Internet of Things最新文献

{"title":"Efficient and secure secret sharing-based data outsourcing suitable for Internet of Things environments","authors":"Ahmad Akmal Aminuddin Mohd Kamal,&nbsp;Masaya Fujisawa","doi":"10.1016/j.iot.2025.101645","DOIUrl":"10.1016/j.iot.2025.101645","url":null,"abstract":"<div><div>Advancements in the Internet of Things (IoT) environment and cloud computing have created new business opportunities, such as cloud-based IoT data outsourcing, in which data that are collected by IoT devices are outsourced to cloud servers for further processing. However, concerns exist regarding the privacy and security of the collected data. Moreover, IoT devices often operate at low power and possess restricted memory, processing capabilities, and storage capacities. Consequently, a secure data-outsourcing method with minimal computational requirements is essential. Secret sharing schemes are recognized for providing robust security while requiring minimal computational resources. Hence, secret sharing schemes have recently been utilized as alternative methods to address privacy protection concerns. In a perfect <span><math><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></math></span> threshold secret sharing scheme, a secret is converted into <span><math><mi>n</mi></math></span> different shares, thereby requiring a total share size of <span><math><mi>n</mi></math></span> times the secret. A <span><math><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>L</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></math></span> ramp secret sharing scheme provides better storage efficiency, albeit at the cost of realizing only weak security. In this study, we propose a new protocol for a secure and storage-efficient <span><math><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>L</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></math></span> ramp secret sharing scheme that incorporates the idea of encrypting each secret with a random number. By introducing new randomization steps based on the one-time pad encryption approach to randomize the inputs, we prove that even if part of the information can be leaked because of the nature of <span><math><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>L</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></math></span> ramp secret sharing, the actual secret remains secure, provided that the product of the random numbers is not leaked from the unauthorized shares. Furthermore, we performed an experimental evaluation of the distribution phase of the proposed method with C++ using a Raspberry Pi 4 Model B as an IoT device and showed that the proposed method can be executed in significantly less time than most conventional secret haring schemes, particularly when the parameter <span><math><mi>k</mi></math></span> increases.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101645"},"PeriodicalIF":6.0,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241278","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":"An adaptive backoff algorithm for enhanced MAC-layer security and operational efficiency in IoT and cyber–physical systems","authors":"Sofiane Hamrioui ,&nbsp;Redouane Djelouah ,&nbsp;Pascal Lorenz","doi":"10.1016/j.iot.2025.101641","DOIUrl":"10.1016/j.iot.2025.101641","url":null,"abstract":"<div><div>The proliferation of industrial IoT and cyber–physical systems demands Medium Access Control (MAC) protocols that simultaneously address security threats and operational efficiency in resource-constrained environments. Existing solutions frequently fail to provide adequate protection against real-time threats like jamming and denial-of-service (DoS) attacks while maintaining performance. We present the Adaptive MAC-layer Backoff Algorithm (AMBA), a novel protocol that enhances security, efficiency, and resilience through dynamic backoff adaptation based on real-time traffic analysis and physical-layer feedback. AMBA achieves: (1) 20 Mbps peak throughput (15.5 Mbps under jamming; 17 Mbps under DoS), (2) 50% lower latency than JR-MAC, (3) 75% improvement in packet loss resilience, and (4) 20%–30% higher Security Threat Resilience Metric (STRM) scores against diverse attacks. Evaluations demonstrate AMBA’s superiority over existing protocols while meeting the stringent reliability requirements of industrial IoT and vehicular networks. The solution’s lightweight design and scalability make it particularly suitable for next-generation cyber–physical systems where security and performance must coexist.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101641"},"PeriodicalIF":6.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137284","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":"Optimising secure and sustainable smart home configurations","authors":"Daniel Muñoz-Heredia,&nbsp;Ángel Jesús Varela-Vaca,&nbsp;Diana Borrego,&nbsp;María Teresa Gómez-López","doi":"10.1016/j.iot.2025.101637","DOIUrl":"10.1016/j.iot.2025.101637","url":null,"abstract":"<div><div>As the adoption of smart devices accelerates rapidly in smart homes worldwide, the variety of available devices on the market is also diversifying. This creates a challenge for users, who must choose devices that best meet their needs, and for system designers, who must ensure these devices integrate efficiently within a connected ecosystem.