Topic
Data aggregator
About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.
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TL;DR: Wang et al. as mentioned in this paper proposed a Divide-and-Conquer privacy-preserving data aggregation scheme in fog computing to improve data privacy, data processing, and storage capabilities, which not only effectively divides data according to the Level of Privacy (LoP) defined by data owners, but also reduces computational and memory overhead in the processing simultaneously.
11 citations
TL;DR: An efficient and privacy-friendly hop-by-hop data aggregation scheme and a billing solution for smart grid systems and from the outcome of the security and performance analyses it can argue that the proposed scheme is secure and computationally more efficient, as compared to the other solutions.
Abstract: The fine-grained and large number of measurements collected by smart meters can be used to reconstruct consumer behavior, and thus their widespread deployment for the modernization of electricity distribution networks has been associated with privacy concerns. This paper proposes an efficient and privacy-friendly hop-by-hop data aggregation scheme and a billing solution for smart grid systems. In our approach, hop-by-hop communication is utilized for transmitting usage reports of the smart meters. From the outcome of the security and performance analyses we can argue that our proposed scheme is secure and computationally more efficient, as compared to the other solutions.
11 citations
24 Jun 2019
TL;DR: This work takes advantage of the opportunities offered by ECEL-Gamal and its homomorphic property as well as the ECDSA and ECDHE algorithms to use them in order to aggregate privacy-preserving data and shows that the proposed scheme can meet various security requirements and preserves user privacy.
Abstract: Over the last few years, the rapid development of smart grids and the deployment of smart metering infrastructures on a much larger scale have raised a crucial privacy concern related to the collection of real-time energy consumption data. For these purpose, data aggregation is a widely used technique for privacy preservation. Securing the data aggregation process is a challenge since smart grids are susceptible to many cyber security threats and attacks. Therefore, several approaches have been proposed to address this issue. However, many of them are vulnerable to various types of attacks and suffer from high communication and computation costs in addition to the use of complex key management schemes. To overcome this problem, we propose a secure and privacy-preserving data aggregation scheme based on homomorphic encryption. In this solution, we take advantage of the opportunities offered by ECEL-Gamal and its homomorphic property as well as the ECDSA and ECDHE algorithms to use them in order to aggregate privacy-preserving data. Through detailed analyses, we show that our proposed scheme can meet various security requirements and preserves user privacy. In addition, experiment results demonstrate that our scheme is more efficient than existing competing approaches in terms of communication and computation overhead.
11 citations
01 Dec 2007
TL;DR: In this article, the authors investigated the effect of data aggregation interval on the final outcome of a power quality survey on the magnitude of supply voltage monitoring when data is captured at different data aggregation intervals.
Abstract: For various technical and operational reasons, many power quality surveys are carried out using non-standard data aggregation intervals. The data aggregation interval is the time interval that rapidly sampled data is reduced to by the monitoring instrument for subsequent analysis and reporting. Some of the rationales for using non-standard data aggregation intervals include instrumentation limitations, memory restrictions, a belief that more insights may be obtained from data captured at faster aggregation intervals and dual use of instrumentation (such is the case for many smart revenue meters). There is much conjecture over the effect which the data aggregation interval will have on the final outcomes of a power quality survey. IEC61000-4-30 which is the international standard describing power quality monitoring methodology suggests 10 minute data aggregation intervals are appropriate for routine power quality monitoring of most power quality disturbances including magnitude of supply voltage. This paper investigates the variation observed for magnitude of supply voltage monitoring when data is captured at a range of data aggregation intervals.
11 citations
TL;DR: A Delay-Constrained Optimal Data Aggregation (DeCODA) framework is presented that considers the unique feature of traffic patterns and information processing at ANs for energy saving and Mathematical models and analytical results are provided, and simulation studies are performed to verify the effectiveness of the DeC ODA framework.
Abstract: A lot of realistic applications with wireless sensor networks adopt hierarchical architecture in which sensor nodes are grouped into clusters, with each cluster relying on a gateway node for local data aggregation and long-distance radio transmission. Compared to normal sensor nodes, the gateway nodes, also called application nodes (ANs), are equipped with relatively powerful transceivers and have more energy. Nevertheless, since an AN is the main gateway for sensor nodes within its clusters, its energy may be depleted more quickly than normal sensor nodes. As such, it is important to find methods to save energy for ANs. This paper presents a Delay-Constrained Optimal Data Aggregation (DeCODA) framework that considers the unique feature of traffic patterns and information processing at ANs for energy saving. Mathematical models and analytical results are provided, and simulation studies are performed to verify the effectiveness of the DeCODA framework.
11 citations