| 1. |
- Arghavani, Abbas, et al.
(författare)
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Power-Adaptive Communication With Channel-Aware Transmission Scheduling in WBANs
- 2024
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:9, s. 16087-16102
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Tidskriftsartikel (refereegranskat)abstract
- Radio links in wireless body area networks (WBANs) are highly subject to short and long-term attenuation due to the unstable network topology and frequent body blockage. This instability makes it challenging to achieve reliable and energy-efficient communication, but on the other hand, provides a great potential for the sending nodes to dynamically schedule the transmissions at the time with the best expected channel quality. Motivated by this, we propose improved Gilbert-Elliott Markov chain model (IGE), a memory-efficient Markov chain model to monitor channel fluctuations and provide a long-term channel prediction. We then design adaptive transmission power selection (ATPS), a deadline-constrained channel scheduling scheme that enables a sending node to buffer the packets when the channel is bad and schedule them to be transmitted when the channel is expected to be good within a deadline. ATPS can self-learn the pattern of channel changes without imposing a significant computation or memory overhead on the sending node. We evaluate the performance of ATPS through experiments using TelosB motes under different scenarios with different body postures and packet rates. We further compare ATPS with several state-of-the-art schemes, including the optimal scheduling policy, in which the optimal transmission time for each packet is calculated based on the collected received signal strength indicator (RSSI) samples in an off-line manner. The experimental results reveal that ATPS performs almost as efficiently as the optimal scheme in high-date-rate scenarios and has a similar trend on power level usage.
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| 2. |
- Borah, Jintu, et al.
(författare)
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AiCareBreath : IoT Enabled Location Invariant Novel Unified Model for Predicting Air Pollutants to Avoid Related Respiratory Disease
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:8, s. 14625-14633
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a Light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a Random Forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast dataset of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive dataset containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on RNNs and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.
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| 3. |
- Chen, Haizhou, et al.
(författare)
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Measurement Capability Evaluation of Acoustic Emission Sensors in IIoT System for PHM
- 2024
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- In the realm of Industry 4.0, Industrial Internet of Things (IIoT) is pivotal for advancing Prognostics and Health Management (PHM) through real-time monitoring of asset conditions. The efficacy of these IIoT systems heavily relies on the precision and reliability of Acoustic Emission (AE) sensor data. Recognizing the critical dependence of IIoT system functionality on AE sensor capability, this study proposes a novel, systematic framework tailored for PHM applications. Our approach expands the application of the Gage Repeatability and Reproducibility (Gage R&R) technique, focusing on enhancing the reliability of IIoT-AE systems. In experimental study, AE sensors are deployed to collect data across various operational contexts, including different fault types, measurement positions, operators, speeds, and trial counts. This comprehensive exploration reveals how different contextual factors impact AE sensor capability, thereby facilitating the strategic selection of contexts for precise data acquisition. Additionally, we introduce an innovative three-region graph comprising key metrics, namely Percentage of Repeatability & Reproducibility (PRR), Precision-to-Tolerance Ratio (PTR), and Signal-to-Noise Ratio (SNR), to provide a clear and intuitive visualization of AE sensor capability and acceptability based on well-defined criteria. Our findings lay the groundwork for ensuring the accuracy and reliability in IIoT systems for PHM, while also provides invaluable guidance for contextual design and practical enhancement of AE sensor, with broader implications for real-time sensor capability evaluations in IoT systems. This work marks a significant step forward in ensuring the reliability of IIoT deployments in PHM, ultimately contributing to the advancement of sensor technology in Industry 4.0 applications.
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| 4. |
- Ghayvat, Hemant, et al.
(författare)
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Healthcare-CT : SoLiD PoD and Blockchain-Enabled Cyber Twin Approach for Healthcare 5.0 Ecosystems
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:4, s. 6119-6130
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Tidskriftsartikel (refereegranskat)abstract
- The healthcare personals often use stored healthcare data to make crucial decisions, assess risk, and care for patients. The extraction of the required information from the saved healthcare data needs a healthcare ecosystem that can guarantee reliable data delivery. The reliability of cyber-physical data needs to be cross-examined using several sources of data of overlapping nature. The cross-examined data can be saved on blockchain and SOLID POD (SP) to preserve its reliability and privacy. Once the reliable healthcare data is stored on the blockchain and SP, the patients’ medical history can be delivered to data-operated systems to monitor, diagnose, and detect augmented healthcare anomalies. Cyber twins (CT) combine the specific cyber-physical objects with digital tools portraying their actual settings. The creation of a live model for the delivery of healthcare services presents a novel opportunity in patient care comprising better evaluation of risk and assessment without hampering the activities of daily living. The introduction of blockchain technology can improve the notion of CTs by certifying transparency, decentralized data storage, data irreversibility, and person-to-person industrial communication. The storage and exchange of CT data in the healthcare ecosystem depend on disseminated ledgers and decentralized databases for storing and processing data to avoid single point reliance. The present study develops an owner-centric decentralized sharing technique to fulfil the decentralized distribution of CT data.
