Latency Reduction Research Articles

Implementing a smart city using internet of things based on wildwood combined with fractional order-based golden search algorithm.

The fast expansion of Internet of Things (IoT) devices in urban environments has resulted in a dramatic increase in both the volume and complexity of data produced, necessitating the implementation of sophisticated data analytics and machine learning methodologies to fully realize the advantages of smart cities. The incorporation of IoT sensors and devices has facilitated the establishment of extensive, dynamic, and diverse networks, which present considerable challenges for data analysis and decision-making processes. In response to these challenges, machine learning algorithms have surfaced as a feasible solution, capable of discerning intricate patterns and relationships within the data. Nonetheless, the efficacy of these algorithms is significantly influenced by the precise tuning of hyperparameters, a task that can be quite complicated, particularly within IoT-enabled smart cities. This study concentrates on the creation of an innovative optimization framework that integrates the WildWood algorithm with a fractional-order variant of the Golden Search Algorithm for hyperparameter optimization. The proposed framework is measured through simulations in a smart city traffic management background, resulting in notable reductions in latency (up to 30%), energy consumption (up to 25%), and enhancements in throughput (up to 20%) when compared to conventional optimization techniques. Moreover, the optimized WildWood model achieves a Mean Squared Error (MSE) of 0.85, indicating its proficiency in accurately forecasting traffic flow patterns. The results underscore the effectiveness of the proposed framework in enhancing the reliability, efficiency, and sustainability of IoT-enabled smart city systems.

End-to-End Deployment of Winograd-Based DNNs on Edge GPU

The Winograd algorithm reduces the computational complexity of convolutional neural networks (CNNs) by minimizing the number of multiplications required for convolutions, making it particularly suitable for resource-constrained edge devices. Concurrently, most edge hardware accelerators utilize 8-bit integer arithmetic to enhance energy efficiency and reduce inference latency, requiring the quantization of CNNs before deployment. Combining Winograd-based convolution with quantization offers the potential for both performance acceleration and reduced energy consumption. However, prior research has identified significant challenges in this combination, particularly due to numerical instability and substantial accuracy degradation caused by the transformations required in the Winograd domain, making the two techniques incompatible on edge hardware. In this work, we describe our latest training scheme, which addresses these challenges, enabling the successful integration of Winograd-accelerated convolution with low-precision quantization while maintaining high task-related accuracy. Our approach mitigates the numerical instability typically introduced during the transformation, ensuring compatibility between the two techniques. Additionally, we extend our work by presenting a custom-optimized CUDA implementation of quantized Winograd convolution for NVIDIA edge GPUs. This implementation takes full advantage of the proposed training scheme, achieving both high computational efficiency and accuracy, making it a compelling solution for edge-based AI applications. Our training approach enables significant MAC reduction with minimal impact on prediction quality. Furthermore, our hardware results demonstrate up to a 3.4× latency reduction for specific layers, and a 1.44× overall reduction in latency for the entire DeepLabV3 model, compared to the standard implementation.

Optimizing IoT Networks in Smart Agriculture Using Probabilistic Models and Machine Learning Algorithms

This study aims to enhance the performance of Internet of Things (IoT) networks in smart agriculture by integrating probabilistic models and machine learning algorithms. The research addresses the need for efficient, reliable, and scalable IoT infrastructures to support precision farming and sustainable agricultural practices in increasingly complex and data-intensive farming environments. A multi-faceted approach was employed, combining probabilistic modeling and machine learning techniques. IoT sensor networks were simulated using real-world agricultural data, supplemented by a small-scale field deployment. Bayesian networks and Hidden Markov Models were applied to capture system dynamics, while Random Forest, Support Vector Machines, and Long Short-Term Memory networks were used for pattern recognition and predictive analytics. Network performance was evaluated using metrics including latency, throughput, packet loss rate, energy efficiency, scalability, reliability, and data accuracy. The integrated approach significantly improved IoT network performance in agricultural settings. Key improvements include: 25% reduction in latency, 35% increase in throughput, 60% decrease in packet loss rate, 30% improvement in energy efficiency, and 6.5% increase in data accuracy. Adaptive routing algorithms, informed by machine learning predictions, were particularly effective in managing network load during peak agricultural seasons, reducing latency by 18% during these periods. The study relied primarily on simulations and limited field trials. Comprehensive real-world implementation across diverse agricultural environments and larger scale deployments is necessary to fully validate the findings. The research did not address potential cybersecurity challenges in optimized IoT networks, which remains an important area for future investigation.

