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Blockchain of Things: Benefits, Challenges and Future Directions

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 59846

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Special Issue Editors

Dr. Kamanashis Biswas

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Guest Editor

Faculty of Law and Business, Australian Catholic University, 8-20 Napier St, North Sydney, NSW 2060, Australia
Interests: WSN; WBAN; security; privacy; routing; blockchain; e-healthcare; smart health
Special Issues, Collections and Topics in MDPI journals

Dr. Mohammad Jabed Morshed Chowdhury

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Guest Editor

Department of Computer Science and Technology, La Trobe University, Melbourne, VIC 3086, Australia
Interests: blockchain; cyber security; privacy; WSN; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Usman

E-Mail Website
Guest Editor

University of South Wales, Pontypridd, CF37 1DL, UK
Interests: Security, Privacy, Trust, Blockchain, Internet of Things, Computational Intelligence

Special Issue Information

Dear Colleagues,

Blockchain is a pivotal element of the security arsenal of modern networking technologies in general, and Internet of Things (IoT) technologies in practice. It is considered as a missing link that would enable IoT devices to leverage their full benefits. This Special Issue would make significant contributions to both the theory and practice of IoT devices and technologies.

Internet of Things (IoTs) technologies are becoming an integral part of our daily life due to the proliferation of different types of Internet-enabled devices. Managing the massive amount of data generated by these devices in an efficient, secure, and economical way is a challenging task. Recently, blockchain has been perceived as a promising technique that can be used to build trust without the need of a trusted third party. The convergence of blockchain and IoT can provide a secure and robust mechanism of managing data generated by IoT devices. This Special Issue aims to attract contributions from a broader domain of IoT-enabled blockchain-driven solutions. Potential topics include but are not limited to the following:

Blockchain of Things (BCoT) and smart cities;
BCoT in smart industry, smart vehicles and smart homes;
BCoT and smart contracts;
Distributed consensus for BCoT;
Performance evaluation of BCoT;
BCoT applications;
Security and privacy mechanisms for BCoT;
Role of BCoT in financial services.

Dr. Kamanashis Biswas
Dr. Mohammad Jabed Morshed Chowdhury
Dr. Muhammad Usman
Guest Editors

Manuscript Submission Information

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Keywords

Blockchain of Things (BCoT)
consensus
security
privacy
application

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Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 146 KiB

Open AccessEditorial

Blockchain of Things: Benefits, Challenges and Future Directions

by Kamanashis Biswas, Mohammad Jabed Morshed Chowdhury and Muhammad Usman

Sensors 2024, 24(3), 934; https://doi.org/10.3390/s24030934 - 31 Jan 2024

Cited by 2 | Viewed by 1495

Abstract

As Internet of Things (IoT) technologies become increasingly integrated into our daily lives through a multitude of Internet-enabled devices, the efficient, secure, and cost-effective management of the vast amount of data generated by these devices poses a significant challenge [...] Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

Research

Jump to: Editorial, Review

15 pages, 1555 KiB

Open AccessArticle

HeriLedger—A New Generation of Blockchains for Cultural Heritage Preservation

by Denis Trček

Sensors 2022, 22(22), 8913; https://doi.org/10.3390/s22228913 - 18 Nov 2022

Cited by 3 | Viewed by 2143

Abstract

The immense success of Bitcoin has also highlighted the hidden potential of blockchains and distributed ledgers in general. However, most blockchains are based on the so-called Proof-of-Work principle, which requires significant resources, making them unsuitable for the growing number of Internet of Things devices, not to mention other problems such as ensuring privacy and resistance to quantum computing. This paper, therefore, analyses current approaches in the field of ledgers with applications for the Internet of Things. Based on this, it presents a new ledger architecture for cultural heritage preservation that is energy sustainable and tailored to smartphones while pushing the deployment boundaries closer to the rest of the Internet of Things world. It is also resistant to quantum computing and provides privacy with accountability. Moreover, the developed solution considers not only the core technological structure but also its broader social (business) context, i.e., links to the tourism sector. This is performed by incorporating the relevant business model, as business models are important for the successful adoption of new technologies. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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25 pages, 4017 KiB

