scispace - formally typeset
Search or ask a question

Showing papers on "Data aggregator published in 2006"


Journal ArticleDOI
TL;DR: This article presents a survey of data-aggregation algorithms in wireless sensor networks and compares and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy.
Abstract: Wireless sensor networks consist of sensor nodes with sensing and com- munication capabilities. We focus on data-aggregation problems in energy- constrained sensor networks. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that net- work lifetime is enhanced. In this article we present a survey of data-aggre- gation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy. We conclude with possible future research directions.

943 citations


Journal ArticleDOI
TL;DR: A secure energy-efficient data aggregation protocol called ESPDA (Energy-Efficient Secure Pattern based Data Aggregation), which outperforms conventional data aggregation methods up to 50% in bandwidth efficiency.

190 citations


Proceedings ArticleDOI
04 Dec 2006
TL;DR: The framework for end-to-end encrypted data aggregation has higher computation cost on the sensor nodes, but achieves stronger security, in comparison with the framework for hop-by-hopencrypted data aggregation.
Abstract: Data aggregation is a widely used technique in wireless sensor networks. The security issues, data confidentiality and integrity, in data aggregation become vital when the sensor network is deployed in a hostile environment. There has been many related work proposed to address these security issues. In this paper we survey these work and classify them into two cases: hop-by-hop encrypted data aggregation and end-to-end encrypted data aggregation. We also propose two general frameworks for the two cases respectively. The framework for end-to-end encrypted data aggregation has higher computation cost on the sensor nodes, but achieves stronger security, in comparison with the framework for hop-by-hop encrypted data aggregation.

141 citations


Proceedings ArticleDOI
01 Jan 2006
TL;DR: Simulation results demonstrate that the trust based framework provides a powerful mechanism for detecting compromised nodes and reasoning about the uncertainty in the network and can purge false data to accomplish robust aggregation in the presence of multiple compromised nodes.
Abstract: In unattended and hostile environments, node compromise can become a disastrous threat to wireless sensor networks and introduce uncertainty in the aggregation results. A compromised node often tends to completely reveal its secrets to the adversary which in turn renders purely cryptography-based approaches vulnerable. How to secure the information aggregation process against compromised-node attacks and quantify the uncertainty existing in the aggregation results has become an important research issue. In this paper, we address this problem by proposing a trust based framework, which is rooted in sound statistics and some other distinct and yet closely coupled techniques. The trustworthiness (reputation) of each individual sensor node is evaluated by using an information theoretic concept, Kullback-Leibler (KL) distance, to identify the compromised nodes through an unsupervised learning algorithm. Upon aggregating, an opinion, a metric of the degree of belief, is generated to represent the uncertainty in the aggregation result. As the result is being disseminated and assembled through the routes to the sink, this opinion will be propagated and regulated by Josang's belief model. Following this model, the uncertainty within the data and aggregation results can be effectively quantified throughout the network. Simulation results demonstrate that our trust based framework provides a powerful mechanism for detecting compromised nodes and reasoning about the uncertainty in the network. It further can purge false data to accomplish robust aggregation in the presence of multiple compromised nodes

129 citations


Journal ArticleDOI
TL;DR: Simulations show that the proposed techniques for handling packet loss can effectively mitigate the effects of random transmission losses in a power-efficient way and study in-network aggregation's cost-efficiency using simple mathematical models.
Abstract: This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios where a large number of nodes produce data periodically. Such communication model is typical of monitoring applications, an important application domain sensor networks target. The main idea behind in-network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Through simulations, we evaluate the performance of different in-network aggregation algorithms, including our own cascading timers, in terms of the trade-offs between energy efficiency, data accuracy and freshness. Our results show that timing, that is, how long a node waits to receive data from its children (downstream nodes in respect to the information sink) before forwarding data onto the next hop (toward the sink) plays a crucial role in the performance of aggregation algorithms for applications that generate data periodically. By carefully selecting when to aggregate and forward data, cascading timers achieves considerable energy savings while maintaining data freshness and accuracy. We also study in-network aggregation's cost-efficiency using simple mathematical models. Since wireless sensor networks are prone to transmission errors and losses can have considerable impact when data aggregation is used, we also propose and evaluate a number of techniques for handling packet loss. Simulations show that, when used in conjunction with aggregation protocols, the proposed techniques can effectively mitigate the effects of random transmission losses in a power-efficient way.

