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Blockchain Technologies and Security in IoT Networks
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Blockchain Technologies and Security in IoT Networks

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 5038

Special Issue Editor

Dr. Alexander Horst Norta

E-Mail Website
Guest Editor
Baltic Film, Media and Arts School, Tallinn University, Narva mnt 25, 10120 Tallinn, Estonia
Interests: blockchain; distributed systems; smart city; machine-to-everything economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of Internet-of-Things (IoT) systems creates many novel business opportunities, and it is expected that by 2025, circa 55 billion IoT devices will be in operation. The consequence of these IoT systems is the explosion in big-data generation that is challenging to secure. Simultaneously, blockchain technology has found many use-cases beyond the first application of Bitcoin. The vision arises that novel blockchain technology is a means of securing future distributed IoT systems. Thus, in this Special Issue, the aim is to first report research results that intersect the three aspects of IoT, security, and blockchain technology. Thus, paper submissions should explore the relationship between these elements of investigation and how blockchains are useful for resolving security problems in the IoT. Additionally, this Special Issue is interested in outlining open research issues for blockchains in the context of securing the IoT. This Special Issue is also interested in case study submissions reporting examples for existing application cases.

Dr. Alexander Norta
Guest Editor

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. 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 2600 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

  • blockchain
  • IoT
  • scalability
  • security
  • identity authentication

Published Papers (3 papers)

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Research

24 pages, 1917 KiB  
Article
Data Modifications in Blockchain Architecture for Big-Data Processing
by Khikmatullo Tulkinbekov and Deok-Hwan Kim
Sensors 2023, 23(21), 8762; https://doi.org/10.3390/s23218762 - 27 Oct 2023
Viewed by 1350
Abstract
Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities [...] Read more.
Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%. Full article
(This article belongs to the Special Issue Blockchain Technologies and Security in IoT Networks)
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17 pages, 4611 KiB  
Article
Microcontroller-Based PUF for Identity Authentication and Tamper Resistance of Blockchain-Compliant IoT Devices
by Davor Vinko, Kruno Miličević, Ivica Lukić and Mirko Köhler
Sensors 2023, 23(15), 6769; https://doi.org/10.3390/s23156769 - 28 Jul 2023
Cited by 2 | Viewed by 963
Abstract
Blockchain-based applications necessitate the authentication of connected devices if they are employed as blockchain oracles. Alongside identity authentication, it is crucial to ensure resistance against tampering, including safeguarding against unauthorized alterations and protection against device counterfeiting or cloning. However, attaining these functionalities becomes [...] Read more.
Blockchain-based applications necessitate the authentication of connected devices if they are employed as blockchain oracles. Alongside identity authentication, it is crucial to ensure resistance against tampering, including safeguarding against unauthorized alterations and protection against device counterfeiting or cloning. However, attaining these functionalities becomes more challenging when dealing with resource-constrained devices like low-cost IoT devices. The resources of IoT devices depend on the capabilities of the microcontroller they are built around. Low-cost devices utilize microcontrollers with limited computational power, small memory capacity, and lack advanced features such as a dedicated secure cryptographic chip. This paper proposes a method employing a Physical Unclonable Function (PUF) to authenticate identity and tamper resistance in IoT devices. The suggested PUF relies on a microcontroller’s internal pull-up resistor values and, in conjunction with the microcontroller’s built-in analog comparator, can also be utilized for device self-checking. A main contribution of this paper is the proposed PUF method which calculates the PUF value as the average value of many single PUF measurements, resulting in a significant increase in accuracy. The proposed PUF has been implemented successfully in a low-cost microcontroller device. Test results demonstrate that the device, specifically the microcontroller chip, can be identified with high accuracy (99.98%), and the proposed PUF method exhibits resistance against probing attempts. Full article
(This article belongs to the Special Issue Blockchain Technologies and Security in IoT Networks)
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22 pages, 1744 KiB  
Article
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
by Faiza Iqbal, Ayesha Altaf, Zeest Waris, Daniel Gavilanes Aray, Miguel Angel López Flores, Isabel de la Torre Díez and Imran Ashraf
Sensors 2023, 23(11), 5263; https://doi.org/10.3390/s23115263 - 01 Jun 2023
Cited by 4 | Viewed by 1961
Abstract
Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, [...] Read more.
Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes. Full article
(This article belongs to the Special Issue Blockchain Technologies and Security in IoT Networks)
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