Secure and controllable data management mechanism for multi‐sensor fusion in internet of things

With the rapid growth of digital data and inter-connected devices, the integration of blockchain and Internet of Things technology offers a promising chance to revolutionize data management approaches. With today's global connectivity, the management and securing of ever-increasing amounts the amount of data produced by Internet of Things (IoT) devices is becoming an increasing concern. This study investigates a new solution to address such a concern by integrating blockchain technology into IoT systems. The aim is to develop a strong framework that takes advantage of the decentralized and secure character of blockchain and the wall-to-wall connectivity offered by the IoT. This integration is intended to address important needs such as data integrity, The integration aims to tackle crucial issues like data integrity, privacy, and unauthorized access. With the connectivity and sensing capabilities of the IoT, along with the blockchain technology of decentralized consensus and immutable ledgers, businesses are able to develop secure systems to retrieve, transfer, and store such data. In the experiments conducted on the Ethereum blockchain platform, it has been found that the blockchain and IoT technology solution not only secures data but also facilitates information management. The experiment results demonstrate that the proposed solution is able to protect against tampering, increase the transparency of transactions, and to minimize the chances of unauthorized access. The framework is scalable and highly adaptable, and it is therefore a promising solution for various applications. This framework employs an innovative approach to ensure secure and efficient data management.

Agriculture is an essential part of developing countries' economies. Internet of Things (IoT) sensors can provide data about agricultural fields and then operate based on human input. The IoT is cloud-based technology that allows for scalable management, data management, data security, data analysis, and more. The present IoT solutions are not suitable for the Indian agriculture sector due to it being very complex and not being cost-effective, which means it is not applicable to the reality on the ground. The existing IoT solutions employ paid network GSM/NB-IoT/4G/5G for sensor networks. In this chapter, a holistic affordable IoT framework focused on the agriculture Indian scenario is proposed. The IoT end nodes are battery-operated, and power consumption should be low and adopt low-power wide-area network technology. Cloud services included the weather department, agriculture scientists of Indian Council of Agricultural Research, e-mandi, the Indian market, the nearest point for selling, and so on. It also includes the monitoring of temperature, humidity, soil moisture, irrigation and insect and pest detection, actuator intervention, message notification for disastrous weather warnings, and expert advice for farmers. Crop monitoring via by using machine learning and deep learning algorithms. In future work, this framework can adopt edge and fog computing to improve more secure data management.

A transformative revolution has been triggered by the inclusion of Internet of Things (IoT) sensors in healthcare’s real-time tracking capabilities. Recent advancements in miniaturization and low-power designs, wireless connectivity, and sensor fusion (integration of information from multiple sensors) continue to have a profound impact on the monitoring of health. Intelligent embeddability and superior sensing capabilities facilitate seamless patient data tracking, and strong security protects data against breaches. The role of IoT sensors in healthcare is to connect devices, such as biometric and temperature sensors or imaging and environmental sensors. IoT sensors are used in multiple applications such as fitness and vital signs tracking information, and patient care in smart healthcare facilities. Such technologies enable IoT-based medication adherence systems so that patients follow prescribed treatments, thus improving outcomes and reducing the possibility of hospital readmissions. Compared to regular monitoring devices, IoT sensors provide continuous data capture, increased accuracy, improved patient mobility and personalized healthcare solutions. Healthcare IoT adoption faces challenges in data privacy, regulation, and the complexity of technical integration of healthcare IoT in existing infrastructures. Ethical considerations must also be addressed to ensure equitable access and unbiased use of IoT technology. However, despite these obstacles, the future of IoT-based healthcare technologies remains an attractive proposition with continuous technology evolution likely to improve the quality of care, optimal resource allocation, and decision-making in real-time. This chapter explores the evolution of healthcare monitoring through IoT sensors, detailing their technological mechanisms, applications, benefits, and the potential for transforming the healthcare landscape.

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

This study investigates the deployment of artificial intelligence (AI)–supported structural health monitoring (SHM) systems for in-service bridges using Internet of Things (IoT) sensor networks, a rapidly advancing domain that merges cutting-edge sensing, communication, and data analytics to improve infrastructure safety and durability. Traditional bridge inspections and early wired SHM systems have faced persistent limitations, including manual data interpretation, latency in damage detection, and scalability barriers due to cost and complexity. Recent breakthroughs in wireless IoT technologies, multi-modal sensor fusion, and AI-based analytics now offer the potential for continuous, automated, and highly reliable monitoring of structural integrity across diverse bridge typologies and environmental conditions. To critically synthesize the state of knowledge, this systematic review analyzed 146 peer-reviewed papers published between 2000 and 2022 spanning civil engineering, computer science, and information systems. The review explored key dimensions: (a) the historical evolution and objectives of SHM in bridge engineering; (b) IoT sensor modalities and network architectures enabling large-scale monitoring; (c) AI techniques for damage classification, anomaly detection, and signal feature engineering; (d) system reliability and data integrity strategies including calibration, drift compensation, and cybersecurity; (e) deployment challenges and scalability considerations across steel and concrete bridges; (f) comparative field case studies and lessons from global smart infrastructure programs; and (g) emerging research directions such as digital twins, blockchain data provenance, climate-resilient AI, and hybrid human–AI decision systems. The findings indicate that AI-enhanced SHM significantly improves predictive damage detection, reduces false alarms, and supports timely maintenance decisions, especially when combined with multi-sensor IoT networks and robust data governance. However, challenges remain in standardization, model retraining under concept drift, cost–benefit justification for large-scale deployment, and regulatory acceptance of AI-informed safety decisions. The review also highlights knowledge gaps around extreme climate resilience, secure and scalable data management, and human oversight frameworks for trustworthy AI. By consolidating insights across 146 studies, this work provides an integrated roadmap for researchers, engineers, and policymakers aiming to advance the next generation of smart, resilient, and cost-effective bridge monitoring systems.