115 citations


Proceedings ArticleDOI
23 Apr 2006
TL;DR: This work designs techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure, and proposes two corresponding mechanisms Data-Aware Anycast at the MAC layer and Randomized Waiting at the application layer.
Abstract: Data aggregation protocols can reduce the cost of communication, thereby extending the lifetime of sensor networks. Prior work on data aggregation protocols has 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.

96 citations


Proceedings ArticleDOI
11 Apr 2006
TL;DR: The proposed ant-aggregation algorithm, a population-based algorithm, provides natural and intrinsic way of exploration of search space in optimization settings in determining optimal data aggregation, and results shows improvement in energy efficiency depending on the number of source nodes in the sensor network.
Abstract: Data aggregation is an essential paradigm for energy efficient routing in energy constraint wireless sensor networks. The complexity of optimal data aggregation is NP-hard. Ant colony system, a population-based algorithm, provides natural and intrinsic way of exploration of search space in optimization settings in determining optimal data aggregation. The simulation results shows improvement in energy efficiency depending on the number of source nodes in the sensor network which is 45% energy efficiency using optimal aggregation compared to approximate aggregation schemes in moderate number of source whereas 20% energy efficiency in large number of source nodes. The proposed ant-aggregation algorithm is simulated in MATLAB.

80 citations


Journal ArticleDOI
TL;DR: The authors first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS) protocol, which generalize it to an aggregation hierarchy and extend EPAS to Hierarchical EPAS.
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, are often aggregated (or compressed). This compression is done by a subset of the sensors called "aggregators." Inasmuch as 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, the authors first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS) protocol. Then, they generalize it to an aggregation hierarchy and extend EPAS to Hierarchical EPAS. The optimal number of aggregators with generalized compression and power-consumption models was derived, and fully distributed algorithms for aggregator selection were presented. Simulation results show that the 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

79 citations


Proceedings ArticleDOI
30 Oct 2006
TL;DR: It is shown that even if a few compromised nodes contribute false sub-aggregate values, this results in large errors in the aggregate computed at the root of the hierarchy, which means that the approach is scalable and efficient.
Abstract: In a large sensor network, in-network data aggregation, i.e., combining partial results at intermediate nodes during message routing, significantly reduces the amount of communication and hence the energy consumed. Recently several researchers have proposed robust aggregation frameworks, which combine multi-path routing schemes with duplicate-insensitive algorithms, to accurately compute aggregates (e.g., Sum, Count, Average) in spite of message losses resulting from node and transmission failures. However, these aggregation frameworks have been designed without security in mind. Given the lack of hardware support for tamper-resistance and the unattended nature of sensor nodes, sensor networks are highly vulnerable to node compromises. We show that even if a few compromised nodes contribute false sub-aggregate values, this results in large errors in the aggregate computed at the root of the hierarchy. We present modifications to the aggregation algorithms that guard against such attacks, i.e., we present algorithms for resilient hierarchical data aggregation despite the presence of compromised nodes in the aggregation hierarchy. We evaluate the performance and costs of our approach via both analysis and simulation. Our results show that our approach is scalable and efficient.

