Internet of Things最新文献

{"title":"LoRa-SPaaS: Spectrum sensing as a service using LoRaWAN: Resources management and practical considerations","authors":"Abbass Nasser ,&nbsp;Hussein Al Haj Hassan ,&nbsp;Alaaeddine Ramadan ,&nbsp;Chamseddine Zaki ,&nbsp;Nada Sarkis ,&nbsp;Jad Abou Chaaya ,&nbsp;Ali Mansour","doi":"10.1016/j.iot.2025.101750","DOIUrl":"10.1016/j.iot.2025.101750","url":null,"abstract":"<div><div>This paper investigates the feasibility of using LoRaWAN as the communication protocol for a Spectrum Sensing Provider (SSP) in Cognitive Radio (CR) networks. We evaluate LoRaWAN capability to deliver reliable spectrum detection services by analyzing the impact of key protocol parameters such as duty cycle restrictions, gateway capacity, and network interference on delivering the sensing outcome in Cooperative Spectrum Sensing (CSS) scenarios. Additionally, we propose a novel cost function for selecting CSS groups, optimizing the trade-off between energy consumption and channel availability, along with a greedy scheduling algorithm to enhance sensing timeliness. Numerical analysis shows that our cost function may improve spectral and energy efficiency by 50% compared to classical SNR-based approaches, while the greedy algorithm effectively balances the SSP’s response to service requests. Our findings highlight that despite LoRaWAN constraints, increasing the number of users and detected channels significantly enhances SSP performance, enabling it to meet diverse spectrum sensing demands more efficiently.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101750"},"PeriodicalIF":7.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STREAMLINE: Dynamic and Resource-Efficient Auto-Tuning of Stream Processing Data Pipeline Ensembles","authors":"Stefan Pedratscher ,&nbsp;Zahra Najafabadi Samani ,&nbsp;Juan Aznar Poveda ,&nbsp;Thomas Fahringer ,&nbsp;Marlon Etheredge ,&nbsp;Abolfazl Younesi ,&nbsp;Juan Jose Durillo Barrionuevo ,&nbsp;Peter Thoman","doi":"10.1016/j.iot.2025.101731","DOIUrl":"10.1016/j.iot.2025.101731","url":null,"abstract":"<div><div>With the growing volume of data generated by IoT devices and user-driven services, stream processing has become essential for handling continuous, real-time data. However, fluctuating workloads and the dynamic nature of data streams make it difficult to maintain consistent performance over time, requiring adaptive resource allocation and frequent configuration tuning. Running multiple data stream processing pipelines on shared resources further exacerbates the problem by increasing contention, leading to higher end-to-end latency and reduced performance stability. Most existing approaches focus on tuning individual configuration parameters in isolation and overlook interactions between concurrently running data pipelines. To address these limitations, we present STREAMLINE, a dynamic multi-layer auto-tuning framework designed for stream processing environments. STREAMLINE uses transformers to predict future workloads and an evolutionary algorithm to automatically tune configuration parameters. It also includes a resource-efficient scheduler that efficiently assigns operators to resources across a compute cluster. Our dynamic update mechanism minimizes downtime and preserves state during configuration parameter and scheduling changes. We evaluate STREAMLINE on the Grid’5000 testbed using real-time IoT and streaming benchmarks. Results show that STREAMLINE outperforms state-of-the-art methods, improving throughput, end-to-end latency, and CPU utilization by up to 4<span><math><mo>×</mo></math></span> , 10<span><math><mo>×</mo></math></span> , and 9<span><math><mo>×</mo></math></span> , respectively, while reducing costs by up to 10<span><math><mo>×</mo></math></span> .</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101731"},"PeriodicalIF":7.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Hardware Security Modules into BLE Beacons: Fundamentals and Use in a Secure and Private Geofencing Application","authors":"Miguel Mesa-Simón ,&nbsp;Antonio Escobar-Molero ,&nbsp;Luis Parrilla ,&nbsp;Diego P. Morales ,&nbsp;José Antonio Álvarez-Bermejo ,&nbsp;Francisco J. Romero","doi":"10.1016/j.iot.2025.101762","DOIUrl":"10.1016/j.iot.2025.101762","url":null,"abstract":"<div><div>Bluetooth Low Energy (BLE) is a wireless technology designed for creating personal area networks in low-power applications. In the context of BLE, Beacon devices are widely used to transmit small packets of data with unique identifiers at regular intervals to be detected by surrounding devices. These devices enable a wide range of applications, including indoor navigation, marketing, and asset tracking. However, BLE Beacons suffer from multiple security issues and privacy concerns since the transmissions are unencrypted and do not include