Continuous Data Streams Research Articles

Graph LRP and Domain Adversarial Neural Networks: An Approach to evaluate the performance and Design an Iterative method for Lumpy skin disease prediction

The rising incidence of Lumpy Skin Disease in livestock is a threat to animal health and agricultural economies worldwide. Traditional approaches towards the prediction of this disease often fall short of expectations in explain ability, cross-domain adaptability, and real-time responsiveness for effective intervention. The authors mentioned the limitations by introducing a comprehensive machine learning framework LSD prediction. Specifically, graph neural networks combined with layer-wise relevance propagation, Graph LRP, to make the model more interpretable and transparent about the model's decision process. Graph LRP assigns a relevance score for input features. DANN generalizes it better and reduces labeled data requirements. Afterwards, to ensure adaptability in real-time, TinyML-based approach is followed by using the lightweight MobileNetV3 with GCNs for edge device deployment. This will enable low latency and efficient predictions to be made using continuous IoT sensor data streams and satellite images. Our proposed model improves the aspect of interpretability by 85% in relevance precision and cross-domain accuracy up to 95%, real-time inference performance at a 70-80% reduction in inference time. These make for a robust, scalable solution for the early detection and intervention of diseases, hence conferring benefits of magnitude in LSD management and control across different environments.

Exploring the Effectiveness of Continuous Glucose Monitoring in Improving Quality of Life and Reducing Hypoglycemic Episodes in Children with Type 1 Diabetes: A Narrative Review

Type 1 diabetes mellitus (T1DM) poses unique challenges in pediatric populations, requiring meticulous glycemic management to mitigate risks such as hypoglycemia and long-term complications. Continuous glucose monitoring (CGM) has emerged as a transformative tool, providing real-time data to support proactive diabetes management. This narrative review evaluated the effectiveness of CGM in improving quality of life (QoL) and reducing hypoglycemic episodes among children with T1DM. A comprehensive literature review methodology was employed to synthesize evidence on CGM’s impact, challenges, and future potential. CGM enhances glycemic control by providing continuous data streams and predictive alarms, addressing the limitations of traditional self-monitoring methods. Psychosocial benefits include reduced anxiety, greater independence, and improved communication between families and healthcare providers. However, barriers such as cost, adherence, and accessibility impede its widespread adoption. Emerging technologies, including artificial intelligence integration and advanced hybrid closed-loop systems, promise to overcome these challenges, further enhancing CGM’s usability and accuracy. Future research should explore CGM’s long-term outcomes, including its impact on psychological well-being and academic performance, while prioritizing equitable access. Addressing these factors will enable CGM to fulfill its potential as a cornerstone of pediatric diabetes management, significantly improving the lives of children with T1DM and their families. Keywords: Continuous Glucose Monitoring (CGM), Type 1 Diabetes Mellitus (T1DM), Pediatric Diabetes Management, Quality of Life (QoL), Hypoglycemic Episodes.

Diversity-Representativeness Replay and Knowledge Alignment for Lifelong Vehicle Re-identification

Lifelong vehicle re-identification (LVReID) aims to match a target vehicle across multiple cameras, considering non-stationary and continuous data streams, which fits the needs of the practical application better than traditional vehicle re-identification. Nonetheless, this area has received relatively little attention. Recently, methods for lifelong person re-identification (LPReID) have been emerging, with replay-based methods achieving the best results by storing a small number of instances from previous tasks for retraining, thus effectively reducing catastrophic forgetting. However, these methods cannot be directly applied to LVReID because they fail to simultaneously consider the diversity and representativeness of replayed data, resulting in biases between the subset stored in the memory buffer and the original data. They randomly sample classes, which may not adequately represent the distribution of the original data. Additionally, these methods fail to consider the rich variation in instances of the same vehicle class due to factors such as vehicle orientation and lighting conditions. Therefore, preserving more informative classes and instances for replay helps maintain information from previous tasks and may mitigate the model’s forgetting of old knowledge. In view of this, we propose a novel Diversity-Representativeness Dual-Stage Sampling Replay (DDSR) strategy for LVReID that constructs an effective memory buffer through two stages, i.e. , Cluster-Centric Class Selection and Diverse Instance Mining. Specifically, we first perform class-level sampling based on density in the clustered class-centered feature space and then further mine the diverse, high-quality instances within the selected classes. In addition, we introduce Maximum Mean Discrepancy loss to align the feature distribution between replay data and the new arrivals and apply L2 regularisation in the parameter space to facilitate knowledge transfer, thus enhancing the model’s generalization ability to new tasks. Extensive experiments demonstrate effective improvements of our method compared to current state-of-the-art lifelong ReID methods on the VeRi-776, VehicleID, and VERI-Wild datasets.

