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Blockchain Technology for Internet of Things

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

Deadline for manuscript submissions: 30 December 2026 | Viewed by 13589

Special Issue Editor

Dr. Faisal Jamil

E-Mail Website
Guest Editor
School of Computing, Engineering and Intelligent Systems, Ulster University, Derry-Londonderry BT48 7JL, Northern Ireland, UK
Interests: Internet of Things; blockchain; big data; machine learning; indoor navigation; digital twins; metaverse
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Blockchain is a decentralized, distributed ledger technology that ensures data security, transparency, and immutability through a network of nodes that validate and record transactions in linked blocks. It eliminates intermediaries, reduces data tampering, and enhances trust. The IoT is a network of interconnected devices communicating and exchanging data over the internet, enabling real-time monitoring, data collection, and automation across various sectors. The convergence of blockchain and IoT addresses IoT's challenges, such as security, privacy, and interoperability, through secure data exchange, automated processes via smart contracts, and decentralized peer-to-peer communication. This Special Issue aims to explore blockchain's application in enhancing IoT across multiple domains. Specifically, this Special Issue aims to focus on the following:

  • Applying blockchain in healthcare to secure patient data and ensure tamper-proof medical records.
  • Utilizing blockchain for smart cities ensures secure transactions, improving efficiency and trust in city initiatives.
  • Leveraging blockchain in smart navigation secures vehicle-to-vehicle communication, ensuring reliable navigation information.
  • Implementing blockchain for mission-critical IoT systems enhances security and resilience in critical infrastructure and industrial IoT.
  • Employing blockchain in big data ensures decentralized, secure processing, and sharing of IoT-generated data.
  • Using blockchain for supply chain management ensures transparency and traceability, reducing fraud and improving efficiency.
  • Integrating blockchain in agriculture secures IoT sensor data, aiding informed decisions and traceability of agricultural products.
  • Adopting blockchain for energy management secures transactions in smart grids, enhancing energy management efficiency.

Dr. Faisal Jamil
Guest Editor

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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 technology
  • Internet of Things (IoT)
  • healthcare
  • smart cities
  • smart navigation
  • mission-critical IoT systems
  • big data
  • supply chain management
  • agriculture
  • energy management
  • decentralized ledger
  • smart contracts
  • data security
  • privacy
  • interoperability
  • decentralized identity management

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Published Papers (7 papers)