69 citations


Journal ArticleDOI
TL;DR: A localized adaptive data collection algorithm performed at the source nodes is developed that balances the design tradeoffs of delay, measurement accuracy, and buffer overflow, for given QoS requirements.
Abstract: In this paper, an efficient quality of service (QoS)-constrained data aggregation and processing approach for distributed wireless sensor networks is investigated and analyzed. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion, based on the availability of local only information. Data aggregation is performed on the fly at intermediate sensor nodes, while at the same time the end-to-end latency constraints are satisfied. Furthermore, a localized adaptive data collection algorithm performed at the source nodes is developed that balances the design tradeoffs of delay, measurement accuracy, and buffer overflow, for given QoS requirements. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as energy efficiency, network lifetime, end-to-end latency, and data loss are also evaluated and discussed

48 citations


Proceedings ArticleDOI
10 May 2006
TL;DR: The simulation results show that the cluster protocol with the data aggregation scheme is effective in prolonging the network lifetime and supporting scalable data aggregation.
Abstract: Energy efficiency has been known as the most significant problem in all facets of the wireless sensor network operations. In this paper, we propose a meta-data-based data aggregation scheme in the clustering wireless sensor networks. For all sensor nodes in the sensing range of an event can detect the event and generate the sensing data, the cluster head will receive the sensing data packets about the same event, and there is likely to be some redundancy. In our scheme, only one of the sensor nodes within the sensing range of an event in a cluster is selected to transmit the sensing data to the cluster head via the meta data negotiation. We also evaluate this scheme with the LEACH protocol. The simulation results show that the cluster protocol with our data aggregation scheme is effective in prolonging the network lifetime and supporting scalable data aggregation.

Proceedings ArticleDOI
10 Apr 2006
TL;DR: A secure aggregation tree (SAT) is proposed to detect and prevent cheating in large-scale wireless sensor networks and does not require any cryptographic operations when all sensor nodes work honestly.
Abstract: In-network data aggregation in an essential operation to reduce energy consumption in large-scale wireless sensor networks With data aggregation, however, raw data items are invisible to the base station and thus the authenticity of the aggregated data is hard to guarantee A compromised sensor node may forge an aggregation value and mislead the base station into trusting a false reading Due to the stringent constraints of energy supply and computing capability on sensor nodes, it is challenging to detect a compromised sensor node and keep it from cheating This paper proposes a secure aggregation tree (SAT) to detect and prevent cheating Our method is essentially different from other existing solutions in that it does not require any cryptographic operations when all sensor nodes work honestly The detection of cheating is based on the topological constraints in the aggregation tree

Book
25 Sep 2006
TL;DR: The Theory of Information and Privacy, the Institutions of Credit Reporting, and Lessons for Credit Reporting Regulation have been studied in this article, with a focus on the role of credit reporting.
Abstract: Introduction.- Theory of Information and Privacy.- The Institutions of Credit Reporting.- Economic Effects of Credit Reporting.- Lessons for Credit Reporting Regulation.- Conclusions.

Proceedings ArticleDOI
13 Mar 2006
TL;DR: A new model of resilient data aggregation in sensor networks, where the aggregator analyzes the received sensor readings and tries to detect unexpected deviations before the aggregation function is called.
Abstract: In this paper, we propose a new model of resilient data aggregation in sensor networks, where the aggregator analyzes the received sensor readings and tries to detect unexpected deviations before the aggregation function is called. In this model, the adversary does not only want to cause maximal distortion in the output of the aggregation function, but it also wants to remain undetected. The advantage of this approach is that in order to remain undetected, the adversary cannot distort the output arbitrarily, but rather the distortion is usually upper bounded, even for aggregation functions that were considered to be insecure earlier (e.g., the average). We illustrate this through an example in this paper.

Proceedings ArticleDOI
25 Apr 2006
TL;DR: This paper describes the design and implementation of a physical tracking system, using an aggressive data aggregation architecture as one of building blocks, which can be generally applied to other sensor systems, where communication efficiency is a paramount concern and networking resources are limited.
Abstract: Since sensor nodes normally have limited resources in terms of energy, bandwidth and computation capability, efficiency is a key design goal in sensor network research. As one of techniques to achieve efficiency, data aggregation has been extensively investigated in recent literature. Previous research on data aggregation has demonstrated its effectiveness in reducing traffic, easing congestion and decreasing the energy consumption. However few are actually designed for a real-world application and implemented in a running system. This paper describes our design and implementation of a physical tracking system, using an aggressive data aggregation architecture as one of building blocks. This architecture can be generally applied to other sensor systems, where communication efficiency is a paramount concern and networking resources are limited.

