Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data

Chatamoni, Anil; Bhukya, Rajendra Naik

doi:10.46604/ijeti.2022.8599

Cited by 2 publications

(2 citation statements)

References 26 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sensor nodes range from straightforward devices such as temperature monitors to complex systems like surveillance cameras, and they determine the nature and format of the data collected. These nodes are increasingly being equipped with edge computing capabilities, which allow for a degree of local data preprocessing [14]. Even less resource-intensive sensors can engage in basic edge computing tasks by employing streamlined algorithms and optimized firmware tailored to their processing abilities.…”

Section: Sensor Nodesmentioning

confidence: 99%

Enhancing Industrial IoT Network Security through Blockchain Integration

Bobde,

Narayanan,

Jati

et al. 2024

Electronics

View full text Add to dashboard Cite

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

show abstract

Section: Sensor Nodesmentioning

confidence: 99%

Enhancing Industrial IoT Network Security through Blockchain Integration

Bobde,

Narayanan,

Jati

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Sudarsono et al [20] suggested a method that uses CP-ABE and Hashed MAC to securely transfer environmental data from sensor nodes to a gateway while maintaining integrity. To safely encrypt the sensor data, Chatamoni et al [21] adopted a portable compressive sensing technology that uses structurally random matrices and blocks compressive sensing.…”

Section: Related Workmentioning

confidence: 99%

Partitioning-Based Data Sharing Approach for Data Integrity Verification in Distributed Fog Computing

Kaliyaperumal

Somasundaram

Nickolas

2023

Int. j. eng. technol. innov.

View full text Add to dashboard Cite

With the increasing popularity of the internet of things (IoT), fog computing has emerged as a unique cutting-edge approach along with cloud computing. This study proposes an approach for data integrity verification in fog computing that does not require metadata stored on the user side and can handle big data efficiently. In the proposed work, fuzzy clustering is used to cluster IoT data; dynamic keys are used to encrypt the clusters; and dynamic permutation is used to distribute encrypted clusters among fog nodes. During the process of data retrieval, fuzzy clustering and message authentication code (MAC) are used to verify the data integrity. Fuzzy clustering and dynamic primitives make the proposed approach more secure. The security analysis indicates that the proposed approach is resilient to various security attacks. Moreover, the performance analysis shows that the computation time of the proposed work is 50 times better than the existing tag regeneration scheme.

show abstract

Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data

Cited by 2 publications

References 26 publications

Enhancing Industrial IoT Network Security through Blockchain Integration

Enhancing Industrial IoT Network Security through Blockchain Integration

Partitioning-Based Data Sharing Approach for Data Integrity Verification in Distributed Fog Computing

Contact Info

Product

Resources

About