Online Multikernel Learning Method via Online Biconvex Optimization.

Random feature-based online multikernel learning (RF-OMKL) is a promising low-complexity framework for machine learning optimization from continuous streaming data. Nonetheless, it is still an open problem to find an efficient algorithm with an analytical performance guarantee due to the challenge of an underlying online biconvex optimization (OBO). The state-of-the-art method [named expert-based online multikernel learning (EoKle)] tackled this problem approximately with the lens of expert-based online learning, in which multiple kernels (or experts) optimize their own kernel functions separately and the best sole one is determined via Hedge algorithm. It is asymptotically optimal as to the best sole kernel function in hindsight. We propose collaborative expert-based online multikernel learning (CoKle) by devising a collaborative Hedge (CoHedge) algorithm, in which kernel functions separately optimized as in EoKle are combined in an asymptotically optimal way. It is proved that CoKle is asymptotically optimal as to the best combination of each optimal kernel function in hindsight. Remarkably, this is the first method with a theoretical performance guarantee for expert-based RF-OMKL. Despite its effectiveness, CoKle is inherently suboptimal due to the individual optimization of kernel functions. We address this by presenting an OBO-based method (named BoKle) and partially prove its asymptotic optimality for RF-OMKL. Thus, BoKle can outperform the suboptimal expert-based methods such as CoKle and EoKle. Finally, we demonstrate the superiority of BoKle via experiments with real datasets.

Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines

Abstract Structural health monitoring in floating offshore wind turbines’ mooring lines is vital for detecting early faults and preventing disruptions. Currently, sporadic and expensive monitoring is conducted via remote-operating vehicles. Efficient, automated, economical monitoring methods based on a continuous stream of data acquired via multiple measurement points in the wind turbine, are required. Such methods based on vibration signals have been investigated limitedly for steel chains. Recently, faulty synthetic mooring lines of a simulated 10MW semi-submersible wind turbine have been detected under varying environmental conditions and via the Functional Model Based Method (FMBM) equipped with functional models. The uncertainty due to the conditions’ stochasticity has been addressed by training the models using ten signal realizations per condition (wind) under the healthy wind turbine and from each of two measurement points. The current study presents a preliminary sensitivity analysis of the FMBM’s capability in detecting the simulated wind turbine’s faulty mooring lines when the functional models are trained using one signal realization per varying condition (wind) from each measurement point. The method’s effectiveness is evaluated with acceleration signals from 11 healthy/ 66 faulty (reduced stifness in one mooring line) cases. The FMBM detects all cases even when trained using one signal realization.

Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation.

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries.

Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model

Data assimilation-based real-time forecasting is widely used in meteorological and hydrological applications, where continuous data streams are employed to update forecasts and maintain accuracy. However, the reliability of the data source can be compromised due to sensor and communication failures or physical or cyber-attacks, and the impact of data stream failures on the accuracy of the forecasting system is not well understood. This study aims to systematically investigate the process of data stream failure and recovery for the first time. To achieve this, data gaps with varying lengths and timings are introduced to EnKF-based data assimilation system on the Lorenz model operating in both chaotic and periodic modes. Results show that the forecasting error grows exponentially in the chaotic mode but was limited in the periodic mode from the start of the data gap. For chaotic mode, the recovery of the system depends on the length of the data gap if the model error is not saturated; after saturation, the timing of the data stream recovery is important. Moreover, even long after restarting the data assimilation in the chaotic mode, the forecasting system cannot fully restore the original accuracy, while the periodic mode is generally resilient to disruption. This research introduces new metrics for quantifying system resilience and provides crucial insights into the long-term implications of data gaps, advancing our understanding of forecasting system behavior and reliability.

Protecting Infinite Data Streams from Wearable Devices with Local Differential Privacy Techniques

The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting and securely sharing wearable device data has become a critical concern. This paper proposes a local differential privacy-preserving algorithm designed for continuous data streams generated by wearable devices. Initially, the data stream is sampled at key points to avoid prematurely exhausting the privacy budget. Then, an adaptive allocation of the privacy budget at these points enhances privacy protection for sensitive data. Additionally, the optimized square wave (SW) mechanism introduces perturbations to the sampled points. Afterward, the Kalman filter algorithm is applied to maintain data flow patterns and reduce prediction errors. Experimental validation using two real datasets demonstrates that, under comparable conditions, this approach provides higher data availability than existing privacy protection methods for continuous data streams.