Proceedings ArticleDOI
22 Mar 2006
TL;DR: It is demonstrated that a data aggregation rate of Theta(logn/n) is optimal and that this rate can be achieved in wireless sensor networks using a generalization of cooperative beamforming called cooperative time-reversal communication.
Abstract: The predominate traffic patterns in a wireless sensor network are many-to-one and one-to-many communication. Hence, the performance of wireless sensor networks is characterized by the rate at which data can be disseminated from or aggregated to a data sink. In this paper, we consider the data aggregation problem. We demonstrate that a data aggregation rate of Theta(logn/n) is optimal and that this rate can be achieved in wireless sensor networks using a generalization of cooperative beamforming called cooperative time-reversal communication.

01 Jan 2006
TL;DR: Wang et al. as discussed by the authors proposed a tree-based data collection scheme ( TBDCS), which combines the delivery of query messages with the selection of data aggregation points, using a flooding avoidance method.
Abstract: Combining the delivery of query messages with the selection of data aggregation points, a novel distributed data collection scheme for wireless sensor network - TBDCS (A Tree Based Data Collection Scheme) is proposed in this paper. Using a flooding avoidance method, it sets up a tree with minimum intermediate nodes, which are also data aggregators when sensor nodes send data back. Moreover, each intermediate node knows all its next-hop children in the tree, so it can choose a proper time locally to aggregate the received data. Theoretical analysis proves that TBDCS changes neither the network connectivity nor the shortest path’s length between the sink and sensor nodes. Simulations show TBDCS significantly reduces the traffic and achieves longer system lifetime.

Book ChapterDOI
15 May 2006
TL;DR: A context adaptive clustering mechanism is proposed, which tries to form clusters of sensors with similar output data within the bound of a given tolerance parameter, which can reduce energy consumption and prolong the sensor lifetime.
Abstract: Wireless sensor networks are characterized by the widely distributed sensor nodes which transmit sensed data to the base station cooperatively. However, due to the spatial correlation between sensor observations, it is not necessary for every node to transmit its data. There are already some papers on how to do clustering and data aggregation in-network, however, no one considers about the data distribution with respect to the environment. In this paper a context adaptive clustering mechanism is proposed, which tries to form clusters of sensors with similar output data within the bound of a given tolerance parameter. With similar data inside a cluster, it is possible for the cluster header to use a simple technique for data aggregation without introducing large errors, thus can reduce energy consumption and prolong the sensor lifetime. The algorithm proposed is very simple, transparent, localized and does not need any central authority to monitor or supervise it.

Patent
28 Jul 2006
TL;DR: In this paper, a system for portable data aggregation may include a communications module to receive an electronic form, an extractor to extract field data from the electronic form and an aggregator to associate the field data with an aggregate package.
Abstract: A system for portable data aggregation may include a communications module to receive an electronic form, an extractor to extract field data from the electronic form, an aggregator to associate the field data with an aggregate package. The system may further include a characterization module. The characterization module may be configured to preserve visual presentation of the electronic form. The field data may be presented in the aggregate package in an aggregated format.

Journal ArticleDOI
TL;DR: A novel mixed entropy data compression algorithm based on interval wavelet transforming is proposed for sensor network, according to the characteristics of data in sensor networks and the good performances of wavelets transforming in compression of the data stream.
Abstract: Considering the characteristics and location information of nodes in sensor networks, a modified directed transfer model of sensor networks and a new distributed data aggregation model based on "area" are proposed. On the basis of these new models, a novel mixed entropy data compression algorithm based on interval wavelet transforming is proposed for sensor network, according to the characteristics of data in sensor networks and the good performances of wavelet transforming in compression of the data stream. Theoretical analyses and simulation results show that, the above new methods can compress the data stream and reduce the energy costs of nodes in data transferring efficiently for sensor networks. So, it can prolong the lifetime of the whole networks to a greater degree when the above new methods are deployed with those traditional DC (data centric) routing