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Research

37 pages, 2784 KB  
Article
FedSMOTE-DP: Privacy-Aware Federated Ensemble Learning for Intrusion Detection in IoMT Networks
by Theyab Alsolami and Mohammad Ilyas
Sensors 2026, 26(5), 1592; https://doi.org/10.3390/s26051592 - 3 Mar 2026
Viewed by 568
Abstract
The Internet of Medical Things (IoMT) transforms healthcare through interconnected medical devices but faces significant cybersecurity threats, particularly intrusion and exfiltration attacks. Centralized intrusion detection systems (IDSs) require data aggregation, presenting privacy and scalability risks. This paper proposes FedEnsemble-DP, a privacy-aware Federated Learning [...] Read more.
The Internet of Medical Things (IoMT) transforms healthcare through interconnected medical devices but faces significant cybersecurity threats, particularly intrusion and exfiltration attacks. Centralized intrusion detection systems (IDSs) require data aggregation, presenting privacy and scalability risks. This paper proposes FedEnsemble-DP, a privacy-aware Federated Learning (FL) framework for decentralized intrusion detection in IoMT networks. The framework integrates three data balancing scenarios (Raw Imbalanced, Local SMOTE, Centralized SMOTE) with Differential Privacy (DP) and Secure Aggregation mechanisms. Extensive experiments on WUSTL-EHMS-2020 and CIC-IoMT-2024 datasets under non-IID settings (Dirichlet α = 0.3) demonstrate that models with strong privacy guarantees (ε = 3.0) frequently match or exceed non-private baselines. Key findings show Local SMOTE with ε = 3.0 achieved 94.60% accuracy and 0.9598 AUC, while Raw Imbalanced with ε = 3.0 attained 94.50% accuracy and 0.9494 AUC. Even with strict privacy (ε = 3.0), these results surpassed the non-private baseline (93.20% accuracy) in the raw scenario. Centralized SMOTE showed effectiveness but introduced training instability. These results indicate that local data balancing combined with calibrated DP noise can yield high detection performance while preserving privacy, effectively bridging security-performance and data confidentiality requirements in distributed healthcare networks. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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20 pages, 2104 KB  
Article
Research on Dynamic Spectrum Sharing in the Internet of Vehicles Based on Blockchain and Game Theory
by Xianhao Shen, Mingze Li, Jiazhi Yang and Jinsheng Yi
Sensors 2026, 26(4), 1190; https://doi.org/10.3390/s26041190 - 12 Feb 2026
Viewed by 383
Abstract
With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and [...] Read more.
With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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24 pages, 1716 KB  
Article
Multi-Modal Decentralized Hybrid Learning for Early Parkinson’s Detection Using Voice Biomarkers and Contrastive Speech Embeddings
by Khaled M. Alhawiti
Sensors 2025, 25(22), 6959; https://doi.org/10.3390/s25226959 - 14 Nov 2025
Cited by 1 | Viewed by 1529
Abstract
Millions worldwide are affected by Parkinson’s disease, with the World Health Organization highlighting its growing prevalence. Early neuromotor speech impairments make voice analysis a promising tool for detecting Parkinson’s, aided by advances in deep speech embeddings. However, existing approaches often rely on either [...] Read more.
Millions worldwide are affected by Parkinson’s disease, with the World Health Organization highlighting its growing prevalence. Early neuromotor speech impairments make voice analysis a promising tool for detecting Parkinson’s, aided by advances in deep speech embeddings. However, existing approaches often rely on either handcrafted acoustic features or opaque deep representations, limiting diagnostic performance and interoperability. To address this, we propose a multi-modal decentralized hybrid learning framework that combines structured voice biomarkers from the UCI Parkinson’s dataset (195 sustained-phonation samples from 31 subjects) with contrastive speech embeddings derived from the DAIC-WOZ corpus (189 interview recordings originally collected for depression detection) using Wav2Vec 2.0. This system employs an early fusion strategy followed by a dense neural classifier optimized for binary classification. By integrating both clinically interpretable and semantically rich features, the model captures complementary phonatory and affective patterns relevant to early-stage Parkinson’s detection. Extensive evaluation demonstrates that the proposed method achieves an accuracy of 96.2% and an AUC of 97.1%, outperforming unimodal and baseline fusion models. SHAP-based analysis confirms that a subset of features have disproportionately high discriminative value, enhancing interpretability. Overall, the proposed framework establishes a promising pathway toward data-driven, non-invasive screening for neurodegenerative conditions through voice analysis. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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19 pages, 3879 KB  
Article
CSA: Utility Optimization Scheduling Algorithm for IoT Blockchain Sharding Committees
by Xin Cong, Qi Jing, Lingling Zi and Changjiang Lin
Sensors 2025, 25(6), 1648; https://doi.org/10.3390/s25061648 - 7 Mar 2025
Cited by 1 | Viewed by 1385
Abstract
The rapid proliferation of the Internet of Things (IoT) poses significant challenges for utility optimization in sharding blockchain systems. In this paper, we propose a Committee Scheduling Algorithm (CSA), which employs an iterative optimization framework based on the Markov chain to balance transaction [...] Read more.
The rapid proliferation of the Internet of Things (IoT) poses significant challenges for utility optimization in sharding blockchain systems. In this paper, we propose a Committee Scheduling Algorithm (CSA), which employs an iterative optimization framework based on the Markov chain to balance transaction throughput, cumulative latency, and transaction fees. CSA dynamically adjusts the committee members to achieve near-optimal solutions while addressing operational constraints. Theoretical analysis demonstrates the convergence bounds of the algorithm and its robustness against Sybil and eclipse attacks, ensuring high entropy for committee selection. Experimental results show that CSA outperforms Stochastic-Exploration (SE), Simulated Annealing (SA), and Policy Gradient-Based Computing Task Scheduling (PG-CTS) in terms of utility, convergence speed, and adaptability to dynamic events, with the committee scheduling utility improving by about 30%. Furthermore, CSA demonstrates stable performance in large-scale IoT environments characterized by dynamic node additions and failures. This paper offers a robust and adaptive solution for utility optimization in sharding blockchains, thereby improving the scalability, security, and efficiency of IoT applications. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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27 pages, 1569 KB  
