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Scalable Blockchain and AI-Based Embedded IoT Systems for Smart Spaces...
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Scalable Blockchain and AI-Based Embedded IoT Systems for Smart Spaces (3rd Edition)

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

Deadline for manuscript submissions: 25 August 2026 | Viewed by 2902

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,

The Internet of Things (IoT) has been playing a vital role in adding value to human lives. In recent years, IoT applications have been coupled with machine learning techniques to form intelligent IoT-enabled blockchain applications. However, for intelligent IoT nodes, the machine learning technologies should be lightweight in order to meet the constrained capabilities of the embedded hardware. This Special Issue aims to highlight advances in the open research topics in this field, which include, but are not limited to, the following:

  • Optimizing existing machine learning architecture for embedded IoT devices;
  • Lightweight machine learning architecture and frameworks;
  • Distributed predictive optimization;
  • Positioning systems and infrastructures;
  • Energy-saving and energy harvesting methods and techniques;
  • Blockchain for security and privacy;
  • Data collection and management methods (big data and data retrieval);
  • Lightweight intelligent IoT service orchestration;
  • Intelligent IoT for lightweight driver-assistance systems in electric vehicles.

Dr. Faisal Jamil
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 250 words) can be sent to the Editorial Office for assessment.

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
  • Internet of Things (IoT)
  • smart systems

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Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 1819 KB  
Article
Multi-Modal Anomaly Detection in Review Texts with Sensor-Derived Metadata Using Instruction-Tuned Transformers
by Khaled M. Alhawiti
Sensors 2025, 25(22), 7048; https://doi.org/10.3390/s25227048 - 18 Nov 2025
Viewed by 551
Abstract
Fake review detection is critical for maintaining trust and ensuring decision reliability across digital marketplaces and IoT-enabled ecosystems. This study presents a zero-shot framework for multi-modal anomaly detection in user reviews, integrating textual and metadata-derived signals through instruction-tuned transformers. The framework integrates three [...] Read more.
Fake review detection is critical for maintaining trust and ensuring decision reliability across digital marketplaces and IoT-enabled ecosystems. This study presents a zero-shot framework for multi-modal anomaly detection in user reviews, integrating textual and metadata-derived signals through instruction-tuned transformers. The framework integrates three complementary components: language perplexity scoring with FLAN-T5 to capture linguistic irregularities, unsupervised reconstruction via a transformer-based autoencoder to identify structural deviations, and semantic drift analysis to measure contextual misalignment between task-specific and generic embeddings. To enhance applicability in sensor-driven environments, the framework incorporates device-level metadata such as timestamps, product usage logs, and operational signals to enable cross-validation between unstructured text and structured sensor features. A unified anomaly score fusing textual and sensor-informed signals improves robustness under multi-modal detection scenarios, while interpretability is achieved through token-level saliency maps for textual analysis and feature-level attributions for sensor metadata. Experimental evaluations on the Amazon Reviews 2023 dataset, supplemented by metadata-rich sources including the Amazon Review Data 2018 and Historic Amazon Reviews (1996–2014) datasets demonstrate strong zero-shot performance (AUC up to 0.945) and additional few-shot adaptability under limited supervision (AUC > 0.95), maintaining stable precision–recall trade-offs across product domains. The proposed framework provides real-world impact by enabling real-time, multi-modal fake review detection in IoT-driven platforms and smart spaces, supporting consumer trust, automated decision-making, and transparent anomaly detection in sensor-enhanced digital ecosystems. Full article
(This article belongs to the Special Issue Scalable Blockchain and AI-Based Embedded IoT Systems for Smart Spaces (3rd Edition))
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22 pages, 1443 KB  
Article
AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration
by Adeel Rafiq, Muhammad Zeeshan Shakir, David Gray, Julie Inglis and Fraser Ferguson
Sensors 2025, 25(19), 6248; https://doi.org/10.3390/s25196248 - 9 Oct 2025
Viewed by 1740
Abstract
Despite the widespread adoption of network monitoring tools, Managed Service Providers (MSPs), specifically small- and medium-sized enterprises (SMEs), continue to face persistent challenges in achieving predictive, multi-tenant-aware visibility across distributed client networks. Existing monitoring systems lack integrated predictive analytics and edge intelligence. To [...] Read more.
Despite the widespread adoption of network monitoring tools, Managed Service Providers (MSPs), specifically small- and medium-sized enterprises (SMEs), continue to face persistent challenges in achieving predictive, multi-tenant-aware visibility across distributed client networks. Existing monitoring systems lack integrated predictive analytics and edge intelligence. To address this, we propose an AI- and IoT-driven monitoring and visualisation framework that integrates edge IoT nodes (Raspberry Pi Prometheus modules) with machine learning models to enable predictive anomaly detection, proactive alerting, and reduced downtime. This system leverages Prometheus, Grafana, and Mimir for data collection, visualisation, and long-term storage, while incorporating Simple Linear Regression (SLR), K-Means clustering, and Long Short-Term Memory (LSTM) models for anomaly prediction and fault classification. These AI modules are containerised and deployed at the edge or centrally, depending on tenant topology, with predicted risk metrics seamlessly integrated back into Prometheus. A one-month deployment across five MSP clients (500 nodes) demonstrated significant operational benefits, including a 95% reduction in downtime and a 90% reduction in incident resolution time relative to historical baselines. The system ensures secure tenant isolation via VPN tunnels and token-based authentication, while providing GDPR-compliant data handling. Unlike prior monitoring platforms, this work introduces a fully edge-embedded AI inference pipeline, validated through live deployment and operational feedback. Full article
(This article belongs to the Special Issue Scalable Blockchain and AI-Based Embedded IoT Systems for Smart Spaces (3rd Edition))
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