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| 5. |
- Hossain, Mohammad Istiak, 1987-, et al.
(författare)
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Techno-Economic Framework for IoT Service Platform: : A Cost-Structure Aspects of IoT Service Provisioning
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE Communications Society. - 2327-4662.
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Tidskriftsartikel (refereegranskat)abstract
- A plethora of Internet of Things (IoT) platforms are available in the market today. Most of the IoT platforms are used mainly for service prototyping. Cost-efficient service scalability on any platform is still an unresolved concern that, so far, has been addressed qualitatively. A quantitative method for IoT platform economics is missing in the literature. In this paper, we propose a generic framework to address this gap. Our proposed framework covers the dimensioning of the platform's software and hardware to envisage the design, deployment, and operation cost of platform services. Then, we use the framework to perform a quantitative study of platform rollout in three platform business contexts. Our analysis shows the applicability of different deployment and platform integration choices. Our results suggest that storage and energy are the main cost drivers for platforms' hardware scalability, where the main cost driver is the intensity of the sensors' message transmission rate. Additionally, our use-case based study suggests that platform as a service (PaaS) is only beneficial for actors who have limited scale or niche market need.
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| 6. |
- Li, Huafu, et al.
(författare)
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Performance Analysis and Transmission Block Size Optimization for Massive MIMO Vehicular Network with Spatially and Temporally Correlated Channels
- 2024
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 11:5, s. 8989-9003
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Tidskriftsartikel (refereegranskat)abstract
- We investigate the effect of spatially and temporally correlated channels on the transmission performance of multi-cell multi-user massive multiple-input multiple-output (MIMO) vehicular networks in generic non-isotropic scattering environments. A new channel model is established to evaluate the harmfulness of the non-isotropic-scattered angle-of-departure/angle-of-arrival (AoD/AoA) spread and the high mobility of users on the uplink transmission. We derive the expressions of achievable spectral efficiency (SE), taking into account the effects of line-of-sight propagation, channel aging, and pilot contamination. Specifically, two novel receive combining schemes, namely the aging-aware maximum ratio combining and the aging-aware minimum mean square error combining, are presented to mitigate the SE decline caused by outdated channel state information. A low-complexity pilot assignment algorithm is proposed to suppress pilot contamination. We find that the quasi-static assumption of the channel may be unsafe for the system design of the vehicular networks even within a single transmission block period lasting from hundreds of microseconds to a few milliseconds. We observe that there exists an optimal block size Copt that maximizes area spectral efficiency. Especially, Copt can be expressed as a function of movement speed, AoD spread, and AoA spread. Numerical results are presented to validate the efficacy of the proposed schemes and highlight the importance of correct performance evaluation for practical massive MIMO system designs.
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| 7. |
- Qu, Zhiguo, et al.
(författare)
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QB-IMD : A secure medical data processing system with privacy protection based on quantum blockchain for IoMT
- 2024
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Ingår i: IEEE Internet of Things Journal. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:1, s. 40-49
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Tidskriftsartikel (refereegranskat)abstract
- Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). Blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. Blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This paper innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy. © IEEE
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| 8. |
- Rauniyar, Ashish, et al.
(författare)
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Federated Learning for Medical Applications : A Taxonomy, Current Trends, Challenges, and Future Research Directions
- 2024
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:5, s. 7374-7398
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.
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| 9. |
- Sardar, Alamgir, et al.
(författare)
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Enhanced biometric template protection schemes for securing face recognition in IoT environment
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662.
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Tidskriftsartikel (refereegranskat)abstract
- With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.
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| 10. |
- Shahid, Zahraa Khais, 1980-, et al.
(författare)
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Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes
- 2024
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:3, s. 4414-4429
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Tidskriftsartikel (refereegranskat)abstract
- Sleep is an essential activity that affects an individual’s health and ability to perform Activities of Daily Living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and non-intrusive environments. This paper presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using Multi-Armed Bandit methods to (i) learn normal sleep patterns, (ii) evaluate sleep quality, and (iii) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson Sampling, Random Selection, and Upper Confidence Bound MAB methods. Thompson Sampling outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 hours with 85% sleep efficiency and up to 3 awakenings per night.
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| 11. |
- Singh, Munesh, et al.