Proceedings ArticleDOI
01 Nov 2006
TL;DR: A new data aggregation technology is presented, conjiguring carefilly the aggregator node's timeouts through making use of a new aggregation timing control scheme, to achieve a good trade-off between energy efficiency and data accuracy.
Abstract: In wireless sensor network, in-network data aggregation can eliminate redundancy, improve data accuracy and thus prolong the lifetime of network. However, most of data aggregation technologies assumed that the aggregator node has been receiving all the data when it performers the aggregation operation, these methods give larger latency to the transmission of network's data and affect the accuracy of network. Therefore, this paper presents a new data aggregation technology, conjiguring carefilly the aggregator node's timeouts through making use of a new aggregation timing control scheme, which is to achieve a good trade-off between energy efficiency and data accuracy.

Proceedings ArticleDOI
10 Apr 2006
TL;DR: The feasibility to use a low-cost timing control scheme to adaptively adjust the waiting period in each DAT level so that the end-to-end query delay could be optimized, i.e. without unnecessary pause in an aggregation node.
Abstract: Data aggregation in Wireless Sensor Networks (WSNs) has attracted wide attention since it could reduce the amount of wireless data communication and thus save power consumption, which is one of the top concerns in low-power WSN systems. Given a Data Aggregation Tree (DAT) for data query purpose in a WSN, and the expected query accuracy, we show the feasibility to use a low-cost timing control scheme to adaptively adjust the waiting period in each DAT level so that the end-to-end query delay could be optimized, i.e. without unnecessary pause in an aggregation node. For such an adaptive timing control scheme, we propose to use a Finite State Machine (FSM)- based auto-feedback control algorithm to adjust the waiting period of each DAT node to "timely" respond to the queries. Our sensor network simulation results and hardware experiments verify the validity of our proposed timing control scheme.

Proceedings ArticleDOI
10 Apr 2006
TL;DR: This work considers the problem of extending the lifetime of wireless sensor networks (WSNs) where sensors report their data to a base station via multi-hop transmission and introduces the concept of flow loss multiplier to express the impact of data aggregation over correlated data.
Abstract: We consider the problem of extending the lifetime of wireless sensor networks (WSNs) where sensors report their data to a base station via multi-hop transmission. In-network data aggregation is introduced to reduce the traffic. The lifetime is optimized using a LP (linear programming) framework built on a multicommodity network flow model to which we introduce the concept of flow loss multiplier to express the impact of data aggregation over correlated data. To balance energy consumption across the network, data aggregation is performed only at the first hop (FH) transmission. Heuristics are proposed to obtain significant FH aggregation, accordingly prolonging system lifetime. Simulation results show that FH aggregation possesses characteristics that make it suitable under certain data collection applications/scenarios.

Proceedings ArticleDOI
26 Jun 2006
TL;DR: An efficient Quality of Service- constrained data aggregation and processing approach for distributed wireless sensor networks is introduced and evaluated to improve the operational efficiency and effectiveness of the sensor networks, while at the same time still satisfying the latency and measurement quality constraints.
Abstract: In this paper an efficient Quality of Service (QoS)- constrained data aggregation and processing approach for distributed wireless sensor networks is introduced and evaluated. The objective of the proposed approach is to aggregate data on the fly at intermediate sensor nodes in order to improve the operational efficiency and effectiveness of the sensor networks, while at the same time still satisfying the latency and measurement quality constraints. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as the energy efficiency and end-to-end latency, are also evaluated and discussed.

Proceedings ArticleDOI
01 Sep 2006
TL;DR: A protocol called RDA is proposed which associates packet's reliability in data transmission with the amount of information it contains and gives higher reliability to the packet which has more information by adjusting the degree of redundancy.
Abstract: Data aggregation in wireless sensor networks is widely accepted as an essential paradigm for energy efficient routing but with low reliability under node and link failures. In this paper, we propose a protocol called RDA which associates packet's reliability in data transmission with the amount of information it contains and gives higher reliability to the packet which has more information by adjusting the degree of redundancy. Therefore, RDA can jointly optimize both information reliability and energy efficiency in sensor networks with data aggregation. Especially, RDA is not banded with special routing schemes; hence it can be used with any kinds of routing schemes supporting data aggregation in wireless sensor networks