Article
Federated Learning Framework for Real-Time Activity and Context Monitoring Using Edge Devices
by Rania A. Alharbey and Faisal Jamil
Sensors 2025, 25(4), 1266; https://doi.org/10.3390/s25041266 - 19 Feb 2025
Cited by 16 | Viewed by 4614
Abstract
With the increasing need for effective elderly care solutions, this paper presents a novel federated learning-based system that uses smartphones as edge devices to monitor and enhance elderly care in real-time. In this system, elderly individuals carry smartphones equipped with Inertial Measurement Unit [...] Read more.
With the increasing need for effective elderly care solutions, this paper presents a novel federated learning-based system that uses smartphones as edge devices to monitor and enhance elderly care in real-time. In this system, elderly individuals carry smartphones equipped with Inertial Measurement Unit (IMU) sensors, including an accelerometer for activity recognition, a barometer for altitude detection, and a combination of the accelerometer, gyrometer, and magnetometer for location tracking. The smartphones continuously collect real-time data as the elderly individuals go about their daily routines. These data are processed locally on each device to train personalized models for activity recognition and contextual monitoring. The locally trained models are then sent to a federated server, where the FedAvg algorithm is used to aggregate model parameters, creating an improved global model. This aggregated model is subsequently distributed back to the smartphones, enhancing their activity recognition capabilities. In addition to model updates, information on the users’ location, altitude, and context is sent to the server to enable the continuous monitoring and tracking of the elderly. By integrating activity recognition with location and altitude data, the system provides a comprehensive framework for tracking and supporting the well-being of elderly individuals across diverse environments. This approach offers a scalable and efficient solution for elderly care, contributing to enhanced safety and overall quality of life. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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24 pages, 2698 KB  
Article
A Blockchain Parallel Activity Architecture with Social Network Graphs as Carriers for Internet of Things Networks
by Xin Cong and Lingling Zi
Sensors 2025, 25(4), 1003; https://doi.org/10.3390/s25041003 - 8 Feb 2025
Cited by 2 | Viewed by 2349
Abstract
There is a need for information transactions between nodes in the Internet of Things (IoT) and blockchain technology can guarantee the anonymity and security of such transactions. However, current blockchain systems require networks to be connected in real time, but IoT networks cannot [...] Read more.
There is a need for information transactions between nodes in the Internet of Things (IoT) and blockchain technology can guarantee the anonymity and security of such transactions. However, current blockchain systems require networks to be connected in real time, but IoT networks cannot fulfill this requirement. Therefore, we put forward a blockchain parallel activity architecture using carriers (CBPA), which is capable of deploying blockchain systems on IoT networks. Firstly, the blockchain operation architecture, and its components are demonstrated. Secondly, the generation methods of the carrier, the carrier block and the transaction block are designed, respectively. Additionally, a feature-to-transaction correspondence algorithm is proposed, with the objective of accommodating the previous work of nodes when they are in a disconnected state within the network. Thirdly, a parallel generation method for transaction blocks is designed to permit multiple nodes to collaborate in generating blocks, thereby reducing the difficulty of block generation while accelerating the generation speed. Finally, intra-block and cross-block conflict resolution algorithms, as well as a block consensus and fork processing algorithm, are designed to ensure that nodes can participate in blockchain activities without being at a disadvantage and obtain legal benefits even when operating on a network with high communication latency. Theoretical analysis indicates that CBPA has both security and liveness. The experimental results show that when the block size is 1MB, CBPA improves the average throughput by about 10% and reduces the average latency by about 14% compared to existing schemes. When the percentage of failed nodes reaches about 18%, the blocking time increases significantly, and the valid block rate decreases by 0.2%. The proposed CBPA architecture expands the applicable blockchain network and provides practical solutions for disconnected operations. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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21 pages, 1868 KB  
Article
AllianceBlockchain in the Governance Innovation of Internet Hospitals
by Xiaofeng Wang, Xiaoguang Yue, Ahthasham Sajid and Noshina Tariq
Sensors 2025, 25(1), 142; https://doi.org/10.3390/s25010142 - 29 Dec 2024
Cited by 2 | Viewed by 1433
Abstract
The rise of Internet hospitals has significant issues associated with data security and governance in managing sensitive patient data. This paper discusses an alliance blockchain (i.e., a private blockchain) model for governance innovation in internet hospitals with an improved encryption methodology. We compare [...] Read more.
The rise of Internet hospitals has significant issues associated with data security and governance in managing sensitive patient data. This paper discusses an alliance blockchain (i.e., a private blockchain) model for governance innovation in internet hospitals with an improved encryption methodology. We compare our proposed model, improved Rivest–Shamir–Adleman (RSA) encryption, integrated into the blockchain framework. Improved RSA achieves impressive improvements in all key metrics by increasing the throughput by 24.7% and lowering the latency by 19.8% compared to the base model. Thus, the improved model is more optimized for processing transactions related to healthcare data. Memory usage was also reduced by 14.3%. While encryption time remained pretty close, the decryption time remarkably improved by 97.5%. IoT sensors are one of the foundations for Internet hospitals that produce consistent patient data streams, such as physiological and environmental metrics. The proposed alliance blockchain model enables the secure and efficient real-time management of this sensor data. These results demonstrate the capability of alliance blockchain and cryptographic upgrades in creating safe and efficient governance frameworks for Internet hospitals. Full article
(This article belongs to the Special Issue Blockchain Technology for Internet of Things)
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