(författare)
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An intelligent IoT-based data analytics for freshwater recirculating aquaculture system
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:3, s. 4206-4217
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Tidskriftsartikel (refereegranskat)abstract
- Smart farming is essential for a nation whose economy largely depends on agro products. In the last few years, rapid urbanization and deforestation have impacted farmers. Due to the lack of rainwater harvesting and changing weather patterns, many crop failure cases have been registered in the last few years. To prevent loss of annual crop production, many researchers propose the technology-driven smart farming method. Smart farming is a technology-driven control environment for monitoring and maintaining the crop. Smart farming increases crop production and provides an alternative source of income to small farmers. To promote smart farming in India, the government initiated many pilot projects for integrated aquaculture farming. However, the lack of technological intervention and skill-oriented process makes it difficult for most farmers to succeed in this business. In this paper, we have proposed an intelligent IoT-based freshwater recirculating aquaculture system. The proposed system has integrated sensors and actuators. The sensor system monitors the water parameters, and actuators maintain the aquaculture environment. An intelligent data analytics algorithm played a significant role in monitoring and maintaining the freshwater aquaculture environment. The analytics derived the relationship between the water parameters and identified the relative change. From the experimental evaluation, we have identified that the M5 model tree algorithm has the highest accuracy for monitoring the relative change in water parameters.
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| 12. |
- Zhang, Liangwei, et al.
(författare)
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Wave-ConvNeXt : An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform
- 2024
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- The burgeoning field of the Industrial Internet of Things (IIoT) necessitates advanced fault diagnosis methods capable of navigating the dual challenges of high predictive accuracy and the constraints of edge computing environments. Our study introduces Wave-ConvNeXt, a novel fault diagnosis model that seamlessly integrates the state-of-the-art ConvNeXt architecture with Wavelet Transform. This innovative model stands out for its lightweight design yet delivers exceptional accuracy in fault diagnosis. In Wave-ConvNeXt, we re-engineer the ConvNeXt model for IIoT applications by adopting onedimensional convolution, tailored for processing high-frequency, non-periodic inputs. This adaptation is complemented by replacing the traditional “patchify” layer with a Wavelet transform layer, which simplifies input signals into sub-signals, thereby easing learning complexities and diminishing the dependence on elaborate deep architectures. Further enhancing this model, we incorporate a squeeze-and-excitation module, enriching its ability to prioritize channel-wise feature relevance, akin to self-attention mechanisms. This integration is rigorously validated through an ablation study. Wave-ConvNeXt epitomizes a holistic approach, enabling an end-to-end optimization of feature learning and fault classification. Our empirical analysis on two real-world IIoT datasets demonstrates Wave-ConvNeXt’s superiority over existing models. It not only elevates prediction accuracy but also significantly curtails computational complexity. Additionally, our exploration into the impact of various mother wavelets reveals the effectiveness of using wavelet basis functions with smaller support, bolstering diagnostic precision. The source code of Wave-ConvNeXt is available at https://github.com/leviszhang/waveConvNeXt.
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| 13. |
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| 14. |
- Zheng, Zhejian, et al.
(författare)
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Capacity of Vehicular Networks in Mixed Traffic With CAVs and Human-Driven Vehicles
- 2024
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 11:10, s. 17852-17865
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Tidskriftsartikel (refereegranskat)abstract
- Connected and Automated Vehicles (CAVs) are characterized by diverse communication attributes, embodying the trajectory of future automotive progress. Meanwhile, the transportation system will be in a mixed stage of CAVs and Human-Driven Vehicles (HDVs) for a long time. The study of communication capacity and strategies for mixed traffic systems is of great significance for the popularization of CAVs and the deployment of communication infrastructures. However, current research mainly focuses on the communication capacity analysis in the scenario with full penetration of CAVs, while the influence caused by HDVs on Vehicle-to-Vehicle (V2V) communications and the capacity analysis of connected vehicles in mixed traffic systems need further understanding. To address this issue, this paper considers the shadow fading caused by HDVs on wireless communication links and analyzes the communication capacity in mixed traffic systems. Specifically, we first synthesize the V2V and Vehicle-to-Infrastructure (V2I) communication modes to propose an analytical framework for vehicular network communication capacity in mixed traffic. Then, a predictive communication strategy is also provided that caches the required content at infrastructure in advance according to predicted vehicle trajectories to improve the capacity of vehicular networks in mixed traffic. Furthermore, the derived capacity analysis theorems reveal the communication capacity of mixed traffic is closely related to the CAV penetration rate, the vehicle arrival rate, and the infrastructure deployment interval. Simulation results prove the effectiveness of the proposed framework, and the proposed predictive communication strategy can increase the mixed traffic communication capacity compared to existing communication strategies. The theoretical results herein can guide the implementation of vehicular network applications and the design of communication strategies in mixed traffic systems.
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