Open AccessArticle

IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security

by Dongjun Na and Sejin Park

Sensors 2022, 22(21), 8271; https://doi.org/10.3390/s22218271 - 28 Oct 2022

Cited by 11 | Viewed by 3161

Abstract

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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32 pages, 5649 KiB

Open AccessArticle

SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

by Lejun Zhang, Yuan Li, Tianxing Jin, Weizheng Wang, Zilong Jin, Chunhui Zhao, Zhennao Cai and Huiling Chen

Sensors 2022, 22(12), 4621; https://doi.org/10.3390/s22124621 - 19 Jun 2022

Cited by 25 | Viewed by 3792

Abstract

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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25 pages, 4844 KiB

Open AccessArticle

A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning

by Lejun Zhang, Jinlong Wang, Weizheng Wang, Zilong Jin, Chunhui Zhao, Zhennao Cai and Huiling Chen

Sensors 2022, 22(9), 3581; https://doi.org/10.3390/s22093581 - 8 May 2022

Cited by 42 | Viewed by 6234

Abstract

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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24 pages, 3827 KiB

Open AccessArticle

CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model

by Lejun Zhang, Weijie Chen, Weizheng Wang, Zilong Jin, Chunhui Zhao, Zhennao Cai and Huiling Chen

Sensors 2022, 22(9), 3577; https://doi.org/10.3390/s22093577 - 7 May 2022

Cited by 49 | Viewed by 5928

Abstract

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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24 pages, 4048 KiB

Open AccessArticle

Smart Contract Vulnerability Detection Model Based on Multi-Task Learning

by Jing Huang, Kuo Zhou, Ao Xiong and Dongmeng Li

Sensors 2022, 22(5), 1829; https://doi.org/10.3390/s22051829 - 25 Feb 2022

Cited by 46 | Viewed by 7978

Abstract

The key issue in the field of smart contract security is efficient and rapid vulnerability detection in smart contracts. Most of the existing detection methods can only detect the presence of vulnerabilities in the contract and can hardly identify their type. Furthermore, they have poor scalability. To resolve these issues, in this study, we developed a smart contract vulnerability detection model based on multi-task learning. By setting auxiliary tasks to learn more directional vulnerability features, the detection capability of the model was improved to realize the detection and recognition of vulnerabilities. The model is based on a hard-sharing design, which consists of two parts. First, the bottom sharing layer is mainly used to learn the semantic information of the input contract. The text representation is first transformed into a new vector by word and positional embedding, and then the neural network, based on an attention mechanism, is used to learn and extract the feature vector of the contract. Second, the task-specific layer is mainly employed to realize the functions of each task. A classical convolutional neural network was used to construct a classification model for each task that learns and extracts features from the shared layer for training to achieve their respective task objectives. The experimental results show that the model can better identify the types of vulnerabilities after adding the auxiliary vulnerability detection task. This model realizes the detection of vulnerabilities and recognizes three types of vulnerabilities. The multi-task model was observed to perform better and is less expensive than a single-task model in terms of time, computation, and storage. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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27 pages, 1668 KiB

Open AccessArticle

SPETS: Secure and Privacy-Preserving Energy Trading System in Microgrid

by Ahmed Samy, Haining Yu, Hongli Zhang and Guangyao Zhang

Sensors 2021, 21(23), 8121; https://doi.org/10.3390/s21238121 - 4 Dec 2021

Cited by 4 | Viewed by 3289

Abstract

Recently, the development of distributed renewable energy resources, smart devices, and smart grids empowers the emergence of peer-to-peer energy trading via local energy markets. However, due to security and privacy concerns in energy trading, sensitive information of energy traders could be leaked to an adversary. In addition, malicious users could perform attacks against the energy market, such as collusion, double spending, and repudiation attacks. Moreover, network attacks could be executed by external attackers against energy networks, such as eavesdropping, data spoofing, and tampering attacks. To tackle the abovementioned attacks, we propose a secure and privacy-preserving energy trading system (SPETS). First, a permissioned energy blockchain is presented to perform secure energy transactions between energy sellers and buyers. Second, a discrete-time double auction is proposed for energy allocation and pricing. Third, the concept of reputation scores is adopted to guarantee market reliability and trust. The proposed energy system is implemented using Hyperledger Fabric (HF) where the chaincode is utilized to control the energy market. Theoretical analysis proves that SPETS is resilient to several security attacks. Simulation results demonstrate the increase in sellers’ and buyers’ welfare by approximately 76.5% and 26%, respectively. The proposed system ensures trustfulness and guarantees efficient energy allocation. The benchmark analysis proves that SPETS consumes few resources in terms of memory and disk usage, CPU, and network utilization. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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9 pages, 1840 KiB

