ABSTRACT Coronary heart disease is a leading cause of mortality worldwide. Although no cure exists for this condition, appropriate treatment and timely intervention can effectively manage its symptoms and reduce the risk of complications such as heart attacks. Prior studies have mostly relied on a limited dataset from the UC Irvine Machine Learning Repository, predominantly focusing on Machine Learning (ML) models without incorporating Explainable Artificial Intelligence (XAI) or Generative Artificial Intelligence (GAI) techniques for dataset enhancement. While some research has explored cloud‐based deployments, the implementation of edge AI in this domain remains largely under‐explored. Therefore, this paper proposes HealthEdgeAI, a sustainable approach to heart disease prediction that enhances XAI through GAI‐driven data augmentation. In our research, we assessed multiple AI models by evaluating accuracy, precision, recall, F1‐score, and area under the curve (AUC). We also developed a web application using Streamlit to demonstrate our XAI methods and employed FastAPI to serve the optimal model as an API. Additionally, we examined the performance of these models in cloud computing and edge AI settings by comparing key Quality of Service (QoS) parameters, such as average response rate and throughput. To highlight the potential of sustainable edge AI and cloud computing, we tested edge devices with both low‐ and high‐end configurations to illustrate differences in QoS. Ultimately, this study identifies current limitations and outlines prospective directions for future research in AI‐based cloud and edge computing environments.

The massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module’s performance using a predictive maintenance-based real-world IIoT application and the AI module’s performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO2. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0.

Containerized Edge computing offers lightweight, reliable, and quick solutions to latency-critical Machine Learning (ML) and Deep Learning (DL) applications. Existing solutions considering multiple Quality of Service (QoS) parameters either overlook the intricate relation of QoS parameters or pose significant scheduling overheads. Furthermore, reactive decisionmaking can damage Edge servers at peak load, incurring escalated costs and wasted computations. Resource provisioning, scheduling, and ML model selection substantially influence energy consumption, user-perceived accuracy, and delayoriented Service Level Agreement (SLA) violations. Addressing contrasting objectives and QoS simultaneously while avoiding server faults is highly challenging in the exposed heterogeneous and resource-constrained Edge continuum. In this work, we propose the EdgeAIBus framework that offers a novel joint container management and ML model selection algorithm based on Importance Weighted Actor-Learner Architecture to optimize energy, accuracy, SLA violations, and avoid server faults. Firstly, Patch Time Series Transformer (PatchTST) is utilized for CPU usage predictions of Edge servers for its 8.51% Root Mean Squared Error and 5.62% Mean Absolute Error. Leveraging pipelined predictions, EdgeAIBus conducts consolidation, resource oversubscription, and ML/DL model switching with possible migrations to conserve energy, maximize utilization and user-perceived accuracy, and reduce SLA violations. Simulation results show EdgeAIBus oversubscribed 110% cluster-wide CPU with real usage up to 70%, conserved 14 CPU cores, incurred less than 1% SLA violations with 2.54% drop in inference accuracy against industry-led Model Switching Balanced load and Google Kubernetes Optimized schedulers. Google Kubernetes Engine experiments demonstrate 80% oversubscription, 14 CPU cores conservation, 1% SLA violations, and 3.81% accuracy loss against the counterparts. Finally, constrained setting experiment analysis shows that PatchTST and EdgeAIBus can produce decisions within 100ms in a 1-core and 1 GB memory device.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and$CO_{2}$emission amount of these models are evaluated and compared for the training and prediction phases.

Heart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest artificial intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP)-based serverless computing environment due to its advantages, such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters, such as accuracy, precision, recall,$F$-Score, and AUC. Experimental results demonstrate that the light gradient boosting machine model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality-of-Service (QoS) parameters, such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.

Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.

As the concept of the Internet of Things (IoT) has gained attraction, we are seeing an increase in the number of smart homes equipped with Internet-capable devices (such as smart door locks, disaster detectors, and sweeping robots). As a result of the volume of data generated by these connected devices, storage space may be limited, and network congestion can impede the regular operation of other devices. While it is possible to rely on cloud computing to meet performance requirements, some critical and deadline-oriented services require low latency (such as healthcare applications) and improves energy efficiency. As a result, fog computing has been proposed to reduce latency and network congestion, by extending the service to the terminal’s edge. To this aim, a high-performance router can be used as a fog computing node by deploying the service on it. In this paper, we have developed a data forwarding application called Router-Distributor for sustainable smart home, which simulates the fog computing environment using the iFogSim toolkit. We evaluate the performance by comparing the cloud with fog in terms of latency, network utilisation and energy consumption, which clearly shows sustainability of fog computing.

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security- and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, machine learning (ML), and function as a service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to nonserverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and area under the curve (AUC) values, and the K-nearest neighbor model gives the highest accuracy rate of 97.51%.