</div><div>In response to this challenge, the solution presented in this work provides a metamodel that gathers the smart home features, including attributes related to security, usability, connectivity and sustainability. These features are used to create personalised configurations of smart homes that meet user requirements. This is achieved through the creation of multi-objective optimisation problems focused on improving: security, to ensure network and personal data protection; usability, to facilitate the easy management of the environment; connectivity, to maintain seamless interaction between both existing and future devices; and sustainability, which assesses the environmental impact and energy efficiency of the technological ecosystem. The implementation of the proposal is available and a set of experiments have been developed to evaluate the proposal’s applicability using real devices, being reproducible and replicable.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101637"},"PeriodicalIF":6.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123259","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":"An energy-focused model for batteryless IoT: Vortex wireless power transfer and fog computing in 6 G networks","authors":"Mehdi Hosseinzadeh ,&nbsp;Jawad Tanveer ,&nbsp;Saqib Ali ,&nbsp;Marcia L. Baptista ,&nbsp;Farhad Soleimanian Gharehchopogh ,&nbsp;Shakia Rajabi ,&nbsp;Thantrira Porntaveetus ,&nbsp;Sang-Woong Lee","doi":"10.1016/j.iot.2025.101657","DOIUrl":"10.1016/j.iot.2025.101657","url":null,"abstract":"<div><div>The Internet of Things (IoT) refers to the networked interconnection of devices that collect, exchange, and analyze data to enable intelligent applications. In emerging sixth-generation (6 G) networks, batteryless IoT devices have gained significant attention, as they rely on ambient energy harvesting rather than traditional batteries. This paper presents an energy-focused model for a 6G-enabled batteryless IoT network that integrates Vortex Wireless Power Transfer (WPT) with fog node coordination to manage energy harvesting and computation offloading. WPT exploits electromagnetic resonance to deliver energy wirelessly. Our vortex‐based model applies exponential attenuation, enhancing energy harvesting for batteryless IoT devices. Then system dynamically assigns IoT devices to optimal WPT zones based on coverage and received power, while simultaneously determining whether tasks should be executed locally or offloaded to Mobile Edge Computing (MEC)-enabled fog nodes, based on real-time energy and latency constraints. To solve the result of the NP-hard optimization problem, we develop an Enhanced Adaptive Quantum Binary Particle Swarm Optimization (EAQBPSO) algorithm that effectively balances workload distribution, energy harvesting, and consumption. Simulation results indicate that our approach significantly outperform traditional methods, achieving improvements of up to 71 % in energy efficiency, nearly 87 % in energy harvesting efficiency, and reducing average energy consumption per task by over 40 %.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101657"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134975","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":"Digital Twin-driven federated learning and reinforcement learning-based offloading for energy-efficient distributed intelligence in IoT networks","authors":"Klea Elmazi ,&nbsp;Donald Elmazi ,&nbsp;Jonatan Lerga","doi":"10.1016/j.iot.2025.101640","DOIUrl":"10.1016/j.iot.2025.101640","url":null,"abstract":"<div><div>Improved frameworks for delivering both intelligence and effectiveness under strict constraints on resources are required due to the Internet of Things’ (IoT) devices’ rapid expansion and the resulting increase in sensor-generated data. In response, this research considers a joint learning-offloading optimization approach and presents an improved framework for energy-efficient distributed intelligence in sensor networks. Our method dynamically allocates computational tasks across resource-constrained sensors and more powerful edge servers through incorporating Federated Learning (FL) with adaptive offloading techniques. This allows collaborative model training across IoT devices. We suggest a multi-objective optimization problem that simultaneously maximizes learning accuracy and convergence time and minimizes energy usage with the objective to solve the dual issues of energy consumption and model performance. To create energy-efficient distributed intelligence in IoT sensor networks, our suggested framework combines FL, Digital Twin (DT), and sophisticated Reinforcement Learning (RL)-based decision-making engine. In order to predict short-term system dynamics, the DT uses linear regression and moving averages for predictive analytics based on real-time data from sensor nodes, such as battery levels, CPU loads, and network latencies. A Dueling Double Deep Q-Network (D3QN) agent with Prioritized Experience Replay (PER) and multi-step returns is directed by these predictions and dynamically chooses between offloading and local processing depending on the operating environment. According to experimental data, our method effectively keeps final battery levels over 85% while allowing the offloading to reduce local CPU drain. We compare the proposed framework with two baseline methods. The evaluation results show that the pure local strategy obtains a slightly increased average battery level, about 91%, but never offloads tasks, the naïve offload method maintains a lower average battery level, about 70%, than our RL agent’s converged policy, about 85%.