Energy: reducing latency in IoT DLTs for AI-driven real-time solutions

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.

This research explores the perspectives of 100 cadets studying multimodal transportation on integrating Internet of Things (IoT) to enhance port security and predictive maintenance. Using qualitative methods, including interviews and document analysis, the study investigates the effectiveness of IoT in real-time monitoring, data security, and predictive maintenance. The findings highlight cadets' recognition of IoT's potential to transform port operations, particularly in improving security measures and maintenance strategies. Cadets also emphasize the importance of professionalism and adherence to standards in IoT integration, valuing compliance with standards, understanding of IoT, training and education, and ethical considerations. The research underscores the need for continuous education and training programmes to prepare future industry professionals for the challenges and opportunities presented by IoT technologies. Overall, this study contributes to the advancement of knowledge in transportation management and education, offering insights for policymakers, industry practitioners, and educators on the integration of IoT in port operations.

Integrating Internet of Things (IoT) with Artificial Intelligence (AI) is one of the catalysts for improving traditional healthcare services. This integration has created many opportunities that have led to healthcare shifting towards enabling home care, the concept that harnesses the technologies advanced potential such as the IoT and deep learning for intelligent monitor and manage chronic diseases. As population growth increases, restrictions on traditional healthcare services increase. Some diseases, such as chronic diseases, require innovative solutions that go beyond the boundaries of traditional healthcare settings due to their impact on individuals’ health for example traditional healthcare systems have little capacity to provide high-quality and real-time services. Empowering home care services using deep learning and internet of things technology is promising. It enables continuous monitoring through interconnected devices and deep learning, which provides intelligent insights from massive data sets. This brief explores the key components of enabling home care, including continuous patient health monitoring, predictive analytics, medication management, and remote patient support by healthcare providers, and provides friendly interfaces for end-users. The conjunction between the IoT and deep learning in home-care signals a shift toward precision medicine, enhancing patient outcomes and creating a sustainable model for chronic disease management in the era of decentralized healthcare. This review article aims to discuss the following aspects: presenting the latest technologies in home care systems, showing the merit of combining the Internet of Medical Things (IoMT) and deep learning and its role in monitoring patient conditions and managing chronic disease to improve patient health status accurately, in real-time, and cost-effective, and lastly, debating future studies and providing recommendations for the ongoing development of home care remote monitoring applications.

Smart health infrastructure that incorporates the Internet of Things (IoT) and edge computing offers an innovative approach to improving disease response and surveillance Public health emergencies and chronic disease surveillance are two areas where this program is particularly important because of the significant impact that early intervention can have on patient outcomes. Hurdles for the integration of healthcare IoT and edge computing are data privacy and security concerns, performance issues on IoT devices as well as robust network infrastructure needs. To reduce its reliance on computers, Cloud has come up with a plan of introducing Edge Computing-based Context Health Monitoring System (EC-CHMS) which enables faster data analysis and response times by eliminating its use. The healthcare information is locally managed between the networks throughout the system network through edge computing in EC-CHMS that incorporates cloud to manage healthcare information locally between networks within the entire network. This technology can recognize a patient’s life threatening condition by fusing IoT sensors with machine learning algorithms and has numerous possibilities for healthcare applications such as early disease detection, real-time outbreak management, remote patient care among others. Continuous and context-aware health monitoring is possible using this system that supports proactive healthcare interventions towards enhancing overall health delivery efficiency. These results demonstrate that EC-CHMS outperforms traditional cloud-based systems in terms of handling data efficiently and time taken to run the code. To ensure accuracy and reliability of these vital signs, simulation shows how multiple health issues can be handled by it.

This book chapter explores the intersection of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), highlighting their transformative impact on various industries and applications. As IoT devices generate massive amounts of real-time data, AI and ML algorithms provide the analytical power necessary to derive actionable insights, automate processes, and enhance decision-making. The chapter covers key AI and ML techniques, such as supervised and unsupervised learning, reinforcement learning, and deep learning, and discusses their integration into IoT systems for improved efficiency, scalability, and real-time performance. Specific use cases, including smart agriculture, industrial IoT, and healthcare, are examined to demonstrate the practical applications of these technologies. Furthermore, the chapter addresses the challenges in data processing, security, and privacy that arise from IoT and AI integration, as well as the future potential of these systems in driving innovation across sectors. This comprehensive analysis serves as a foundational resource for understanding the synergistic relationship between AI, ML, and IoT in creating intelligent, connected systems.

