Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, ETrain and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, roundtrip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.publishedVersio

Dynamic application partitioning and task-scheduling secure schemes for biosensor healthcare workload in mobile edge cloud

Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.publishedVersio

Delay optimal schemes for Internet of Things applications in heterogeneous edge cloud computing networks

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.Web of Science2216art. no. 593

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan

Smart healthcare system for severity prediction and critical tasks management of COVID-19 patients in IoT-fog computing environments

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.Web of Science2022art. no. 501296

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system

Towards Next-Generation Healthcare: Architectural Insights into an AI-Driven, Smartwatch-Compatible mHealth Application

With the rise of telemedicine and wearable technology, mHealth (mobile health) applications are becoming increasingly important in providing real-time, personalized healthcare solutions. However, integrating these technologies effectively to deliver high-quality care is a significant challenge. This paper proposes a novel system architecture for an AIassisted mHealth application that seamlessly integrates video conferencing platforms, smartwatch APIs, and AI algorithms. Our proposed architecture facilitates real-time health monitoring during video consultations by allowing healthcare providers to access patients' health data gathered from their wearable smartwatches. Furthermore, integrating AI algorithms provides personalized health recommendations based on patterns identified from the collected data. Additionally, this research delves deep into the implementation considerations and cloud architectural paradigms, underscoring the challenges and discrepancies between proposed designs and real-world application feasibility. The proposed architecture addresses the needs of modern healthcare services and offers the potential for further enhancements in the realm of AI-assisted telemedicine.publishedVersio

Joint Impact of Agents and Services in Enhancing Software Requirements Engineering

Requirements engineering (RE) is a significant aspect of system development stages in generating reliable software (SW). Despite RE’s decisive impact on project success, SW systems still fail since there is a perplexity in sorting out requirements correctly. Researchers have tried several paradigms to deal with the specified challenges, such as agent-oriented RE (AORE), model-based RE, and service-oriented RE (SORE). By investigating the limitations of the independent use of these paradigms, this research sets an objective that proposes a framework which integrates the two paradigms (agent and service) on top of social media to enhance the SW RE processes. Thus, the research addresses challenges in gathering adequate requirements, detecting alignment between business requirements and SW products, prioritizing requirements, and recommending innovative ideas. The research has mainly adopted an empirical research methodology for SW engineering. Accordingly, two distinct expert groups have been formed based on their previous experience in AORE and SORE, respectively. The experts have been selected from enterprises and academic institutions, and they participated in our case study. After performing the necessary assessment based on specified criteria, those experts in the first group have reported that CASCRE (Collaboration of Agents and Services for Crowd-based Requirements Engineering) with a score of 93.7% is found to be better than that of AORE with a score of 88.7%. Moreover, experts in the second group have declared that CASCRE, with a score of 92.3%, is better than SORE, with a score of 83.7%. In both cases, improvements have been observed, which reveals that the synergy of the CASCRE features has a better impact on the RE process than utilizing individual approaches. Moreover, in order to demonstrate the applicability of CASCRE, feedback has been gathered from a focused crowd of local pharmaceuticals using a mini-prototype. Accordingly, 250 requirements related comments have been gathered from the discussion forum, and 1400 keywords were generated. Then, after performing a sentiment analysis using NLP algorithms, the result was demonstrated to experts. Therefore, 93% of gurus strongly agreed on the applicability of CASCRE in real projects

EDCNNS : Federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection

Funding Information: The authors acknowledge the Researchers Supporting Project number (RSP2023R34), King Saud University, Riyadh, Saudi Arabia. This work has been developed at Kristiania University College, Oslo, 0107, Norway. Publisher Copyright: © 2023 Elsevier B.V.Alzheimer's is a dangerous disease prevalent in human societies, and unfortunately, its incidence is increasing daily. The number of patients is on the rise, while the availability of physical doctors has become limited and their schedules are packed. Consequently, the adoption of digital healthcare systems for Alzheimer's disease (AD) has become more common, aiming to alleviate the burden on both AD patients and doctors. AD digital healthcare is a highly complex domain that incorporates various technologies, including fog computing, cloud computing, and deep learning algorithms. However, the implementation of these fog, cloud, and deep learning technologies has encountered challenges related to high computational time during AD detection processes. To address these challenges, this paper focuses on the convex optimization problem, which aims to optimize computation time and accuracy constraints in digital healthcare applications for AD. Convex optimization necessitates the use of an evolutionary algorithm that can enhance different AD constraints in distinct phases. The paper introduces a novel scheme called Evolutionary Deep Convolutional Neural Network Scheme (EDCNNS), designed to minimize computation time and achieve the highest prediction accuracy criteria for AD. EDCNNS comprises several phases, including feature extraction, selection, execution, and scheduling on the fog cloud nodes. The simulation results demonstrate that EDCNNS optimized security by 38%, reduced the deadline failure ratio by 29%, and improved selection accuracy by 50% across different Alzheimer's classes compared to existing studies.Peer reviewe

Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors’ performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems