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Advanced Security and Privacy Focused Blockchain-Based Sensor Networks, Architectures and Next Generation Communications

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Special Issue Editors

Prof. Dr. Aniruddha Bhattacharjya

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Guest Editor

1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2. Founder Chairman and Executive Director, BCBRBAB Intercontinental Trading Solutions Private Limited, Kolkata 700084, India
Interests: applied cryptography and cryptanalysis (RSA and AES and related ciphers); end-to-end (E2E) secure communication, peer to peer (P2P) communication and security aspects; information systems efficiency; lightweight and security aspects; blockchain applications and security aspects and software testing
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Prof. Dr. Shaohua Wan

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Guest Editor

Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
Interests: deep learning; internet of things; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will introduce the present state of the art and use cases of Blockchain-based sensor networks, systems, architectures and next-generation communications considering security and privacy aspects. Very specific use cases and practical implementations will form a special section. The security, efficiency and scalability of these architectures will be discussed. Articles regarding all types of emerging Blockchain-based architectures, sensor networks and next-generation communication systems will be included in this Special Issue. Topics include, but are not limited to:

Blockchain-based wireless sensor networks (WSNs) (BWSN);
Blockchain-based sensor data communication;
Blockchain-enabled IoT (BIoT) with security and privacy aspects;
Sensor tracking and traceability using blockchain-based supply chain management (SCM);
Blockchain-enabled sensor technology in wireless networks;
Blockchain and mobile wireless sensor networks;
Blockchain and distributed ledger solutions for data veracity and privacy in WSNs;
Improvement of security, reliability and trust in WSN through the use of Blockchain;
Consensus mechanism for wireless sensor networks (WSNs);
Blockchain, IoT, and WSNs;
Industrial Internet of Things (IIoT), Blockchain and smart contracts;
Blockchain for 6G-enabled network-based applications with a focus on security, privacy and efficiency;
Fog/Edge-integrated architecture in WSNs with blockchain with a focus on security, privacy and efficiency;
Blockchain in connected and autonomous vehicles;
Blockchain in edge and cloud computing;
Blockchain in next-generation communications and sensor networks;
Blockchain in crowdsourcing and crowdsensing;
Software-defined networking (SDN) for Blockchain;
Blockchain and data analytics;
AI-based edge computing over Blockchain;
Blockchain and beyond wireless technologies;
Secure storage and access in wireless sensor networks (WSNs) using Blockchain

Prof. Dr. Aniruddha Bhattacharjya
Prof. Dr. Shaohua Wan
Guest Editors

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Keywords

blockchain-based wireless sensor networks (BWSNs)
blockchain-enabled IoT (BIoT)
industrial Internet of Things (IIoT)
data veracity and privacy in wireless sensor networks (WSNs)
blockchain-based sensor data communication (BSDC)

Published Papers (1 paper)

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Research

20 pages, 1964 KiB

Open AccessArticle

A Deep Learning-Based Method for Preventing Data Leakage in Electric Power Industrial Internet of Things Business Data Interactions

by Weiwei Miao, Xinjian Zhao, Yinzhao Zhang, Shi Chen, Xiaochao Li and Qianmu Li

Sensors 2024, 24(13), 4069; https://doi.org/10.3390/s24134069 - 22 Jun 2024

Viewed by 386

Abstract

In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. Full article

(This article belongs to the Special Issue Advanced Security and Privacy Focused Blockchain-Based Sensor Networks, Architectures and Next Generation Communications)

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Advanced Security and Privacy Focused Blockchain-Based Sensor Networks, Architectures and Next Generation Communications

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