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101640"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123151","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":"3D Printed microstrip antenna for symbiotic communication: WiFi backscatter and bit rate evaluation for IoT","authors":"Muhammed Yusuf Onay ,&nbsp;Burak Dokmetas","doi":"10.1016/j.iot.2025.101643","DOIUrl":"10.1016/j.iot.2025.101643","url":null,"abstract":"<div><div>This work presents the design, experimental validation of a novel 3D-printed microstrip antenna operating at 2.4 GHz for WiFi backscatter communication in IoT applications and its performance evaluation on the communication protocol proposed in the work. The antenna is manufactured using PREPERM ABS material, which is specifically designed for high-frequency RF applications. It meets the requirements of 5G systems by ensuring high efficiency and low power loss in transmission. The antenna, integrated into the symbiotic/interference communication system, realizes low-power data transmission by utilizing existing WiFi signals. The signal power levels of each antenna in the system are tested with experimental measurements performed in a real-world environment. Then, the obtained data is used to calculate the total bit transmission rate of the system for two different scenarios proposed in the communication protocol. The proposed antenna achieves 80% efficiency, offering 10%–15% higher performance than conventional RFID-based designs, with a 5 dB gain improvement. Additionally, theoretical analysis reveals that the bit transmission rate is approximately 1.5 bps/Hz higher than experimental results, demonstrating the impact of real-world constraints on system performance. The results provide a comparative analysis of the relationship between experimental and analytical approaches in optimizing the total bit transmission rate of WiFi backscatter communication systems under different benchmarks. These findings confirm the antenna’s efficiency and enhanced performance for energy-efficient IoT applications. This research clearly demonstrates the potential of customized 3D-printed antennas and their applicability in backscatter systems to advance next-generation communication technologies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101643"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098895","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":"Towards IoT device privacy & data integrity through decentralized storage with blockchain and predicting malicious entities by stacked machine learning","authors":"Zahoor Ali Khan ,&nbsp;Nadeem Javaid ,&nbsp;Arooba Saeed ,&nbsp;Imran Ahmed ,&nbsp;Farrukh Aslam Khan","doi":"10.1016/j.iot.2025.101642","DOIUrl":"10.1016/j.iot.2025.101642","url":null,"abstract":"<div><div>Blockchain technology offers significant advantages in securing the internet of things (IoT) networks. However, IoT devices remain highly vulnerable to security and privacy threats, making them prime targets for malicious activities. This study addresses key challenges in IoT security, including ensuring device authenticity, preserving data integrity through decentralized storage, and enhancing the explainability of predictive models. To tackle these challenges, a novel approach integrating blockchain and machine learning (ML) is proposed. A stacking-based classification model is introduced to differentiate between legitimate and malicious IoT entities. At the base layer, the model leverages the extra trees, multinomial Naive Bayes, and Bernoulli Naive Bayes classifiers, while the logistic regression with cross-validation classifier functions as the meta-model. The preprocessing pipeline includes data normalization and handling of missing values to improve model robustness. To further strengthen security, a local blockchain is implemented on an IoT device manager to register IoT requestors with unique addresses. The Keccak256 hashing algorithm converts these addresses into hashes, which are securely stored on the local blockchain. The actual data is managed using the interplanetary file system, while block validation is performed using a proof-of-stake consensus mechanism. The proposed model classifies IoT devices with superior performance compared to baseline classifiers. Experimental results demonstrate the effectiveness of the stacking model, achieving notable improvements: a 6.90% increase in macro-recall, a 4.49% improvement in the Matthews correlation coefficient and Cohen’s kappa, a 3.33% enhancement in the macro-F1-score, and approximately a 1.02% gain in accuracy, micro-precision, micro-recall, and area under the receiver operating characteristics curve. Additionally, log loss and Hamming loss are reduced by 50%, indicating enhanced reliability and lower error rates. Results of the proposed stacking model are further assessed using the Friedman statistical test and 10-fold cross-validation techniques. To ensure interpretability, Shapley additive explanations and local interpretable model-agnostic explanations are employed, providing insights into model decisions. These findings underscore the effectiveness of the proposed approach in improving IoT security by combining blockchain for decentralized authentication and explainable ML for transparent decision-making.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101642"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195320","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":"Smart-contracts-driven personal carbon credit management in smart cities: A review and future research directions","authors":"Reem S. Alharbi ,&nbsp;Farookh Khadeer Hussain","doi":"10.1016/j.iot.2025.101636","DOIUrl":"10.1016/j.iot.2025.101636","url":null,"abstract":"<div><div>This paper examines personal carbon credits from energy saving in smart cities. Personal carbon credits are carbon credits owned by individuals who reduce their household greenhouse gas emissions by a real and verifiable value. This paper presents a systematic literature review (SLR) to examine how individuals can generate carbon credits as a result of energy saving measures in smart cities. The SLR includes research, reviews and conference papers from 2013–2024 from the IEEE, Springer, ACM and ScienceDirect databases. A total of 14 articles were selected for this SLR based on the titles, keywords and abstracts by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We divide the studies into two groups. The first group pertains to blockchain and the Internet of Things (IoT) for carbon trading while the other group pertains to building energy for carbon credits. A comparative analysis was undertaken to understand how individuals can generate carbon credits by reducing their energy consumption. The results of the SLR show there is a lack of studies on how individuals can obtain carbon credits based on their energy consumption behavior in smart cities. Most studies are concerned about carbon emissions trading and how to reduce carbon dioxide emissions in the building sector. Finally, this paper highlights the crucial need for future research on personal carbon credits systems to enhance their scalability, effectiveness, and efficiency.