Task offloading for multi-server edge computing in industrial Internet with joint load balance and fuzzy security

The industrial Internet revolutionizes traditional manufacturing through the incorporation of technologies such as real-time production optimization, big data analysis, etc. Computing resource-constrained industrial terminals struggle to effectively execute latency-sensitive and computation-intensive tasks triggered by these technologies. Edge computing (EC) emerges as a promising paradigm for offloading tasks from terminals to the adjacent edge servers, offering the potentiality to augment the computational capacities for industrial terminals. However, the development of accurate offloading strategies poses a prominent challenge for EC in the industrial Internet. Incorrect offloading strategies will misguide the task offloading procedure, resulting in adverse consequences. In this paper, we study the latency-aware multi-server partial EC task offloading problem in the industrial Internet with the consideration of joining load balancing and security protection to provide accurate strategies. Firstly, we establish a task offloading model that supports partial offloading, facilitating latency reduction, task offloading across multiple edge servers with load balance, and accommodation of fuzzy task risks. We quantify the established model as a constrained optimization formulation and prove its NP-hardness. Secondly, to solve the composite offloading strategy comprising both the offloading location and offloading ratio derived from our model, we propose a bi-layer offloading algorithm with joint load balance and fuzzy security, which is based on the adaptive genetic algorithm and simulated annealing particle swarm optimization. Based on extensive experimental results, we find that the established model is effective in reducing the objective value, with a respective decrease of 27% and 46% compared to full execution in edge servers and local execution in industrial terminals. Furthermore, the proposed offloading algorithm exhibits superior performance in terms of solution accuracy compared to existing algorithms.

Enhancing data management and real‐time decision making with IoT, cloud, and fog computing

AbstractThe convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real‐time decision‐making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real‐time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud‐only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real‐time decision‐making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real‐world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large‐scale IoT deployments. Future research will focus on real‐world validation and the development of enhanced dynamic resource management techniques.

Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.

Artificial intelligence in the Internet of Things: Integrating and optimizing AI algorithms for real-time data processing and decision-making

Abstract. The introduction of Artificial Intelligence (AI) will provide great opportunities and pose significant challenges in Internet of Things (IoT) systems. IoT devices can create huge amount of data in real-time. Meanwhile, it is crucial to handle immediate processing and decision-making in IoT applications. However, IoT devices are usually constrained by limited computation power, memory, and energy compared to traditional devices, making the deployment of efficient AI algorithms a challenging task. In this paper, we present different algorithmic strategies to overcome the above problems, including model compression, quantization, and hardware accelerators. On the other hand, we also focus on the role of decentralization and edge computing architectures to increase the scalability and performance of AI-IoT systems. Finally, this paper also reviews energy-efficient AI algorithms and latency reduction methods to make the decision process real-time for various IoT applications.

A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm

In the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing such services, as it can meet computation requirements and enhance resource utilization. This study delves into efficient and intelligent dynamic edge-server placement by taking into account time-varying network scenarios and deployment costs. Firstly, edge servers are classified into static and dynamic ones. Subsequently, an improved snake optimization algorithm is proposed to determine the number and placement locations of dynamic servers while adhering to delay requirements. Finally, a minimum placement-cost algorithm is put forward to further reduce the service cost. Experimental results demonstrate that compared to classic algorithms, the proposed algorithms can achieve a reduction in latency of 5% to 12%. And compared to the state-of-the-art methods, they can reduce service costs by 20% to 43%. This research offers an effective solution for dynamic edge-server placement and holds great theoretical and practical significance.

