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Special Issue "Artificial Intelligence and Blockchain in Wireless Sensors Networks"

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

Deadline for manuscript submissions: closed (30 November 2019).

Special Issue Editors

Prof. Dr. Juan M. Corchado
Website
Guest Editor
IoT European Digital Innovation Hub, Full Professor at the University of Salamanca, Visiting Professor at the Osaka Institute of Technology, Visiting Professor at the Universiti Malaysia Kelantan, President of the IEEE SMC (Spanish Chapter), Director of BISITE - Bioinformatics Intelligent Systems and Educational, Technology Research Group, Salamanca, Spain
Interests: artificial intelligence; machine learning; edge computing; distributed computing; blockchain; consensus model; smart cities; smart grid
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Blockchain is a decentralized transaction and data management technology that has been recently developed and successfully used first for Bitcoin cryptocurrency. Blockchain provides security, anonymity, and data integrity without any third party organization in the control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. Most of the literature about this technology focusses on revealing and improving the limitations of blockchain from privacy and security perspectives.

Artificial intelligence (AI) and machine learning (ML) algorithms may be the complement that blockchain models need to be used in more applications, such as Industry 4.0, Internet of things, domotic systems, Securtech, crypto chips, and so on. Blockchain is a distributed database solution that maintains a continuously growing list of data records that are confirmed by the nodes participating in it. The data is recorded in a public ledger, including the information of every transaction ever completed. It is a decentralized solution where AI and ML algorithms may play different roles in order to guaranty security in an efficient way. Even though blockchain seems to be a suitable solution for conducting transactions using cryptocurrencies, it has some technical challenges that need to be addressed. A high integrity of transactions and privacy of nodes are needed to prevent attacks, and AI may provide a solution, especially when it is used in wireless sensors. Wireless crypto chips may be a solution for many logistics problems, and ML algorithms may be used on them too. 

This Special Issue calls for innovative work that explores new frontiers and challenges in the field of applying AI algorithms to blockchain wireless sensor networks. As mentioned previously, this work could include new machine learning models, distributed AI proposals, hybrid AI systems, and so on, as well as case studies or reviews of the state-of-the-art, in all cases related to blockchain and wireless sensors.

The topics of interest include, but are not limited to, the following:

  • Artificial intelligence models and blockchain for sensor networks
  • Blockchain in intelligent networks
  • Clustering and classification algorithms for blockchain for sensor networks
  • Crypto chips and artificial intelligence
  • Reinforcement learning and blockchain for sensor networks
  • Intelligent systems for fraud detection and forensics in blockchain environments
  • Intelligent applications of blockchain and wireless sensors
  • Multi agent systems and blockchain for wireless sensor networks
  • Regulatory and legal aspects
  • Smart contacts blockchain applications

Prof. Dr. Juan Manuel Corchado Rodríguez
Dr. Javier Prieto Tejedor
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 papers will be 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. Sensors 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 2000 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.

Published Papers (2 papers)

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Research

Open AccessArticle
A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection
Sensors 2020, 20(1), 147; https://doi.org/10.3390/s20010147 - 25 Dec 2019
Cited by 1
Abstract
The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and [...] Read more.
The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain in Wireless Sensors Networks)
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Open AccessArticle
SmartFire: Intelligent Platform for Monitoring Fire Extinguishers and Their Building Environment
Sensors 2019, 19(10), 2390; https://doi.org/10.3390/s19102390 - 25 May 2019
Abstract
Due to fire protection regulations, a minimum number of fire extinguishers must be available depending on the surface area of each building, industrial establishment or workplace. There is also a set of rules that establish where the fire extinguisher should be placed: always [...] Read more.
Due to fire protection regulations, a minimum number of fire extinguishers must be available depending on the surface area of each building, industrial establishment or workplace. There is also a set of rules that establish where the fire extinguisher should be placed: always close to the points that are most likely to be affected by a fire and where they are visible and accessible for use. Fire extinguishers are pressure devices, which means that they require maintenance operations that ensure they will function properly in the case of a fire. The purpose of manual and periodic fire extinguisher checks is to verify that their labeling, installation and condition comply with the standards. Security seals, inscriptions, hose and other seals are thoroughly checked. The state of charge (weight and pressure) of the extinguisher, the bottle of propellant gas (if available), and the state of all mechanical parts (nozzle, valves, hose, etc.) are also checked. To ensure greater safety and reduce the economic costs associated with maintaining fire extinguishers, it is necessary to develop a system that allows monitoring of their status. One of the advantages of monitoring fire extinguishers is that it will be possible to understand what external factors affect them (for example, temperature or humidity) and how they do so. For this reason, this article presents a system of soft agents that monitors the state of the extinguishers, collects a history of the state of the extinguisher and environmental factors and sends notifications if any parameter is not within the range of normal values.The results rendered by the SmartFire prototype indicate that its accuracy in calculating pressure changes is equivalent to that of a specific data acquisition system (DAS). The comparative study of the two curves (SmartFire and DAS) shows that the average error between the two curves is negligible: 8% in low pressure measurements (up to 3 bar) and 0.3% in high pressure (above 3 bar). Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain in Wireless Sensors Networks)
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