Journal Article
TL;DR: Experimental results show that the proposed energy-efficient data gathering mechanism outperforms the direct scheme protocol and the LEACH protocol on the point of view of the network lifetime.
Abstract: Sensor networks require energy-aware, efficient datacollecting methods to extend their network lifetime. In this paper, we propose an energy-efficient data gathering mechanism which clusters sensor nodes and forms a distributed data-routing tree based on in-network data fusion. In our mechanism, the cluster formation and the data-routing tree construction are simultaneously carried out so that they reduce their energy required to organize a multi-hop routing tree of sensed data. The mechanism also performs data aggregation at each member node to reduce the amount of transmission data. Moreover our work distributes energy load to each node to avoid the intensive energy consumption of a cluster-head. Experimental results show that our data gathering mechanism outperforms the direct scheme protocol and the LEACH protocol on the point of view of the network lifetime.

Journal Article
TL;DR: An overview of the intelligent information processing approaches of WSN, which includes in-network aggregation, data compression, distributed database storage and query and so on, is given.
Abstract: The Wiressless Sensor Network (WSN), which consists of a large number of micro sensor nodes, is mainly used to gather data information from the monitored environments. However, the strict constraints on individual sensor node's resource bring great challenges to the information processing of WSN. Consequently, simple and efficient algorithms must be taken. This paper gives an overview of the intelligent information processing approaches of WSN, which includes in-network aggregation, data compression, distributed database storage and query and so on. The principles and advantages/disadvantages of each approach are pointed out,and the key problems to be studied are pointed out here too.

Proceedings ArticleDOI
30 Aug 2006
TL;DR: It is shown that by recording the enough intermediate reading information, the proposed E-TiNA algorithm can provide better quality of data at all kinds of cases.
Abstract: As a good approach to decrease the data traffic and energy consumption in wireless sensor networks, in-network data aggregation has been concerned widely in recent years. Mohamed (A. S. Mohamed et al., 2003) presented the TiNA scheme, namely temporal coherency in-network aggregation, which utilizes the end-user tolerance to temporal coherency to minimize energy consumption of wireless sensor networks and provides high quality of data at the same time. In this paper, we modify the original TiNA algorithm by adding an external data item used to keep the last sampled data reading. It is shown that by recording the enough intermediate reading information, the proposed E-TiNA algorithm can provide better quality of data at all kinds of cases

Proceedings ArticleDOI
01 Jan 2006
TL;DR: Based on the correlation and frequency spectrum analysis results of some types of slowly varying sensor data, two data approximation methods to reduce data transmission while make queries easy to answer are presented.
Abstract: One way of conserving the scarce resources in a sensor network is to minimize the amount of data transmitted. This can be accomplished by data compression, aggregation or approximation. The current researches on sensor data compression mainly focus on lossless compression methods, they cannot achieve higher compression ratio than lossy data compression. In-network data aggregation and data approximation can be regarded as lossy data reduction methods. However, in-network data aggregation methods cannot record all the features of sensor data, thus queries referring to the historical data might not be answered. Moreover, the data cached in sensor networks should be used easily for answering queries. Based on the correlation and frequency spectrum analysis results of some types of slowly varying sensor data, we have presented two data approximation methods to reduce data transmission while make queries easy to answer. We have implemented these methods, tested on some real life data sets and compared with related methods. The results indicate that the algorithms are simple and deliver high data reduction ratios, while meeting the user's tolerance of errors.

Proceedings ArticleDOI
10 Apr 2006
TL;DR: A scheme is proposed for secure data aggregation in wireless sensor networks where commitment schemes and a class of functions called quasi commutative functions are used to achieve provablySecure data aggregation.
Abstract: A scheme is proposed for secure data aggregation in wireless sensor networks. Commitment schemes and a class of functions called quasi commutative functions are used to achieve provably secure data aggregation. Efficient schemes to verify the data aggregation are provided.