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.

The Internet of Things (IoT) is transforming the way physical systems interact with the digital world by enabling seamless communication between devices, sensors, and networks. Through real-time data collection, processing, and intelligent decision-making, IoT has found applications across diverse domains including smart homes, smart cities, healthcare, manufacturing, agriculture, transportation, and logistics. This chapter presents an overview of IoT fundamentals, highlighting its key components such as sensors, actuators, connectivity options, embedded gateways, systems, cloud platforms, and data centers. It discusses how IoT solutions enhance efficiency, productivity, and user experience while supporting sustainable practices. Practical use cases illustrate the technology’s potential to improve quality of life, optimize resources, and drive innovation. Alongside these opportunities, the chapter addresses major challenges such as data security, privacy, interoperability, scalability, and power management. Emerging trends, including the integration of artificial intelligence, machine learning, edge computing, and 5G connectivity, are explored as pathways to strengthen IoT performance and expand its capabilities. The discussion concludes with an outlook on IoT’s future role in creating intelligent, connected, and environmentally responsible systems that can significantly impact both industry and society.

How to realize the safe and efficient use of power terminal equipment data is a key research content to enhance the value of smart grid data. In order to solve this problem, based on the master-slave blockchain theory, this paper designs an intelligent terminal data management system architecture based on the master-slave blockchain. The architecture includes three modules: master blockchain, slave blockchain, and system service interface. In order to improve the security of data in the process of data use, this paper conducts research from the two dimensions of data security storage and data access control, and proposes a data security storage mechanism and a data access control mechanism. In order to improve the efficiency of data query when the amount of data is large, this paper adopts the grouping bloom filtering strategy and the B+ tree index strategy to reduce the number of data queries and improve the efficiency of data query. In the experimental part, from the performance analysis of the improved Bloom filter based on grouping and the performance analysis of the block query mechanism based on the B+ tree index, it is verified that the data usage mechanism proposed in this paper has high block creation and data query efficiency.KeywordsSmart gridPower terminalData securityBlockchain

The Internet of things (IoT) plays important roles in the modern digital world. It has several important roles in the modern power systems and power grids. IoT presents a lot of potential in the power systems and power grids. Some of the support functions are direct and several others are found to be indirect. Either way, IoT can play a lot of important roles in the modern power systems. It can help significantly in the measurement, control, and monitoring of the physical parameters in the power grids. It helps to in the reduction of energy consumption in the power electronic components. It has the potential to provide a lot of operational flexibilities in the power electronic components. Implementation of advanced operational algorithms using artificial intelligence and machine learning is facilitated by the IoT based sensors, actuators and other key components. It can provide smart operational assistance to the power electronic systems used in the power grids. Due to their logical flexibilities IoT sensors can be deployed alongside the power electronic components to track their performances. Consequently, using the IoT sensors' information, the actuators are driven to deliver optimal outcome. IoT sensors' information can be sent directly to the central servers in regular intervals to monitor the overall performances of the power electronic components. In addition to the aforesaid applications, several other potential uses of IoT in power electronics include monitoring of critical power grid parameters such as temperature, current, voltage and vibration at different key locations. In this paper, we analyze the use of IoT in power electronic components in the modern power systems.