Edge-Cloud Continuum: Integrating Edge Computing and Cloud Computing for IOT Applications

Abstract: This research will use a mixed-method approach with qualitative understanding and the quantitative analysis of data to examine how edge and cloud computing blend into the frame of an edge-cloud continuum for IoT applications. It adopts an exploratory and descriptive approach to research in carrying out a holistic assessment of the edge-cloud continuum in efficiency, scalability, and performance. The data required for collection was obtained through simulations using the IoTSim-Osmosis framework and also through the use of other case studies and literature published elsewhere before preparing this document. The data is usual to what has been used in several IoT deployments, including smart cities, the health sector, and industrial automation. Based on those KPIs - latency, 70.83%; bandwidth utilization, 40% reduced; energy consumption, 25% reduced; and task completion rate improved by 18.75% - the edge-cloud continuum significantly outperforms traditional cloud-only systems. Especially a very big reduction in latency is significantly important for real-time applications as it would drive the potential ability to improve responsiveness in those applications, like autonomous cars and smart healthcare. The current study demonstrates that the edge-cloud continuum can efficiently enhance resource allocation and enhance the effectiveness of complex IoT systems. This has wide implications for designing and implementing IoT solutions across industry domains

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.

Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments

This article presents a comprehensive analysis of edge computing and analytics for Internet of Things (IoT) devices, addressing the growing need for real-time processing and reduced latency in IoT applications. We explore the evolution of IoT architectures, from cloud-centric models to edge-enabled systems, and examine the key components and data flows in edge computing environments. Through a detailed comparison of cloud and edge processing, we demonstrate significant latency reductions across various IoT scenarios, with improvements from hundreds of milliseconds to mere milliseconds. The article delves into crucial aspects of data management in edge computing, including local versus cloud processing trade-offs, data synchronization strategies, and privacy considerations. A case study of an edge analytics pipeline in a smart factory setting showcases practical implementations, revealing substantial improvements in anomaly detection speed, bandwidth utilization, and overall system efficiency. The case study achieved a 92.5% reduction in anomaly detection latency and an 85% decrease in bandwidth usage. Finally, we discuss ongoing challenges and future directions in edge computing, including scalability issues, standardization efforts, and the integration of emerging technologies such as 5G and AI accelerators. This article contributes to the growing body of knowledge on edge computing in IoT, offering insights into its transformative potential for creating more responsive and intelligent systems across diverse applications.

LSTM‐based real‐time stress detection using PPG signals on raspberry Pi

AbstractStress, widely recognised for its profound adverse effects on both physical and mental health, necessitates the development of innovative real‐time detection methods. In this context, the escalating prevalence of wearable embedded systems, integrated with artificial intelligence (AI) for the continuous monitoring of critical physiological indicators like heart rate and blood pressure, accentuates their growing relevance in the efficient detection of stress. This article presents an innovative methodology employing deep learning algorithms on the Raspberry Pi 3, a platform distinguished by its cost‐effectiveness and limited resources. The authors have developed an advanced AI algorithm that achieves high accuracy in real‐time stress detection using photoplethysmography (PPG) sensors while significantly reducing computational demands. The authors’ method utilises an artificial neural network with long short‐term memory (LSTM) layers, proving highly effective in time‐series data analysis. In this study, the authors implement key TensorFlow toolkit optimisation techniques including quantisation aware training (QAT), Pruning and prune‐preserving quantisation aware training. These techniques are applied to refine the authors’ model, decreasing size and latency without sacrificing accuracy. The results highlight the LSTM‐based model's proficiency in accurately detecting stress using raw PPG sensor data on the Raspberry Pi 3, a comparatively affordable platform. The model attains an accuracy of 89.32% and an F1 score of 89.55% on the diverse wearable stress and affect detection stress‐level dataset. Additionally, the authors’ optimised model exhibits substantial reductions in both size and latency while maintaining high accuracy. This approach shows great potential for various applications, such as stress monitoring in healthcare, sports, and workplace settings. The use of the Raspberry Pi 3 makes the system portable, cost‐effective, and energy‐efficient, enhancing its potential impact and accessibility.

Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite

This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing. Emerging intelligent sensors equipped with computing assets to run neural network inferences and embedded in the same package, which hosts the sensing elements, present new challenges due to their limited memory resources and computational skills. This benchmark evaluates models trained with Quantization Aware Training (QAT) and compares their performance with Post-Training Quantization (PTQ) across three use cases: Human Activity Recognition (HAR) by means of the SHL dataset, Physical Activity Monitoring (PAM) by means of the PAMAP2 dataset, and superficial electromyography (sEMG) regression with the NINAPRO DB8 dataset. The results demonstrate the effectiveness of QAT over PTQ in most scenarios, highlighting the potential for deploying advanced AI models on highly resource-constrained sensors. The INT8 versions of the models always outperformed their FP32, regarding memory and latency reductions, except for the activations for CNN. The CNN model exhibited reduced memory usage and latency with respect to its Dense counterpart, allowing it to meet the stringent 8KiB data RAM and 32 KiB program RAM limits of the ISPU. The TCN model proved to be too large to fit within the memory constraints of the ISPU, primarily due to its greater capacity in terms of number of parameters, designed for processing more complex signals like EMG. This benchmark aims to guide the development of efficient AI solutions for In-Sensor Machine Learning Computing, fostering innovation in the field of Edge AI benchmarking, such as the one conducted by the MLCommons-Tiny working group.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

A Collaborative Visual Sensing System for Precise Quality Inspection at Manufacturing Lines

Visual sensing has been widely adopted for quality inspection in production processes. This article presents the design and implementation of a smart collaborative camera system, called BubCam , for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system that leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system that additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction compared with the manual inspection approach.

Telehealth based parental support over 6 months improves physical activity and sleep quality in children with autism: a randomized controlled trial.

Sleep disturbances are prevalent in autistic children. The emergence of telehealth offers new possibilities for remote professional intervention. By combining telehealth with parental support, this study aims to explore a novel family-based model to enhance moderate-to-vigorous physical activity (MVPA) and improve sleep quality in children with autism. Thirty-four autistic children (mean age = 15.7 years) were randomly assigned to either a 6-month intervention group or a control group. Both groups received standard physical education classes at school. The intervention group received additional after-school telehealth support. MVPA and sleep quality were assessed 1 week before the intervention and at the 6-month follow-up. After 6 months, children in the intervention group nearly doubled their daily MVPA compared to the control group (Cohen's d = 8.34, CI95% = 6.17-10.52). Actigraphy-assessed sleep efficiency was notably higher (d = 2.35, CI95% = 1.44-3.26), and there were reductions in wake time (d = 1.65, CI95% = 0.84-2.46), sleep fragmentation (d = 0.80, CI95% = 0.07-1.52), and sleep latency (d = 0.82, CI95% = 0.09-1.54) were all reduced. These improvements in objective sleep metrics were corroborated by subjective assessments using the Sleep Disturbance Scale for Children (d = 0.86, CI95% = 0.13-1.59). Telehealth combined with parental support addresses barriers to enhancing health behaviors at home. This innovative model not only improves after-school MVPA and sleep quality in autistic children but also holds significant potential for benefiting other populations requiring remote support. https://clinicaltrials.gov/study/NCT06444659?id=NCT06444659&rank=1 (NCT06444659).