Open AccessCommunication

skillsChain: A Decentralized Application That Uses Educational Robotics and Blockchain to Disrupt the Educational Process

by Panayiotis Christodoulou, Andreas S. Andreou and Zinon Zinonos

Sensors 2021, 21(18), 6227; https://doi.org/10.3390/s21186227 - 16 Sep 2021

Cited by 1 | Viewed by 2549

Abstract

Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial Revolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final results present its efficacy. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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21 pages, 2941 KiB

Open AccessArticle

Interoperable Blockchains for Highly-Integrated Supply Chains in Collaborative Manufacturing

by Paolo Bellavista, Christian Esposito, Luca Foschini, Carlo Giannelli, Nicola Mazzocca and Rebecca Montanari

Sensors 2021, 21(15), 4955; https://doi.org/10.3390/s21154955 - 21 Jul 2021

Cited by 34 | Viewed by 4688

Abstract

Blockchain technology plays a pivotal role in the undergoing fourth industrial revolution or Industry 4.0. It is considered a tremendous boost to company digitalization; thus, considerable investments in blockchain are being made. However, there is no single blockchain technology, but various solutions exist, and they cannot interoperate with one each other. The ecosystem envisioned by the Industry 4.0 does not have centralized management or leading organization, so a single blockchain solution cannot be imposed. The various organizations hold their own blockchains, which must interoperate seamlessly. Despite some solutions for blockchain interoperability being proposed, the problem is still open. This paper aims to devise a secure solution for blockchain interoperability. The proposed approach consists of a relay scheme based on Trusted Execution Environment to provide higher security guarantees than the current literature. In particular, the proposed solution adopts an off-chain secure computation element invoked by a smart contract on a blockchain to securely communicate with its peered counterpart. A prototype has been implemented and used for the performance assessment, e.g., to measure the latency increase due to cross-blockchain interactions. The achieved and reported experimental results show that the proposed security solution introduces an additional latency that is entirely tolerable for transactions. At the same time, the usage of the Trusted Execution Environment imposes a negligible overhead. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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Review

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24 pages, 689 KiB

Open AccessReview

Privacy-Preserving Blockchain Technologies

by Dalton Cézane Gomes Valadares, Angelo Perkusich, Aldenor Falcão Martins, Mohammed B. M. Kamel and Chris Seline

Sensors 2023, 23(16), 7172; https://doi.org/10.3390/s23167172 - 14 Aug 2023

Cited by 1 | Viewed by 3819

Abstract

The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps). However, part of the information exchanged in the typical blockchain is public, which can lead to privacy issues. To avoid or mitigate these issues, some blockchains are applying mechanisms to deal with data privacy. Trusted execution environments, the basis of confidential computing, and secure multi-party computation are two technologies that can be applied in that sense. In this paper, we analyze seven blockchain technologies that apply mechanisms to improve data privacy. We define seven technical questions related to common requirements for decentralized applications and, to answer each question, we review the available documentation and gather information from chat channels. We briefly present each blockchain technology and the answers to each technical question. Finally, we present a table summarizing the information and showing which technologies are more prominent. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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36 pages, 2269 KiB

Open AccessReview

Blockchain for Future Wireless Networks: A Decade Survey

by Tejal Rathod, Nilesh Kumar Jadav, Mohammad Dahman Alshehri, Sudeep Tanwar, Ravi Sharma, Raluca-Andreea Felseghi and Maria Simona Raboaca

Sensors 2022, 22(11), 4182; https://doi.org/10.3390/s22114182 - 31 May 2022

Cited by 13 | Viewed by 9987

Abstract

The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions. Full article

(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)

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