Internet of Things (IoT) has gripped domains with this ubiquitous connectivity, in-themoment data collection, and autonomous decision-making. But rising numbers of heterogeneous, extremely constrained IoT devices pose serious concerns regarding data privacy, security, and trust management, drawing great attention into these areas in the academic field and on all sides. Thus, blockchain technology came into the limelight for strengthening security and privacy in IoT systems in a decentralized manner, giving the system immutability, transparency, and distributed trust. This study proposes a Systematic Literature Review (SLR) of blockchain-based approaches that aim to enhance the IoT applications' privacy and security, focusing chiefly on healthcare, supply chains, and smart cities. The review uses a structured methodology to find, select, evaluate, and synthesize relevant peer-reviewed studies published between 2018 and 2025 taken from major scientific databases such as IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Articles were also examined to narrow the scope of study and set the subject. The selected studies are analyzed and classified based on their security goals (e.g., confidentiality, integrity, authentication), privacy-preserving techniques (e.g., anonymization, differential privacy, zero-knowledge proofs), blockchain configurations (e.g., public, private, consortium), and consensus mechanisms. The findings reveal a growing body of research applying blockchain to a wide range of IoT domains, addressing diverse application domains such as healthcare, smart homes, industrial IoT, and agriculture, and demonstrating its potential to enhance data integrity, access control, and authentication. However, the integration of blockchain in IoT also faces challenges such as scalability, latency, and resource overhead, especially in real-time and constrained environments. This review offers a comprehensive synthesis of the state-of-the-art, identifies current limitations and research gaps, and proposes future research directions for building secure, efficient, privacy-aware, and scalable blockchain-enabled IoT systems.

Continuous Optimization of Business Environment for Power Grid Timing Control

The Internet of Things (IoT) has emerged as one of the most transformative paradigms in modern computing, enabling billions of connected devices to sense, process, and transmit data. This growth poses challenges in scalability, security, and real-time data management. Cloud platforms, particularly Amazon Web Services (AWS), have become central enablers of large-scale IoT deployments due to their comprehensive service ecosystem. This paper presents a review of AWS IoT, focusing on its architecture, applications, and open research directions. We analyze core components such as AWS IoT Core, Lambda, Greengrass, and data services, highlighting their roles in enabling cloud-edge integration and secure device management. A survey of academic literature and industrial case studies demonstrates AWS IoT's practical adoption across healthcare, agriculture, and smart city domains. Comparative analysis with Microsoft Azure IoT and Google Cloud IoT positions AWS as a scalable and secure platform, while identifying limitations in latency, cost, and provisioning complexity. Finally, we outline future research opportunities in federated learning, energy-efficient edge inference, protocol interoperability, and blockchain-based provisioning. This review provides a consolidated reference for researchers and practitioners aiming to leverage AWS IoT for next-generation intelligent systems.

The digital transformation of healthcare, driven by the Internet of Medical Things (IoMT), has intensified the need for secure, efficient and interoperable medical data management. This study develops and evaluates a secure system that combines blockchain technology, AES encryption, and zlib compression to enable decentralized, tamper-proof sharing of electronic health records (EHRs) while optimizing storage and ensuring data integrity. The system was implemented as a Flask-based web application and tested using diverse medical file types (TXT, PDF, DICOM, JPG/PNG), measuring compression ratios, encryption/decryption speed, and blockchain hash performance. The results demonstrated that text files achieved the highest compression ratio (~6.4), while encryption and decryption times remained fast and scaled linearly with file size. Blockchain hashing proved efficient (5–13 ms), highlighting the system’s suitability for real-time healthcare environments. The integrated approach effectively addressed core issues in current healthcare data systems such as inefficient storage, poor interoperability, and data security vulnerabilities, especially in managing structured and semi-structured clinical data. It can be concluded from the study that blockchain-based systems can significantly improve the security, scalability, and reliability of healthcare data exchange. The system ensures confidentiality, authenticity, and integrity of patient records while promoting seamless data access among authorized entities. Despite its effectiveness, challenges such as processing overhead and blockchain scalability were noted, suggesting a need for further optimization. Recommendations include the adoption of blockchain-integrated data sharing platforms by healthcare institutions to enhance traceability and trust. File compression should be prioritized for large textual records to reduce bandwidth and storage costs. Future improvements may incorporate biometric authentication for enhanced access control. Additionally, collaboration between government agencies, hospitals, and tech providers is essential to establish regulatory standards for blockchain deployment in health data management.

\n Unmanned Aerial Vehicles (UAVs) are being employed in many fields such as precision agriculture, environmental monitoring, disaster response support, and others. Following the technological development, these vehicles are now being connected to other devices and systems such as the Internet of Things (IoT), allowing them to provide and consume services as one of the things in the environment. The IoT is believed to bring new unprecedented development to our society and will impact and enable new, more powerful applications in many areas. This integration does not come without challenges. The intrinsic IoT heterogeneity regarding devices, platforms, and communication protocols makes the task of service provision and management much harder to be done in the application layer. Security is one of the most critical challenges in IoT-enabled applications, being sometimes put in after the main development is done, if at all. This work demonstrates how to leverage UAVs to be connected in the IoT using SEMU SErvice Management for UAVs. SEMU comprises (a) communication security, including contextaware security; (b) service management (registration, discovery, request and consumption) for different types and data requirements and (c) autonomous task management that allows the UAVs to perform their mission without human intervention. Its functionality and feasibility is demonstrated by its use in different applications simulating real-life scenarios such as a UAV-enabled delivery service and a UAV-enabled data collection from IoT sensors. Future research will be able to leverage on SEMU and its prototype to further investigate and advance the state-of-art regarding IoT-enabled UAV applications and their security mechanisms.\n

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.