authentication mechanisms. While many implementations try to provide security to the Beacons packet, they often rely on external servers, static keys, synchronization for key derivation, or use difficult to maintain and to operate Public Key Infrastructure (PKI). In this work, we propose a solution to enhance Beacon security through the integration of Secure Elements (SEs), establishing a Root of Trust. Our approach is based on the over-the-air activation of the BLE beacons incorporating an authentication mechanism and a key derivation technique to safeguard privacy and data integrity in the communication. We demonstrate that this implementation incurs minimal delays and power consumption compared to traditional Beacons while avoiding the added complexity of solutions based on Certificates and Public Key Infrastructure (PKI). The feasibility of the proposed approach is also illustrated through a secure and privacy-preserving geofencing application. In summary, this method supports a low-power and secure point-to-point communication suitable not only for BLE beacon networks, but also for other IoT scenarios where data privacy is critical.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101762"},"PeriodicalIF":7.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent UAV swarm key agreement survey: Systematic taxonomy, cryptographic automaton and quantum resistance","authors":"Guangyue Kou ,&nbsp;Qing Ye ,&nbsp;Mingwu Zhang ,&nbsp;XuAn Wang ,&nbsp;Wei Fu ,&nbsp;Qian Zhou ,&nbsp;Zhimin Yuan ,&nbsp;Renji Huang ,&nbsp;Xiong Zhang","doi":"10.1016/j.iot.2025.101720","DOIUrl":"10.1016/j.iot.2025.101720","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV) swarms are vital for intelligent applications that require robust key agreement protocols to address dynamic network security challenges. This paper systematically reviews protocols for intelligent UAV swarms, addressing the gaps in existing research. It classifies UAV and embedded protocols based on autonomy and computational capabilities, defines security requirements, and establishes threat models for swarm dynamics. Protocols are categorized into three architectures: end-to-end for ground-UAV communication, planar multi-UAV for decentralized peer-to-peer management, and hybrid hierarchical systems for large swarms. A core contribution is the cryptographic automaton, an adaptive framework for autonomous security management that integrates dynamic key generation, context-aware adjustments, and quantum resistance to handle real-time topology changes and emerging threats. The paper merges structural classifications with threat analysis to identify solution gaps and propose scalable, cryptographic automaton-based, and quantum-resistant directions. It establishes a foundation for secure UAV swarm key agreement, with future research focusing on refining the automaton for practical applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101720"},"PeriodicalIF":7.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study on vulnerability analysis process of generative AI-based digital medical contents","authors":"Hoon Ko ,&nbsp;Libor Mesicek ,&nbsp;Marek R. Ogiela ,&nbsp;Yongyun Cho","doi":"10.1016/j.iot.2025.101759","DOIUrl":"10.1016/j.iot.2025.101759","url":null,"abstract":"<div><div>This paper conducts a sequential analysis of the security vulnerabilities associated with AI-generated digital medical content across ten key areas and presents strategies to enhance the safety and reliability of medical AI systems. The study comprehensively examines aspects such as the quality and integrity of digital content, risks of privacy exposure, model security vulnerabilities, system security, ethical risks, performance stability, regulatory compliance, interoperability, and disaster recovery capabilities. To evaluate the AI system’s vulnerabilities, quantitative metrics such as Data Accuracy (DA), Personal Information Risk (PIR), and Model Robustness (MR) are utilized. The results underscore the importance of strengthening encryption, improving backup systems, and enhancing defenses against adversarial attacks. These findings highlight the critical need for reinforcing security protocols, adhering to ethical standards, and ensuring strict compliance with international regulations. The study offers vital guidelines for developing secure AI systems that can be effectively integrated into medical applications, contributing to the safe and reliable use of generative AI technology in healthcare settings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101759"},"PeriodicalIF":7.