A novel trust-based secure data aggregation for Internet of Things

Liu, Yanbing; Gong, Xuehong; Xing, Cong-Cong

doi:10.1109/iccse.2014.6926499

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…(a) Tree-based approach [37][38][39][40][41][42][43]-This approach deploys the nodes in the network in the form of a tree. Here, the hierarchical and intermediate nodes perform the aggregation.…”

Section: Data Aggregationmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

224

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Section: Data Aggregationmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

224

View full text Add to dashboard Cite

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Efficient Channel Access Scheme for IoT

Singh

Kaur

2021

2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)

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Secure data aggregation and intrusion detection in wireless sensor networks

Vamsi

Kant

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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Data Aggregation (DA) is a technique of data gathering in Wireless Sensor Networks (WSNs). It provide advantages such as reporting consolidated data, reducing data redundancy, improving network lifetime etc. However, deploying WSNs in hostile and remote environments presents security vulnerabilities that can lead to various security attacks such as energy based attacks, attacks on data aggregation etc. Numerous secure DA techniques have been proposed in the literature. However, lightweight models using Trust Monitoring System (TMS) and Intrusion Detection Systems (IDS) are limited. This paper presents a secure data aggregation framework for Wireless Sensor Networks (WSNs) using TMS at node level and IDS at Base Station (BS) side. Each node in the network assesses the behavior of its neighbors using trust ratings and performs the network activities such as cluster head selection, data aggregation, and reporting to the BS. Then, BS analyzes the received information using IDS and reports the information about the malicious activities back to nodes in the network. In this way, the proposed model identifies and isolates the malicious nodes from the data aggregation process. Simulation results show the effectiveness of this model.

show abstract

A novel trust-based secure data aggregation for Internet of Things

Cited by 8 publications

References 15 publications

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Efficient Channel Access Scheme for IoT

Secure data aggregation and intrusion detection in wireless sensor networks

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