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101636"},"PeriodicalIF":6.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134976","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":"Dynamic spectrum sharing in heterogeneous wireless networks using deep reinforcement learning","authors":"Sulaimon Oyeniyi Adebayo ,&nbsp;Abdulaziz Barnawi ,&nbsp;Tarek Sheltami ,&nbsp;Muhammad Felemban","doi":"10.1016/j.iot.2025.101635","DOIUrl":"10.1016/j.iot.2025.101635","url":null,"abstract":"<div><div>The rapid expansion of wireless networks demands efficient spectrum allocation. Dynamic Spectrum Sharing (DSS) is a technology that allows multiple wireless networks to share the same frequency spectrum dynamically. It is an effective technique for optimizing spectrum use, particularly in heterogeneous environments where multiple wireless technologies with diverse spectrum access requirements coexist, often leading to interference challenges and increased spectrum competition. This research proposes an enhanced DSS technique based on Deep Reinforcement Learning (DRL). The proposed method enables an effective sharing of the available spectrum between two access technologies, namely Long Term Evolution (LTE) and Narrowband IoT (NB-IoT). The study optimizes throughput through DRL methods, including Deep Q-Networks (DQN), conducting experiments in three phases: LTE-DRL coexistence, NB-IoT-DRL coexistence, and LTE-NB-IoT coexistence. Results show that deep learning enhances the LTE-DRL system’s convergence rate and throughput, achieving over 85% throughput with convergence times as low as 24 milliseconds (ms). The study highlights the trade-offs between parameters such as probabilities (arrival, successful transmission, and retransmission), packet expiry duration, learning rate, discount factor, fairness index, and the neural network architecture as well as the parameters’ impact on the overall system throughput. NB-IoT coexistence with DRL shows similar results with a slight decrement in throughput and negligibly longer convergence rate, while the coexistence of LTE and NB-IoT results in throughput of around 70% for each of the LTE and NB-IoT systems due to increased spectrum competition and increased complexity of the operating environment. This work offers insights into optimizing spectrum sharing using DRL and underscores the balance between various parameters for efficient spectrum management.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101635"},"PeriodicalIF":6.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107893","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":"Time matters: Empirical insights into the limits and challenges of temporal generalization in CSI-based Wi-Fi sensing","authors":"Andrea Brunello ,&nbsp;Angelo Montanari ,&nbsp;Raúl Montoliu ,&nbsp;Adriano Moreira ,&nbsp;Nicola Saccomanno ,&nbsp;Emilio Sansano-Sansano ,&nbsp;Joaquín Torres-Sospedra","doi":"10.1016/j.iot.2025.101634","DOIUrl":"10.1016/j.iot.2025.101634","url":null,"abstract":"<div><div>Wi-Fi is ubiquitous, and Channel State Information (CSI)-based sensing has often emerged as superior for tasks like human activity recognition (HAR) and indoor positioning (IP) The foundational premise is that similar scenarios exhibit similar CSI patterns. However, establishing such similarities is challenging due to signal attenuation and multipath effects caused by static and dynamic objects, that create complex interaction phenomena. Although acknowledged in literature, a comprehensive study of how these variables affect CSI patterns across scenarios, particularly their long-term impact on real-world applications, is still missing. In fact, many recent works focus on laboratory settings disregarding temporal generalization when testing their solutions. Here, we present a systematic study of the reliability of CSI-based sensing, consolidating key challenges and insights previously scattered in the literature. We provide a clear and independent perspective about the need of considering temporal aspects when developing CSI-based sensing approaches, particularly for real-world applications. To achieve that, we consider two tasks, IP and HAR, combining theoretical modeling with experiments using state-of-the-art methods. We show how tasks dependent on reflections from static objects, like IP, are severely impacted by disturbances that accumulate over time , also in the absence of physical modifications of the environment. In contrast, those relying on reflections from dynamic objects, like HAR, face fewer challenges. Our findings, supported by novel real-world datasets for CSI fingerprint-based IP and CSI stability analysis over time, suggest that future research must consider time as a crucial factor both in the development and test of approaches.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101634"},"PeriodicalIF":6.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154538","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}