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing IoT architectures by using model driven approach and evolution strategy","authors":"Julio D. Arjona,&nbsp;José A. Barriga,&nbsp;Fernando Díaz Cantero,&nbsp;Jose M. Chaves-González,&nbsp;Pedro J. Clemente","doi":"10.1016/j.iot.2025.101740","DOIUrl":"10.1016/j.iot.2025.101740","url":null,"abstract":"<div><div>The Internet of Things (IoT) is rapidly transforming the modern world by connecting billions of devices that generate a continuous flow of data. Its rapid expansion has broadened applications from home automation to nearly every industry. Selecting an appropriate IoT architecture is increasingly challenging due to the complexity of components, interactions, and diverse application requirements. Ensuring that an IoT architecture meets performance, reliability, and resource constraints before deployment is essential for avoiding inefficiencies and bottlenecks. Emulating IoT architectures during the design phase allows architects to test different IoT architectures and assess system performance under various conditions. This helps ensure optimal resource utilization and responsiveness. Several simulation approaches, particularly those based on Model-Driven Development (MDD), have been proposed to model and analyse IoT environments. These methodologies provide high-level abstractions of complex IoT architectures, enabling systematic experimentation and evaluation. This work introduces a novel methodology and tools for simulating and refining IoT architectures using a evolutionary approach. They allow architects to assess and improve performance based on configurable parameters such as latency and CPU usage. Through iterative IoT architecture modifications and testing, the proposed MDD-based approach optimizes system design, ensuring that the final architecture is fine-tuned to deliver the best possible performance for a given application. The proposal is validated by a case study related with a smart parking.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101740"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain and AI-based methods for trust management in IoT: A comprehensive survey","authors":"Giuseppe D’Aniello,&nbsp;Lidia Fotia","doi":"10.1016/j.iot.2025.101755","DOIUrl":"10.1016/j.iot.2025.101755","url":null,"abstract":"<div><div>The rapid expansion of the Internet of Things (IoT) is driving the integration of billions of connected devices across various domains, including healthcare, transportation, and smart urban systems. Although this proliferation offers considerable advantages in terms of functionality and operational efficiency, it also brings to the forefront a range of pressing concerns, particularly in relation to security, reliability, and privacy. These challenges are largely rooted in the decentralized and dynamic architecture of IoT ecosystems. In this context, trust and reputation mechanisms have become increasingly vital for enabling secure and reliable interactions between devices and users. This paper examines recent advances in trust management models tailored to IoT environments, with a focus on approaches leveraging blockchain technologies, machine learning techniques, and edge or fog computing paradigms. We assess the practical implications of these solutions, discussing both their strengths and inherent limitations. Furthermore, we identify key open issues such as scalability, data protection, and interoperability across platforms, and we outline potential research directions to support the development of more robust and adaptable trust frameworks for the evolving IoT landscape.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101755"},"PeriodicalIF":7.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-enhanced facial recognition for IoT based on homomorphic encryption","authors":"Haoming Wang ,&nbsp;Wenhao Liu ,&nbsp;Xu An Wang ,&nbsp;Weiwei Jiang ,&nbsp;Jiasen Liu ,&nbsp;Xiaoyuan Yang ,&nbsp;Wei Zhao ,&nbsp;Kaifa Zheng","doi":"10.1016/j.iot.2025.101757","DOIUrl":"10.1016/j.iot.2025.101757","url":null,"abstract":"<div><div>The combination of face recognition technology and IoT system shows great potential. However, how to ensure the security of personal private information has become an urgent problem. In this paper, a system combining face recognition based on convolutional neural network with homomorphic encryption and edge processing is proposed. Firstly, MTCNN is used to correct the error of photos, then FaceNet model is used to extract the biometric information of users, and finally homomorphic encryption is used to ensure that the face feature information is always in ciphertext state during the whole data transmission process, thus effectively reducing the probability of user information leakage. In scenarios involving multiple faces and multiple targets, the project is upgraded using the FaceNet library and edge computing technology to adapt to multi-face and multi-target detection tasks. The experimental results show that the FaceNet model achieves a detection accuracy of 98.06% on the LFW dataset. Therefore, the system can meet the practical requirements and improve the robustness and accuracy of face recognition to a certain extent.