small frame. We present a new technique for aggregating vehicles' data without losing accuracy. Vehicles build a local view based on speed and position reports from neighboring vehicles. This local view, representing vehicles up to 1.6 km ahead, is then aggregated into a single frame and broadcast. Vehicles use received aggregated frames to extend their views even farther.

Computing Aggregation Function Minimum/Maximum using Homomorphic Encryption Schemes in Wireless Sensor Networks (WSNs).

Abstract: Data aggregation in wireless sensor networks (WSN) helps eliminate information redundancy and increase the lifetime of the network. When homomorphic encryption is used for data aggregation, end-to-end encryption is achieved and aggregation function like average or minimum/maximum can be computed on the encrypted data. Aggregation functions like minimum/maximum rely on comparison operation. But, it has been shown that any homomorphic encryption is insecure against ciphertext only attacks if they support comparison operation. The order preserving encryption scheme (OPES) has been suggested for WSNs, for secure comparison of encrypted data at the aggregator node in WSNs. But, the computational cost at the sensor nodes in WSNs by using OPES is huge. This paper provides an alternative for OPES when used to calculate aggregation function minimum/maximum. In this paper we briefly describe some homomorphic encryption schemes and show how the sensed data is encrypted by using these homomorphic encryption schemes. we show how aggregation function minimum/maximum can be computed at the aggregator node in WSNs by performing addition operation and not comparison operation on the data encrypted with homomorphic encryption schemes. We also show how our scheme helps eliminate the encryption cost at the sensor node in WSNs.

Balancing Interactive Data Management of Massive Data with Situational Awareness through Smart Aggregation

Abstract: Designing a visualization system capable of processing, managing, and presenting massive data sets while maximizing the user's situational awareness (SA) is a challenging, but important, research question in visual analytics. Traditional data management and interactive retrieval approaches have often focused on solving the data overload problem at the expense of the user's SA. This paper discusses various data management strategies and the strengths and limitations of each approach in providing the user with SA. A new data management strategy, coined Smart Aggregation, is presented as a powerful approach to overcome the challenges of both massive data sets and maintaining SA. By combining automatic data aggregation with user-defined controls on what, how, and when data should be aggregated, we present a visualization system that can handle massive amounts of data while affording the user with the best possible SA. This approach ensures that a system is always usable in terms of both system resources and human perceptual resources. We have implemented our Smart Aggregation approach in a visual analytics system called VIAssist (Visual Assistant for Information Assurance Analysis) to facilitate exploration, discovery, and SA in the domain of Information Assurance.

Cora: correlation-based resilient aggregation in sensor networks

Abstract: In this paper we consider the problem of resilient data aggregation, namely, when aggregation has to be performed on a compromised sample. We present a statistical framework that is designed to mitigate the effects of an attacker who is able to alter the values of the measured parameters of the environment around some of the sensor nodes. Our proposed framework takes advantage of the naturally existing correlation between the sample elements, which is very rarely considered in other sensor network related papers. The algorithms presented are to be applied without assumption on the sensor network's sampling distribution or on the behaviour of the attacker. The effectiveness of the algorithms is formally evaluated.

Timing control for delay-constrained data aggregation in wireless sensor networks

Abstract: Timing control for delay-constrained data aggregation is critical for improving the performance of a wireless sensor network. In this paper, we propose an adaptive timing control (ATC) mechanism to determine the proper time for data aggregation and forwarding. Given the maximum delay requirements of an application, the ATC mechanism determines the aggregation time for sensor nodes based on both the locations of the nodes and the number of children in the data aggregation tree. The nodes with more children are allocated a longer waiting time. The ATC mechanism maximizes the opportunity for data aggregation and ensures sufficient time to process data from the children. Analytical and simulation results show that the ATC mechanism leads to a higher data delivery rate and lower energy costs compared with existing similar algorithms. Moreover, since a lot of data packets can arrive at the sink earlier, the ATC mechanism is capable of responding more quickly to users. Copyright © 2006 John Wiley & Sons, Ltd.

An Ant Colony Algorithm for Data Aggregation in Wireless Sensor Networks

Abstract: This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. Our goal is to minimize the number of non-source nodes in the tree to save energies. In this paper, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore some paths from source node to sink node. The data aggregation tree will be constructed by the accumulated pheromone. The simulations have shown that our algorithm can deduce significant energy cost.