Learning incremental audio–visual representation for continual multimodal understanding

Deep learning methods have demonstrated remarkable success in processing static datasets for various video tasks. However, when confronted with continuous data streams, these approaches often encounter the challenge of catastrophic forgetting. This phenomenon leads to a significant decline in overall performance when learning new classes incrementally. Moreover, existing methods tend to overlook the correlation between audio and visual modalities in video incremental learning, despite their joint significance in scene comprehension. How to continuously learn from new classes while maintaining the knowledge of old videos with limited storage and computing resources is becoming imperative in the field of multimodal learning. In this paper, we introduce CavRL, a pioneering benchmark for audio–visual representation learning under class incremental scenarios. To mitigate catastrophic forgetting, we propose a rehearsal-based training approach that leverages a small exemplar set from previous classes. Our approach constrains the memory buffer within strict storage limits, optimizing exemplar selection by learning correlative audio–visual representations. Additionally, we employ a distillation method to mitigate forgetting in a self-supervised manner. Evaluations on two prevalent multimodal tasks: audio–visual event classification and audio–visual speaker recognition, which demonstrate that CavRL outperforms existing state-of-the-art incremental learning methods across various settings. We anticipate that CavRL will significantly advance research in continual multimodal learning.

Floor-Level Occupancy Estimation of a Multi-Story Building Using Coarse Wi-Fi Data

In recent years, there has been an extraordinary increase in wireless capable devices and network infrastructure, which spawned a corresponding rise in data produced from the interactions of these technologies. Mobile devices constantly roam, leading to a perpetual dialog between a mobile device and wireless access points. This dialogue generates a continuous stream of device-specific data, including but not limited to a device’s media access control address, time of access, and received signal strength. Given the knowledge of the access point’s location and received signal strength, it is possible to infer the position of user devices and estimate their mobility and occupancy. This article presents two methods for accurately measuring floor-level occupancy in a multi-story building at Texas State University using coarse Wi-Fi log data. The first method employs a static filter, while the second incorporates user-role data and user location to create a dynamic filter. Quantitative methods are used to evaluate these filters against field-collected reference data and existing internal people-counting sensors. Our results demonstrate that the dynamic filter, leveraging variable thresholds, provides a more accurate estimation of occupancy compared to the fixed 5-min static filter which consistently overestimated occupancy. This research sheds light on the potential of dynamic filters derived from user-role data for precise floor-level occupancy estimations, with implications for various applications.

An optimized hybrid encryption framework for smart home healthcare: Ensuring data confidentiality and security

This study proposes an optimized hybrid encryption framework combining ECC-256r1 with AES-128 in EAX mode, tailored for smart home healthcare environments, and conducts a comprehensive investigation to validate its performance. Our framework addresses current limitations in securing sensitive health data and demonstrates resilience against emerging quantum computing threats. Through rigorous experimental evaluation, we show that the proposed configuration outperforms existing solutions by delivering unmatched security, processing speed, and energy efficiency. It employs a robust yet streamlined approach, meticulously designed to ensure simplicity and practicality, facilitating seamless integration into existing systems without imposing undue complexity. Our investigation affirms the framework's capability to resist common cybersecurity threats like MITM, replay, and Sybil attacks while proactively considering quantum resilience. The proposed method excels in processing speed (0.006 seconds for client and server) and energy efficiency (3.65W client, 95.4W server), offering a quantum-resistant security level comparable to AES-128. This represents a security-efficiency ratio of 21.33 bits per millisecond, a 25.6% improvement in client-side processing speed, and up to 44% reduction in server-side energy consumption compared to conventional RSA-2048 methods. These improvements enable real-time encryption of continuous health data streams in IoT environments, making it ideal for IoT devices where AES-128′s smaller footprint is advantageous. By prioritizing high-grade encryption alongside ease of use and implementation, the proposed framework presents a future-proof solution that anticipates the trajectory of cryptographic standards amid advancing quantum computing technologies, signifying a pivotal advancement in safeguarding IoT-driven healthcare data.

