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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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Proceedings ArticleDOI
18 Apr 2016
TL;DR: A pre-filtration method in every sensor node using both relative variation (RV) and a data aggregative window function (DAWF) which can exploit both spatial and temporal redundancies in WSNs is presented.
Abstract: The energy-efficient data aggregation in Wireless Sensor Networks (WSNs) has grown as one of the promising area in many applications. Prior research works have suggested several spatio-temporal models for effectively reducing the data collection costs, but the models are limited to their specificity. This paper presents a pre-filtration method in every sensor node. While using pre-filtration techniques on node's data pre-processing can suppress a huge amount of redundant or correlated data transmissions, thus maximizes the nodes battery lifetime. This paper proposes a framework using both relative variation (RV) and a data aggregative window function (DAWF), which can exploit both spatial and temporal redundancies in WSNs. However, both compressive and prediction-based approaches can find temporal data redundancies or correlations in sensor nodes as well as spatio-temporal correlations in Cluster-Head (CH) nodes. In this regard, the experimental study shows that the proposed mechanism among the considered network nodes can suppress a huge amount of required data transmissions, while providing reliable data towards the base station (BS).

15 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an efficient and secure data transmission and aggregation (ESDTA) scheme to enhance aggregation efficiency and data security in the Internet of Medical Things (IoMT) by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node (FN).
Abstract: Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node (FN), respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the FN minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.

15 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA, which provides strong support for achieving efficient and secure data collection in smart grids.

15 citations


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