Hierarchical and fault-tolerant data aggregation in wireless sensor networks

Abstract: This paper presents three hierarchical algorithms for data aggregation in wireless sensor networks where sensors can crash and recover. The network is divided in several regions. The algorithms ensure (i) the selection of a common data aggregator sensor within each region, in charge of the collection of local data, and (ii) the selection of a unique super-aggregator sensor, in charge of the collection of global data, among all the aggregators. Both selections are achieved by implementing the Omega failure detector, which provides a self-organizing and fault-tolerant leader election service. We also introduce a battery depletion threshold to provide wireless sensor network QoS

Sensor Scheduling for Aggregate Monitoring inWireless Sensor Networks

Abstract: Most of the applications of wireless sensor networks involve primarily data collection with in-network processing in which continuous aggregate queries are posed and processed. There are two principle concerns with this type of applications. First, due to the use of batteries, limited power resource has been identified as a major challenge in deploying wireless sensor networks. Second, data is usually expected to be gathered as soon as possible to facilitate the monitoring of and the response to the physical phenomena. In this paper, we tackle these challenges through sensor state scheduling. The proposed technique is based on the observation that there are two types of traffic in sensor networks designed for data aggregation, bottom-up and top-down within an abstract tree structure. We show that it is possible to achieve deterministic schedules for data aggregation with very good performance. Specifically, we develop greedy algorithms to schedule transmission and listening operations for each sensor node to achieve collision- free communication. We show that the schedules can maximize the time sensor nodes spent on low-power states which helps achieve great energy efficiency, as well as allow fast data aggregation.

Data aggregation in sensor networks: balancing communication and delay costs

Abstract: In a sensor network the sensors, or nodes, obtain data and have to communicate these data to a central node. Because sensors are battery powered they are highly energy constrained. Data aggregation can be used to combine data of several sensors into a single message, thus reducing sensor communication costs at the expense of message delays. Thus, the main problem of data aggregation is to balance the communication and delay costs. In this paper we study the data aggregation problem as a bicriteria optimization problem; the objectives we consider are to minimize maximum energy consumption of a sensor and a function of the maximum latency costs of a message. We consider distributed algorithms under an asynchronous time model, and under an almost synchronous time model, where sensor clocks are synchronized up to a small drift. We use competitive analysis to assess the quality of the algorithms.

Applying Secure Data Aggregation techniques for a Structure and Density Independent Group Based Key Management Protocol

Abstract: In many applications of Wireless Sensor Networks a Sink is interested in aggregated data instead of exact values from all sensors. To send aggregated data, it is also helpful to reduce the amount of data to be transmitted and thereby conserve energy. Indeed current in-network aggregation schemes are helpful to conserve energy but they are designed without considering possible security issues related to data privacy. Often we find wireless sensor networks designed with neighbouring nodes sharing keys or with decryption at aggregator nodes. In either situation the potential for aggregator nodes to be physically compromised means data privacy is at high risk. Therefore secure data aggregation is desirable where data can be aggregated without the need for decryption at aggregator nodes. Aggregation becomes especially challenging if end-to-end privacy between a source and a destination (sink or group leader) is required. In this paper we extend our Structure and Density Independent Group Based Key Management Protocol with the additional feature of secure data aggregation to provide better data privacy to every single node in a large scale Wireless Sensor Network.

Forecast-based temporal data aggregation in wireless sensor networks

Abstract: Data aggregation is an important research area in Wireless Sensor Networks(WSN).In WSN,using data aggregation technique can bring the following benefits:saving energy,improving data gathering efficiency,enhancing data accuracy,getting integrated information and so on.Time series analysis is a statistical method which is used to reveal dynamic architecture and changing rule of certain system according to dynamic data.In this paper,a forecast-based temporal data aggregation technique is proposed based on time series model and time series forecast method.The effect and performance of the method is verified and evaluated by simulation using the temperature data collected by an environment monitoring network deployed in Forbidden City.The results show that autoregressive(AR) algorithm is more effective than others for WSN.When error threshold is 0.05 ℃ to 0.50 ℃,forecast success ratio is 21% to 83%.When error threshold is 0.05 ℃,energy saving is up to 68%.

Adaptive data aggregation for clustered wireless sensor networks

Abstract: Wireless sensor network (WSN) has emerged as an event-driven paradigm based on the collective effort of numerous sensing nodes. Due to the dynamic topology and random deployment, incorporating adaptive behavior into protocols in WSNs is important. Hence, we propose an adaptive data aggregation (ADA) scheme for the clustered WSNs. In ADA scheme, the temporal aggregation degree controlled by the reporting frequency at sensor nodes and the spatial aggregation degree controlled by the aggregation degree at Cluster Heads (CHs) are determined by the current scheme state according to the observed reliability. Furthermore, the ADA scheme is mainly performed at the sink node, with a few functions at CHs and sensor nodes. Performance results show that the scheme state converges to the desired reliability starting from any initial state.