Advancements in health informatics for monitoring perioperative patient data

Advancements in health informatics are revolutionizing perioperative care by enhancing patient monitoring, improving clinical decision-making, and promoting patient safety. The integration of electronic health records (EHR) facilitates seamless information flow across all perioperative stages, ensuring up-to-date patient data is accessible to the entire healthcare team. This accessibility reduces errors, improves coordination, and enhances overall patient outcomes. EHR systems also support standardized documentation, reducing variability in record-keeping and ensuring comprehensive patient information is consistently captured. Real-time data analytics and predictive modeling are powerful tools in perioperative settings, enabling clinicians to anticipate and manage potential complications proactively. By analyzing vast datasets, predictive models identify patterns and risk factors associated with adverse outcomes, allowing for targeted interventions and personalized care plans. Real-time analytics provide continuous monitoring of patient status during surgery, offering immediate feedback and enabling prompt adjustments to ensure patient stability. Wearable devices and remote monitoring systems further augment real-time data analytics by continuously tracking physiological parameters throughout the surgical process and into the postoperative period. This continuous data stream allows for ongoing assessment of patient status and early detection of complications, enhancing patient safety and outcomes. Health informatics solutions, including automated alerts and decision support tools, minimize human error and ensure adherence to clinical guidelines. Automated reminders for critical actions, such as timely antibiotic administration, significantly reduce the incidence of surgical site infections. Decision support tools provide real-time, evidence-based recommendations at the point of care, supporting clinicians in making informed decisions and improving patient outcomes. Additionally, health informatics facilitates continuous quality improvement by enabling the collection and analysis of performance data, informing targeted interventions and process improvements. Overall, advancements in health informatics are transforming perioperative care, offering numerous benefits in terms of efficiency, accuracy, and patient safety, ultimately leading to better patient outcomes and higher quality of care. Continued innovation in this field will be essential for maintaining and improving the standards of perioperative care.

How Users Assess Privacy Risks in the Internet of Things: The Role of Framing, Comparing, and Educating

With the advent of the Internet of Things (IoT), it has become increasingly challenging for users to assess the privacy risks associated with consumer products and the continuous stream of user data needed to operate them. In this study, we propose and test three mechanisms with the potential to help users make more accurate assessments of privacy risks. We refer to these mechanisms as framing (i.e., presenting information on the collection and use of user data with or without direct reference to privacy risks), comparing (i.e., presenting a product and the associated information on data collection and use with or without reference to an alternative product), and educating (i.e., augmenting users’ general privacy literacy). To assess these mechanisms in different IoT contexts, we conducted two scenario-based online experiments with reference to a telematics device ( n = 317) and a fitness tracker ( n = 356). In both studies, we find that actual privacy risks as manipulated in the experiment are only moderately related to the privacy risks perceived by users. However, comparing and educating each helped users make more accurate privacy risk assessments. In Study 2, framing and comparing jointly enabled especially users with low privacy literacy to assess privacy risks more accurately. These findings have meaningful implications for key actors in the IoT ecosystem and those regulating it.

Stream-aware indexing for distributed inequality join processing

Inequality join is an operator to join data on inequality conditions and it is a fundamental building block for applications. While methods and optimizations exist for efficient inequality join in batch processing, little attention has been given to its streaming version, particularly to large-scale data-intensive applications that run on Distributed Stream Processing Systems (DSPSs). Designing an inequality join in streaming and distributed settings is not an easy task: (i) indexes have to be employed to efficiently support inequality-based comparisons, but the continuous stream of data imposes continuous insertions, updates, and deletions of elements in the indexes—hence a huge overhead for the DSPSs; (ii) oftentimes real data is skewed, which makes indexing even more challenging.To address these challenges, we propose the Stream-Aware inequality join (STA), an indexing method that can reduce redundancy and index update overhead. STA builds a separate in-memory index structure for hotkeys, i.e., the most frequently used keys, which are automatically identified with an efficient data sketch. On the other hand, the cold keys are treated using a linked set of index structures. In this way, STA avoids many superfluous index updates for frequent items. Finally, we implement four state-of-the-art inequality join solutions for a widely employed DSPS (Apache Storm) and compare their performance with STA on four real-world data sets and a synthetic one. The results of our experimental evaluation reveal that our stream-aware approach outperforms existing solutions.

IPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control.