Container migration for edge computing in industrial Internet with joint latency reduction and reliability enhancement

Edge computing has emerged as a prominent trend in the field of information technology, offering flexible and robust resources for the industrial Internet. How to migrate container accurately is crucial for edge computing in the industrial Internet, as it plays a vital role in enhancing service response speed and safeguarding uninterrupted continuity of production operations. In this paper, we explore the problem of container migration in edge computing within the industrial Internet, aiming to reduce latency and enhance reliability. We establish a two-objective optimization model to comprehensively capture the container migration problem and formulate it as a constrained optimization model. The formulated model provides a systematic framework that effectively balances the trade-off between reducing latency and enhancing reliability. To tackle the migration strategy derived from the optimization model, we propose a migration algorithm based on the improved binary whale optimization algorithm. Our migration algorithm incorporates the adaptive probability and adaptive position weight within the hunting and searching operations, effectively enhancing the search efficiency during the solving process. The experimental results demonstrate the effectiveness of the established model in reducing the objective value, while the proposed migration algorithm surpasses existing algorithms by achieving an average reduction of at least 15.59% in the objective value.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Contingent negative variation for assessment of neurological deficits in children with hypothyroidism

Objectives: Hypothyroidism is one of the most common endocrine disorders. Its effect on the central nervous system is more pronounced, especially in the pediatric age group. Despite receiving adequate thyroid hormone replacement therapy, several patients continue to suffer from neurological impairments including cognitive dysfunction. Contingent negative variation (CNV) is an event-related potential (ERP) that is considered as an indicator of cognitive function. In this study, CNV was recorded in children with hypothyroidism. To the best of our knowledge to date, there have been no studies of CNV in hypothyroidism. Materials and Methods: This prospective study included 52 children between 8 and 15 years of age who were newly diagnosed with hypothyroidism. Diagnosis of hypothyroidism was based on laboratory thyroid function tests. CNV ERP was recorded in the enrolled children at the time of diagnosis, 1-month, and 6-month follow-up. Initial CNV (iCNV) and late CNV (lCNV) amplitudes and latencies were recorded each time. Results: Although the amplitudes of iCNV and lCNV appeared to increase during follow-ups, the changes were not statistically significant (P > 0.05). Similarly, there appeared to be a modest reduction in latencies of iCNV or lCNV during follow-up; however, these changes were not statistically significant either. Conclusion: Our study did not show any significant changes in neurophysiological parameters. This may be attributed to a shorter time period of follow-up of six months and a smaller sample size. There is a possibility that CNV parameters may show more pronounced changes after a prolonged duration of treatment.

An Enhanced Edge Computing Technique for Detection of Voltage Fluctuation in Grid-tied Renewable Energy

Renewable energy sources (RES) such as solar photovoltaic and wind are becoming the most attractive power generation options in many nations. Even while high penetration seems likely, power quality anomalies such as voltage fluctuation, harmonics, and frequency fluctuation associated with RES hinder seamless integration. The variability and unpredictability of these sources create the most oddities. In grid-tied renewable energy, monitoring power quality efficiently is crucial. Power grid monitoring solutions in related literature use sensor-based cloud and edge computing techniques. The existing systems struggle with excessive latency when delivering large amounts of generated data to the cloud. To fill this gap, a new approach for the detection and localization of voltage fluctuation is proposed in this study. The approach integrated three techniques namely; feed-forward neural network (FFNN), Stockwell transform, and anomaly-aware edge computing to detect and locate voltage fluctuation in a GtRE. Using MATLAB/Simulink, virtual emulation of a modified IEEE 33 Bus and a GtRE representing a section of Ado Ekiti (in Nigeria) low-voltage distribution grid are carried out for data generation and system evaluation. Feature extraction was carried out in a Python IDE using Stockwell transform. The voltage fluctuation events are detected and localized based on the extracted features using the trained FFNN model deployed and evaluated within three microcontroller-based computing devices. The proposed approach integrated anomaly-aware with edge computing to send only voltage data that are considered abnormal to a dedicated data center for visualization and storage. Performance evaluation of the proposed technique on the simulated GtRE demonstrates a significant decrease of 98% and 90% in latency when compared to cloud computing and conventional edge computing respectively. Comparison of the proposed approach to two closely related solutions in literature also demonstrates a 50% and 92.5 % reduction in latency. The contribution of the study is the reduced latency and minimal bandwidth utilization achieved by the implementation of the developed technique.