Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Confidentiality and Integrity for Data Aggregation in WSN Using Homomorphic Encryption

Abstract: Data aggregation is an important method to reduce the energy consumption in wireless sensor networks (WSNs), however, performing data aggregation while preserving data confidentiality and integrity is mounting a challenge. The existing solutions either have large communication and computation overheads or produce inaccurate results. This paper proposes a novel secure data aggregation scheme based on homomorphic encryption in WSNs. The scheme adopts a symmetric-key homomorphic encryption to protect data privacy and combines it with homomorphic signature to check the aggregation data integrity. In addition, during the decryption of aggregated data, the base station is able to classify the encrypted and aggregated data based on the encryption keys. Simulation results and performance analysis show that our mechanism requires less communication and computation overheads than previously known methods. It can effectively preserve data privacy, check data integrity, and achieve high data transmission efficiency. Also, it performs accurate data aggregation rate while consuming less energy to prolong network lifetime.

Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems

Abstract: With the popularity of mobile wireless devices equipped with various kinds of sensing abilities, a new service paradigm named participatory sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy and multimedia data quality preservations are two critical problems. Unfortunately, none of the existing work has fully solved the problem of privacy and quality preserving participatory sensing with multimedia data. In this paper, we propose SLICER , which is the first $k$ -anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message transfer strategies, to achieve strong protection of participants’ privacy, while maintaining high data quality. Specifically, we study two kinds of data transfer strategies, namely transfer on meet up (TMU) and minimal cost transfer (MCT). For MCT, we propose two different but complimentary algorithms, including an approximation algorithm and a heuristic algorithm, subject to different strengths of the requirement. Furthermore, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data quality, with low computation and communication overhead.

Confidentiality Protection for Distributed Sensor Data Aggregation

Abstract: Efficiency and security are two basic requirements for sensor network design. However, these requirements could be sharply contrary to each other in some scenarios. For example, in- network data aggregation can significantly reduce communication overhead and thus has been adopted widely as a means to improve network efficiency; however, the adoption of in-network data aggregation may prevent data from being encrypted since it is a prerequisite for aggregation that data be accessible during forwarding. In this paper, we address this dilemma by proposing a family of secret perturbation-based schemes that can protect sensor data confidentiality without disrupting additive data aggregation. Extensive simulations are also conducted to evaluate the proposed schemes. The results show that our schemes provide confidentiality protection for both raw and aggregated data items with an overhead lower than that of existing related schemes.

Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

Abstract: Data aggregation is a very important method to conserve energy by eliminating the inherent redundancy of raw data in wireless sensor networks (WSNs). In this article, we developed an automatic auto regressive-integrated moving averagemodeling-based data aggregation scheme in WSNs. The main idea behind this scheme is to decrease the number of transmitted data values between sensor nodes and aggregators by utilizing time series prediction model. The proposed scheme can effectively save the precious battery energy of wireless sensor nodes while keeping the predicted data values of aggregators within application-defined error threshold. We show through experiments with real data that the predicted data values of our proposed scheme fit the real sensed data values very well and fewer messages are transmitted between sensor nodes and aggregators than the native data aggregation scheme. Furthermore, the characteristics of the proposed data aggregation scheme are also discussed in this article.

Display of visual data utilizing data aggregation

Abstract: A method for visualizing time-varying data from one or more data streams at a different interval than the interval between acquisition of the individual data items in the data stream. Data received is combined, or aggregated, between updates of a display to retain some information from each element. The aggregated data is then displayed at the next update of the display in a number of display elements. The characteristics of the display elements, and the organization of the elements represent changes in the data streams.