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101757"},"PeriodicalIF":7.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TrustAIoT: A framework for building trustworthy AIoT platforms","authors":"Carlos Mario Braga ,&nbsp;Ángel Suarez-Bárcena ,&nbsp;Manuel A. Serrano ,&nbsp;Eduardo Fernández-Medina","doi":"10.1016/j.iot.2025.101751","DOIUrl":"10.1016/j.iot.2025.101751","url":null,"abstract":"<div><div>In an increasingly connected world, each device is expected to be linked to the internet and, consequently, to other objects with which it can communicate. As the Internet of Things (IoT) expands, so do social, legal, and ethical concerns. Moreover, IoT systems are evolving to process data and provide recommendations based on their findings, a capability that stems from the integration of Artificial Intelligence (AI) and Machine Learning technologies. The convergence of IoT and AI (AIoT), along with the unique aspects of AIoT architectures that differ from non-IoT-enabled AI systems, necessitates a thorough review of specific considerations for building trustworthy AIoT systems. Ensuring trustworthiness in AIoT is crucial due to the increased complexity and potential vulnerabilities introduced by this convergence.</div><div>This article introduces TrustAIoT, a structured framework for the development and long-term governance of trustworthy AIoT platforms. The framework integrates ethical, legal, and technical dimensions, and consists of both a multi-layer guideline and a lifecycle-oriented process tailored to the specific architectural characteristics of AIoT systems.</div><div>Based on a systematic literature review, trustworthiness-related technical, ethical, and legal elements are cross-referenced and contrasted with the operational and architectural needs of AIoT environments, ensuring that all critical aspects are addressed. Challenges in applying these elements across heterogeneous architectures are analyzed, leading to the definition of a guideline for transferring trust principles across the different layers of an AIoT platform, and a process for constructing and maintaining such platforms. A platform is understood as an environment comprising infrastructure, tools, services, processes, and components for developing, deploying, and operating applications.</div><div>The TrustAIoT framework consolidates these artifacts into a cohesive approach that supports trust-oriented decision-making throughout the platform lifecycle, from architectural design to project deployment.</div><div>The proposed guideline and process form a unified framework that ensures high trustworthiness standards, thereby enabling the reliable development of multiple projects and applications within AIoT ecosystems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101751"},"PeriodicalIF":7.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing IoT privacy with artificial intelligence: Recent advances and future directions","authors":"Asimina Tsouplaki ,&nbsp;Carol Fung ,&nbsp;Christos Kalloniatis","doi":"10.1016/j.iot.2025.101752","DOIUrl":"10.1016/j.iot.2025.101752","url":null,"abstract":"<div><div>The proliferation of Internet of Things (IoT) devices has brought tremendous convenience in our daily lives but has also brought significant privacy concerns. In recent years, many solutions have been found in the literature to address these challenges through advanced technologies such as Artificial Intelligence (AI). This paper aims to provide a comprehensive survey of the current landscape of IoT privacy, focusing on the role of AI in enhancing privacy measures. We categorize critical privacy challenges, outline AI strategies to address these challenges, and present AI-driven solutions that have shown real and substantial results in major sectors. We examine various AI techniques, assess their effectiveness, and highlight existing research gaps to inform future researchers. Our main contributions include a taxonomy of AI applications for IoT privacy, an analysis of AI-driven privacy solutions, and a discussion on the ethical implications and compliance requirements. This paper is recommended to researchers, practitioners, and policymakers seeking to develop secure and privacy-aware IoT systems. Unlike previous surveys that analyze thoroughly individual privacy-preserving methods, this study provides a multi layer synthesis of AI techniques tailored to IoT architectures and deployment realities, presenting a taxonomy grounded in both theoretical robustness and implementation feasibility.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101752"},"PeriodicalIF":7.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}