The iPREDICT program aimed to develop an integrated digital health solution capable of continuous data streaming, predicting changes in asthma control, and enabling early intervention. As part of the iPREDICT program, asthma triggers were characterized by surveying 221patients (aged ≥18 years) with self-reported asthma for a risk-benefit analysis of parameters predictive of changes in disease control. Seventeen healthy volunteers (age25-65 years) tested 13 devices to measure these parameters and assessed their usability attributes. Patients identified irritants such as chemicals, allergens, weather changes, and physical activity as triggers that were the most relevant to deteriorating asthma control. Device testing in healthy volunteers revealed variable data formats/units and quality issues, such as missing data and low signal-to-noise ratio. Based on user preference and data capture validity, a spirometer, vital sign monitor, and sleep monitor formed the iPREDICT integrated system for continuous data streaming to develop a personalized/predictive algorithm for asthma control. These findings emphasize the need to systematically compare devices based on several parameters, including usability and data quality, to develop integrated digital technology programs for asthma care.

Federated Learning Empowered Real-Time Medical Data Processing Method for Smart Healthcare.

Computer-aided diagnosis (CAD) has always been an important research topic for applying artificial intelligence in smart healthcare. Sufficient medical data are one of the most critical factors in CAD research. However, medical data are usually obtained in chronological order and cannot be collected all at once, which poses difficulties for the application of deep learning technology in the medical field. The traditional batch learning method consumes considerable time and space resources for real-time medical data, and the incremental learning method often leads to catastrophic forgetting. To solve these problems, we propose a real-time medical data processing method based on federated learning. We divide the process into the model stage and the exemplar stage. In the model stage, we use the federated learning method to fuse the old and new models to mitigate the catastrophic forgetting problem of the new model. In the exemplar stage, we use the most representative exemplars selected from the old data to help the new model review the old knowledge, which further mitigates the catastrophic forgetting problem of the new model. We use this method to conduct experiments on a simulated medical real-time data stream. The experimental results show that our method can learn a disease diagnosis model from a continuous medical real-time data stream. As the amount of data increases, the performance of the disease diagnosis model continues to improve, and the catastrophic forgetting problem has been effectively mitigated. Compared with the traditional batch learning method, our method can significantly save time and space resources.

SS-CRE: A Continual Relation Extraction Method Through SimCSE-BERT and Static Relation Prototypes

Continual relation extraction aims to learn new relations from a continuous stream of data while avoiding forgetting old relations. Existing methods typically use the BERT encoder to obtain semantic embeddings, ignoring the fact that the vector representations suffer from anisotropy and uneven distribution. Furthermore, the relation prototypes are usually computed by memory samples directly, resulting in the model being overly sensitive to memory samples. To solve these problems, we propose a new continual relation extraction method. Firstly, we modified the basic structure of the sample encoder to generate uniformly distributed semantic embeddings using the supervised SimCSE-BERT to obtain richer sample information. Secondly, we introduced static relation prototypes and dynamically adjust their proportion with dynamic relation prototypes to adapt to the feature space. Lastly, through experimental analysis on the widely used FewRel and TACRED datasets, the results demonstrate that the proposed method effectively enhances semantic embeddings and relation prototypes, resulting in a further alleviation of catastrophic forgetting in the model. The code will be soon released at https://github.com/SuyueW/SS-CRE.

Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

The Pulse of Progress: Smart Wearables and the Evolution of Cardiovascular Monitoring Technologies

This paper explores the transformative role of smart wearables in the evolution of cardiovascular monitoring technologies. With cardiovascular diseases (CVDs) as the leading cause of mortality globally, there's a critical need for continuous, real-time health monitoring outside traditional clinical settings. Smart wearables, ranging from fitness trackers to specialized health-monitoring devices, have emerged as pivotal tools in this regard. They offer personalized, preventive, and real-time management of health, leveraging advancements in technology to provide a continuous stream of health data previously inaccessible. This systematic review draws upon recent studies to examine the effectiveness of wearable technologies in detecting, predicting, and managing CVDs.

Optofluidic time-stretch imaging flow cytometry with a real-time storage rate beyond 5.9 GB/s.

Optofluidic time-stretch imaging flow cytometry (OTS-IFC) provides a suitable solution for high-precision cell analysis and high-sensitivity detection of rare cells due to its high-throughput and continuous image acquisition. However, transferring and storing continuous big data streams remains a challenge. In this study, we designed a high-speed streaming storage strategy to store OTS-IFC data in real-time, overcoming the imbalance between the fast generation speed in the data acquisition and processing subsystem and the comparatively slower storage speed in the transmission and storage subsystem. This strategy, utilizing an asynchronous buffer structure built on the producer-consumer model, optimizes memory usage for enhanced data throughput and stability. We evaluated the storage performance of the high-speed streaming storage strategy in ultra-large-scale blood cell imaging on a common commercial device. The experimental results show that it can provide a continuous data throughput of up to 5891 MB/s.