Internet of Things最新文献

{"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}

{"title":"Evaluating and enhancing intrusion detection systems in IoMT: The importance of domain-specific datasets","authors":"Jordi Doménech ,&nbsp;Olga León ,&nbsp;Muhammad Shuaib Siddiqui ,&nbsp;Josep Pegueroles","doi":"10.1016/j.iot.2025.101631","DOIUrl":"10.1016/j.iot.2025.101631","url":null,"abstract":"<div><div>The emergence of the Internet of Medical Things (IoMT) is revolutionizing healthcare delivery, but also introducing critical challenges to cybersecurity and patient safety. Intrusion Detection Systems (IDSs) enhanced by Machine Learning (ML) have emerged as a powerful solution to identify cyberattacks in these environments. However, existing studies often rely on general IoT datasets, potentially limiting their applicability in IoMT-specific scenarios. This study addresses these limitations by comparing the performance of ML models trained on a general IoT dataset (CICIoT2023) and an IoMT-specific dataset (CICIoMT2024) to demonstrate the importance of domain-specific data. Our findings reveal substantial drops of up to 66.87% in the F1-score when models trained on one dataset are tested on the other. Furthermore, the study critiques key dataset design choices in CICIoMT2024, and proposes baseline optimization techniques including uniform windowing, proper train-validation-test splits, adjustments in temporal dependencies for time series data, and improved dataset balancing. By applying these techniques, we observe significant improvements in IDS performance in comparison to other approaches, with scores of 0.9985 in model accuracy. The findings show the necessity of using IoMT-specific datasets and carefully designed preprocessing techniques to build robust IDSs tailored to the unique demands of medical IoT environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101631"},"PeriodicalIF":6.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069632","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 the performance of Industrial IoT LoRaWAN-enabled federated learning frameworks: A case study","authors":"Oscar Torres Sanchez ,&nbsp;Guilherme Borges ,&nbsp;Duarte Raposo ,&nbsp;André Rodrigues ,&nbsp;Fernando Boavida ,&nbsp;Jorge Sá Silva","doi":"10.1016/j.iot.2025.101632","DOIUrl":"10.1016/j.iot.2025.101632","url":null,"abstract":"<div><div>The ongoing development of Industrial Internet of Things (IIoT) smart systems is transforming industrial maintenance by improving operational efficiency. In this context, anomaly detection within IIoT architectures is crucial for early issue identification in industrial machinery. However, many systems generate vast sensor data while operating in environments with poor accessibility and network coverage, making centralized training impractical. Federated learning (FL) offers a solution by enabling distributed training on local devices, reducing bandwidth usage by transmitting models instead of raw data, and enhancing privacy. Despite these advantages, applying FL in IIoT resource-constrained devices — characterized by limited storage, processing capacity, and high-frequency heterogeneous data — remains challenging. This study showcases FL framework performance enhancement in LoRaWAN-enabled IIoT environments through optimized local machine data management. The improvements explore three key approaches: (1) techniques to manage high-variability, high frequency data from multiple sources via LoRaWAN-enabled prototypes, (2) an adaptive optimization approach addressing industrial machinery’s sensory diversity, and (3) strategies to reduce false alarms by refining the management system to categorize risk levels based on proximity to anomaly detection thresholds. The enhanced framework achieves an F1-score of 97%, TPR of 96%, and TNR of 80%, with the positive class representing normal conditions and the negative class indicating anomalies. Moreover, the false alarm reduction strategy decreases false positives by at least 72%, preventing values near the threshold from being misclassified as high risk anomalies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101632"},"PeriodicalIF":6.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089691","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":"Bioinspired small language models in edge systems for bee colony monitoring and control","authors":"Juan M. Nún̄ez V. ,&nbsp;Diana M. Giraldo ,&nbsp;Sebastián Gómez Segura ,&nbsp;Juan M. Corchado ,&nbsp;Fernando De la Prieta","doi":"10.1016/j.iot.2025.101633","DOIUrl":"10.1016/j.iot.2025.101633","url":null,"abstract":"<div><div>This paper proposes a hybrid IoT architecture based on Generative Artificial Intelligence (Gen-AIoT) for the intelligent monitoring and control of beehives, designed with processing capabilities both at the edge and in the cloud, thus adapting to environments with or without internet connectivity. Through an IoT sensor network, the system collects critical data on environmental parameters and hive conditions, such as temperature, humidity, wind speed, and hive weight, processing them locally at the edge or centrally in the cloud. The architecture incorporates a