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Sustainable Blockchain and Computer Systems
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Sustainable Blockchain and Computer Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4328

Special Issue Editors

Prof. Dr. Wei Liang

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Guest Editor
Department of Control Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
Interests: blockchain technology; dependable computer system; cross-chain technology; data privacy and management
Dr. Asis Kumar Tripathy

E-Mail Website
Guest Editor
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore-632014, India
Interests: cloud computing; IoT; IoV; secure communication; blockchain
Prof. Dr. Tien-Hsiung Weng

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan
Interests: blockchain technology; data privacy protection; parallel programming model, and performance measurement
Prof. Dr. Xiong Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: information security; cloud security; edge computing security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Blockchain technology has greatly influenced society because its transactions are multi-party verified, permanently recorded and linked in chronological order.  It can benefit speed, efficiency, and low cost. As blockchain consumes ever-growing massive energy, the sustainability in blockchain and computer systems significantly affects the blockchain scenarios, such as power consumption, environmental crisis, and regenerative agriculture. Therefore, it is necessary to make the blockchain sustainable and efficient, further striking a balance between the long-term system benefit and the urgent requirement of a reduction in energy consumption.

The special issue aims to highlight the challenges, barriers, opportunities, and best practices in realizing sustainable blockchain and computer systems, particularly including the possible technologies, policies, and critical metrics in sustainability.  This special issue is open to original contributions that explore new trends and possible solutions oriented toward supporting sustainable development.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Sustainable blockchain development
  • Efficient blockchain consensus technology
  • Sustainable policy and metrics in blockchain and computer systems
  • Regenerative energy utilization in blockchain and computer systems
  • Energy conservation and management
  • Energy efficient solutions in blockchain and computer systems
  • Cooling technologies in computer systems
  • AI technologies in sustainable blockchain and computer system

Prof. Dr. Wei Liang
Dr. Asis Kumar Tripathy
Prof. Dr. Tien-Hsiung Weng
Prof. Dr. Xiong Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sustainable blockchain development
  • energy-efficient computer systems
  • smart energy management
  • sustainability metrics

Published Papers (3 papers)

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Research

26 pages, 3115 KiB  
Article
Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems
by Yanli Liu, Qiang Qian, Heng Zhang, Jingchao Li, Yikai Zhong and Neal N. Xiong
Sustainability 2024, 16(1), 171; https://doi.org/10.3390/su16010171 - 23 Dec 2023
Viewed by 945
Abstract
With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and [...] Read more.
With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications. Full article
(This article belongs to the Special Issue Sustainable Blockchain and Computer Systems)
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28 pages, 5497 KiB  
Article
Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation
by Rui Jiang, Jiatao Li, Weifeng Bu and Chongqing Chen
Sustainability 2023, 15(21), 15435; https://doi.org/10.3390/su152115435 - 30 Oct 2023
Viewed by 773
Abstract
In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be [...] Read more.
In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be applicable to all deep models. To address these issues, we propose a sub-network-based model storage and inference solution that integrates blockchain and IPFS, which includes a highly distributed storage method, a tamper-proof checking method, a double-attribute-based permission management method, and an automatic inference method. We also design a smart contract to deploy these methods in the blockchain. The storage method divides a deep model into intra-sub-network and inter-sub-network information. Sub-network files are stored in the IPFS, while their records in the blockchain are designed as a chained structure based on their encrypted address. Connections between sub-networks are represented as attributes of their records. This method enhances model security and improves storage and computational efficiency of the blockchain. The tamper-proof checking method is designed based on the chained structure of sub-network records and includes on-chain checking and IPFS-based checking stages. It efficiently and dynamically monitors model correctness. The permission management method restricts user permission based on the user role and the expiration time, further reducing the risk of model attacks and controlling system efficiency. The automatic inference method is designed based on the idea of preceding sub-network encrypted address lookup. It can distribute trusted off-chain computing resources to perform sub-network inference and use the IPFS to store model inputs and sub-network outputs, further alleviating the on-chain storage burden and computational load. This solution is not restricted to model architectures and division methods, or sub-network recording orders, making it highly applicable. In experiments and analyses, we present a use case in intelligent transportation and analyze the security, applicability, and system efficiency of the proposed solution, particularly focusing on the on-chain efficiency. The experimental results indicate that the proposed solution can balance security and system efficiency by controlling the number of sub-networks, thus it is a step towards sustainable model services for deep learning. Full article
(This article belongs to the Special Issue Sustainable Blockchain and Computer Systems)
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20 pages, 4017 KiB  
Article
Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism
by Yilin Liang, Yuping Hu, Dongjun Luo, Qi Zhu, Qingxuan Chen and Chunmei Wang
Sustainability 2023, 15(13), 10480; https://doi.org/10.3390/su151310480 - 03 Jul 2023
Viewed by 1622
Abstract
Despite the emergence of a presale mechanism that reduces manufacturing and ordering risks for retailers, optimizing the real-time pricing strategy in this mechanism and unknown demand environment remains an unsolved issue. Consequently, we propose an automatic real-time pricing system for e-retailers under the [...] Read more.
Despite the emergence of a presale mechanism that reduces manufacturing and ordering risks for retailers, optimizing the real-time pricing strategy in this mechanism and unknown demand environment remains an unsolved issue. Consequently, we propose an automatic real-time pricing system for e-retailers under the inventory backlog impact in the presale mode, using deep reinforcement learning technology based on the Dueling DQN algorithm. This system models the multicycle pricing problem with a finite sales horizon as a Markov decision process (MDP) to cope with the uncertain environment. We train and evaluate the proposed environment and agent in a simulation environment and compare it with two tabular reinforcement learning algorithms (Q-learning and SARSA). The computational results demonstrate that our proposed real-time pricing learning framework for joint inventory impact can effectively maximize retailers’ profits and has universal applicability to a wide range of presale models. Furthermore, according to a series of experiments, we find that retailers should not neglect the impact of the presale or previous prices on consumers’ purchase behavior. If consumers pay more attention to past prices, the retailer must decrease the current price. When the cost of inventory backlog increases, they need to offer deeper discounts in the early selling period. Additionally, introducing blockchain technology can improve the transparency of commodity traceability information, thus increasing consumer demand for purchase. Full article
(This article belongs to the Special Issue Sustainable Blockchain and Computer Systems)
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