This paper introduces an Advanced Automobile Safety Module that integrates Internet of Things (IoT), artificial intelligence (AI), and computer vision to enhance vehicle safety and accident prevention. The system is built on a microcontroller-based platform, incorporating blind spot detection, collision warning, alcohol detection, voice command assistance, drowsiness detection, traffic sign recognition, tire pressure monitoring, NFC-based access control, and a black box data recorder. By leveraging computer vision and deep learning models, the system detects driver fatigue, recognizes traffic signs, and monitors vehicle surroundings to prevent potential collisions. Real-time data from sensors is processed using machine learning algorithms to ensure precise decision-making, while NFC authentication and cloud-based data storage enhance security and remote monitoring. Successful detection of hazards prompts immediate alerts and automated corrective actions, ensuring driver and passenger safety. Performance evaluations indicate high accuracy in detecting road hazards, drowsiness, and alcohol levels, demonstrating the system’s effectiveness. Future advancements will focus on cloud-based AI analytics, vehicle-to-vehicle communication, and adaptive driving assistance for further improvements. Keywords—Automobile Safety, Collision Prevention, Driver Monitoring, Artificial Intelligence, Internet of Things (IoT), Computer Vision

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in supporting national economic growth and expanding employment opportunities in Indonesia. However, many MSMEs still face significant challenges in improving production efficiency due to limited human resources, low levels of technology adoption, and suboptimal operational management. To address these issues, this study developed an innovative digital technology platform designed to help MSMEs enhance productivity, reduce material waste, and accelerate the production process. The platform integrates Internet of Things (IoT), Artificial Intelligence (AI), and simplified Enterprise Resource Planning (ERP) technologies tailored for small business operations. Through IoT, production processes can be monitored in real time; AI is applied to analyze sales data and predict material requirements and production schedules; while the ERP system automates inventory, transaction, and financial reporting processes. Trials conducted across MSMEs in the food, handicraft, and textile sectors demonstrated a 30% improvement in production efficiency and a 20% reduction in operational costs. The results indicate that the implementation of innovative technological platforms can significantly enhance efficiency, accuracy, and competitiveness among MSMEs. Digital transformation not only increases production efficiency but also enables broader business integration into the global market through cloud-based systems. Government and institutional support are essential to expand the adoption of such technologies, ensuring that Indonesian MSMEs become more adaptive, productive, and sustainable in the era of Industry 4.0.

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

The rapid advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming the concept of smart cities by enhancing the quality of life through technology-driven solutions. Among the most significantly impacted sectors is healthcare. AI-powered smart healthcare systems now enable real-time patient monitoring, early disease prediction, and faster medical decision-making. Wearable devices and mobile technologies offer continuous health tracking and telemedicine services, while emerging technologies like 6G sensing promise even greater improvements. This paper critically reviews recent developments in AI and IoT applications within smart healthcare, highlights current challenges such as data security and interoperability, and discusses future research directions to create more efficient, autonomous healthcare ecosystems in smart cities.

This paper explores the integration of artificial intelligence (AI) technologies with human rights considerations in plastic waste recycling, focusing on the context of Nigeria. Using an explanatory design and a basic review of the literature, it examines how AI can enhance human rights protection among workers in plastic waste recycling firms. The paper emphasizes the role of government policies and regulations in ensuring human rights protection, the need for ethical guidelines for AI use, and the potential of AI to improve worker safety and reduce environmental pollution in recycling facilities. Key recommendations for future research include enhancing AI capabilities for waste sorting, integrating Internet of Things (IoT) devices for real-time monitoring, and prioritizing ethics in AI development. Collaboration between stakeholders is identified as crucial, with multi-stakeholder partnerships and policy coherence essential for the effective implementation of AI technologies in plastic waste recycling. In conclusion, integrating AI with human rights considerations in plastic waste recycling is pivotal for enhancing efficiency, reducing pollution, and ensuring sustainable waste management practices. This paper contributes to the growing body of literature on AI and human rights in waste management, offering insights for policymakers, industry stakeholders, and researchers in Nigeria and beyond.