recommendation system that uses a small language model (SLM) to generate real-time alerts based on data provided by the IoT sensors. This system implements two distinct SLM models, Phi-3.5 and Tinyllama, enabling hardware performance measurement and optimizing efficiency for edge processing. To establish optimal environmental ranges, the recommendation system uses bio-inspired algorithms, such as ant colony optimization, genetic algorithms, and bee swarm algorithms. Additionally, LSTM neural networks are included to predict honey production and plan hive placement based on climate and weight projections, allowing for precise and personalized adjustments. This dual processing capability (edge and cloud) reduces the need for human intervention, optimizes hive inspection times, and minimizes false positives in monitoring, making it especially beneficial for large-scale beekeeping, where weekly inspection times can exceed 50 h. With this architecture, inspection time is reduced by 80%, significantly improving efficiency in hive management and promoting sustainable practices for bee conservation through intelligent agriculture.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101633"},"PeriodicalIF":6.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942424","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":"Memory-efficient and robust detection of Mirai botnet for future 6G-enabled IoT networks","authors":"Zainab Alwaisi","doi":"10.1016/j.iot.2025.101621","DOIUrl":"10.1016/j.iot.2025.101621","url":null,"abstract":"<div><div>The rise of 6G-enabled IoT networks has introduced significant challenges in securing resource-constrained devices against high-memory and energy-intensive cyber threats, such as the Mirai botnet. Due to their computational and memory overhead, existing Intrusion Detection Systems (IDS) and deep learning-based security mechanisms are often impractical for constrained IoT environments. This study proposes a TinyML-based real-time anomaly detection framework to classify and detect four distinct Mirai botnet attack types: Scan, UDP flooding, TCP flooding, and ACK flooding while analysing their impact on IoT device memory consumption and security.</div><div>To address the trade-off between detection accuracy, memory efficiency, and inference time, Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN) classifiers optimized for TinyML deployment are implemented and compared. Experimental results demonstrate that KNN achieves detection accuracy above 99%, while maintaining low memory usage, making it the most suitable choice for real-time security in constrained IoT environments. Conversely, NB and RF offer superior inference speed with lower computational overhead, presenting a trade-off between detection latency and resource efficiency. Additionally, analysis reveals that Mirai botnet-induced memory consumption leads to increased fragmentation, excessive RAM usage, and higher energy consumption, highlighting the need for adaptive security mechanisms. This framework provides a lightweight, memory-efficient solution for enhancing security in 6G-enabled IoT ecosystems, with potential applications in smart cities, smart homes, and Industry 4.0. By integrating memory-aware ML models, this work contributes critical insights into developing scalable cybersecurity frameworks to ensure resilience against evolving cyber threats.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101621"},"PeriodicalIF":6.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089690","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":"Intent-based approaches for industry 4.0 applications: A systematic mapping study","authors":"Kaoutar Sadouki,&nbsp;Elena Kornyshova","doi":"10.1016/j.iot.2025.101629","DOIUrl":"10.1016/j.iot.2025.101629","url":null,"abstract":"<div><div>In the context of Industry 4.0, Intent-Based Approaches capture high-level industrial objectives, transforming them into executable tasks that align with digital workflows. An intent is defined as a desired outcome or business objective. Despite the growing importance of intent-based approaches, there is a lack of comprehensive understanding of their application to Industry 4.0. To address this, we conducted a systematic mapping study using a structured framework to examine existing intent-based approaches in the literature. Our study provides a comprehensive overview of current intent-based research in Industry 4.0 and reveals a growing interest in this field, especially in manufacturing, robotics, and networking. The study highlights the variety of intent structures, types, usage goals, and methods used. Intent-based approaches would bridge the gap between industrial goals and I4.0 components configuration, which play a key role in the digital transformation of smart industries. Our structured analysis framework and results provide essential understanding serving as a foundation for advancing intent-based approaches tailored to the needs of Industry 4.0 components.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101629"},"PeriodicalIF":6.0,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942425","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}