Secure and efficient multifunctional data aggregation without trusted authority in edge-enhanced IoT

Wu, Qiyu; Zhou, Fen; Xu, Jian; Wang, Qiang; Feng, Da

doi:10.1016/j.jisa.2022.103270

Cited by 3 publications

References 32 publications

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Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

Abbas,

Merad‐Boudia,

Senouci

et al. 2024

Concurrency and Computation

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SummaryData aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

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Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

Abbas,

Merad‐Boudia,

Senouci

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

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Data Aggregation in Internet of Things (DAIT) with Edge Computing

Yaragal,

Bendigeri

2024

2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)

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RETRACTED: Efficient data management in Internet of Things: A survey of data aggregation techniques

Kang

2024

IFS

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The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed.

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Secure and efficient multifunctional data aggregation without trusted authority in edge-enhanced IoT

Cited by 3 publications

References 32 publications

Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

Data Aggregation in Internet of Things (DAIT) with Edge Computing

RETRACTED: Efficient data management in Internet of Things: A survey of data aggregation techniques

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