<p>In the rapidly evolving technological landscape, modern innovations such as blockchain (BC), artificial intelligence (AI), and the Internet of Things (IoT) are playing a pivotal role in realizing the vision of Society 5.0. This concept envisions a harmonious integration of physical, digital, and human elements to create a sustainable and progressive future. Emerging technologies like robotics, biotechnology, quantum computing, 5G/6G networks, smart cities, and the metaverse are expected to further amplify the impact of Society 5.0, driving transformative changes across industries and societal structures. The integration of BC, AI, and IoT within cloud-based frameworks offers a robust solution to enhance security, privacy, and data management. The integration of blockchain, AI, and IoT within a cloud-based framework offers a transformative solution to address the challenges of modern societies. By leveraging these technologies, the proposed platform aims to create a secure, efficient, and sustainable ecosystem that enhances quality of life and drives progress toward the vision of Society 5.0. This approach not only tackles current societal issues but also paves the way for a more interconnected and innovative future. This article introduces a platform that seamlessly combines these technologies to provide a secure and reliable foundation for data collection, sharing, and analysis while enabling the development of smart applications that improve efficiency, productivity, and overall quality of life (QoL).</p>

This paper discusses a crucial component of contemporary agriculture: using Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) to monitor and reduce carbon emissions from agriculture. We concentrate on how these cutting-edge technologies augment Climate-Smart Agriculture (CSA) and advance sustainable farming methods. A thorough analysis of the ways in which IoT, Big Data, and AI can be integrated to track carbon footprints and promote more general sustainability goals in agriculture is given in this research. As a significant contribution, we provide a practical, all-encompassing system architecture that integrates real-time data analytics, predictive modeling, and IoT-enabled sensors created especially for carbon footprint assessment. This paper assesses how well these technologies work to enhance emission monitoring, operational effectiveness, and environmental compliance while highlighting their observable advantages through real-world case studies. The rigorous analysis of issues including data interoperability, device energy efficiency, and implementation costs also sheds light on current research gaps. The report also outlines potential future paths, such as blockchain-based carbon credit trading platforms, scalable IoT-based carbon markets, and machine learning (ML) algorithms for precision farming. The goal is to offer helpful guidance on how to apply cutting-edge technology in the agricultural sector to achieve carbon neutrality and environmental sustainability.

The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes, and data security. With the increasing complexity of healthcare systems, the integration of cutting-edge technologies such as Artificial Intelligence. The COVID-19 pandemic significantly accelerated digital transformation, compelling healthcare institutions to adopt telemedicine, AI-assisted diagnostics, and cloud-based medical records to meet growing patient demands and resource constraints. The rapid digital transformation of healthcare is driven by advancements in Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data. This review article aims to analyze the objectives and implications of digital transformation in medical institutions, focusing on the integration of AI, blockchain, and IoT in hospital management. The methodological approach for this review article focuses on synthesizing existing literature to examine the role of Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data in the digital transformation of healthcare management. The integration of Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), and Big Data has demonstrated significant improvements in healthcare management, enhancing efficiency, patient outcomes, and data security.

As 4G technology served as a foundation, the emergence of 5G is now underway, ushering in a new era of connectivity. With the growing demand for seamless internet experiences, particularly in the face of escalating internet subscribers, the networking domain must evolve. Developing nations strive to align with this dynamic landscape, necessitating an upgrade that integrates internet of things (IoT), natural language processing (NLP), and artificial intelligence (AI) with network infrastructure. By enhancing networking systems with lower latency and improved scalability, 5G addresses congestion issues. This is achieved by coupling mm‐wave technology with the 5G framework through the 3rd Generation Partnership Project, significantly amplifying channel bandwidth. However, the comprehensive analysis underscores challenges in 5G implementation, encompassing aspects like distance, orientation, non‐line‐of‐sight conditions, protocol utilization, and server positioning. Drawing from a dataset provided by the University of Minnesota that illuminates the limitations of 5G implementation in select US cities, this paper extends its focus to densely populated regions in developing countries of the sub‐continent. Employing a machine learning approach, the paper delves into the constraints of 5G deployment in such areas. Ultimately, this research aims to shed light on the intricacies of 5G's implementation challenges, contributing to the discourse on enhancing network infrastructure in the evolving landscape of global connectivity.