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Data aggregator

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


Papers
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Journal ArticleDOI
TL;DR: Simulation results show that the performance of data aggregation is improved and also energy consumption of the network is reduced.
Abstract: Energy efficiency is very important issue in wireless sensor networks (WSNs). In WSN, sensors are distributed in different places, where they can be exposed to contact with the environment. Data aggregation, eliminating of data redundancy and improve the accuracy of the collected data are essential points for these networks. This research has been suggested some algorithms such as MEDA, LMTBPN, RBDA and RGDA. The first algorithm is based on the moment estimation method and the other three algorithms aggregate the data based on backward propagation, radial basis and general regression. These algorithms use a three-layer neural network. Input layer neurons are located in members of each cluster while the hidden layer neurons are located in cluster heads and output layer neurons are located in base station. Simulation results show that the performance of data aggregation is improved and also energy consumption of the network is reduced.

17 citations

Journal ArticleDOI
TL;DR: An extensive survey of various data aggregation techniques in WSN is performed by categorising the techniques as structured, structure-free, flat and hierarchical in terms of energy conservation, network lifetime, packet delivery ratio, latency and various other parameters.
Abstract: Wireless sensor networks WSNs are self-organised, low cost and low power utilising network, which senses, calculates and communicates the data from the source to sink. The data collection at sensor nodes consumes more energy, but the sensor nodes have only limited energy. Hence, most of the data-aggregation techniques aim to prolong the lifetime of the network by minimising power consumption and optimised data transmission. In this paper, an extensive survey of various data aggregation techniques in WSN is performed by categorising the techniques as structured, structure-free, flat and hierarchical. These techniques are analysed in terms of energy conservation, network lifetime, packet delivery ratio, latency and various other parameters. A comparison of these data aggregation techniques is also presented along with their advantages and issues.

17 citations

Book ChapterDOI
01 Jan 2019
TL;DR: The proposed algorithm aims to data extraction, aggregation, and classification based on novel approach as “DataSpeak” based on k-Nearest Neighbors with Spark as reference and produced a novel approach with modified algorithm.
Abstract: A huge amount of data is coming due to large set of computing devices As a birth of the variety of data, data processing and analysis is a big issue in big data analytics On other hand, data consistency and scalability is also a major problem in the large set of data Our research and proposed algorithm aims to data extraction, aggregation, and classification based on novel approach as “DataSpeak” We have used k-Nearest Neighbors with Spark as reference and produced a novel approach with modified algorithm We have analyzed our approach on the large dataset from travel and tourism, placement papers, movies and historical, smartphone, etc, domains As for ability and accuracy of our algorithm, we have used cross validation, precision, recall, and comparative statistical analysis with the existing algorithm Our approach returns with the fast accessing of data with efficient data extraction in a minimal time when compared to the existing algorithm in same domain As concerned with the data aggregation and classification, our approach returns 98% of data aggregation and classification based on the data structure

17 citations

Proceedings ArticleDOI
05 Oct 2020
TL;DR: V-PATD is proposed, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems and a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server.
Abstract: Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy" cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads.

17 citations

Journal ArticleDOI
TL;DR: This paper proposes a malleability resilient concealed data aggregation protocol for protecting the network against active and passive adversaries, and simultaneously realizes the conflicting objectives like privacy at intermediate nodes, end-to-end integrity, replay protection, and en route aggregation.
Abstract: The objective of concealed data aggregation is to achieve the privacy preservation at intermediate nodes while supporting in-network data aggregation. The need for privacy preservation at intermediate nodes and the need for data aggregation at intermediate nodes can be simultaneously realized using privacy homomorphism. Privacy homomorphism processes the encrypted data without decrypting them at intermediate nodes. However, privacy homomorphism is inherently malleable. Although malicious adversaries cannot view transmitted sensor readings, they can manipulate them. Hence, it is a formidable challenge to realize conflicting requirements, such as end-to-end privacy and end-to-end integrity, while performing en route aggregation. In this paper, we propose a malleability resilient concealed data aggregation protocol for protecting the network against active and passive adversaries. In addition, the proposed protocol protects the network against insider and outsider adversaries. The proposed protocol simultaneously realizes the conflicting objectives like privacy at intermediate nodes, end-to-end integrity, replay protection, and en route aggregation. As per our knowledge, the proposed solution is the first that achieves end-to-end security and en route aggregation of reverse multicast traffic in the presence of insider, as well as outsider adversaries.

17 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023104
2022277
2021189
2020207
2019179
2018188