Cloud Fog Computing Research Articles

A Unification of Fog-Cloud Computing for Data Agitation and Guard Intensification in Industrial Health Care Security

The development of Fog computing is a decentralized environment in which the data processing, storage and applications are processed between the located server in a cloud environment. By increasing the Internet of things (IoT) and remote storage, the communication in cloud become more sophisticated by processing the data safely and securely. Healthcare data is important which contains medical information and processed centrality in the public environment through IoT, due to increasing security breaches they need to protect depends on the security applicants. All over the centralized data computing are accessed by virtual environment through remote protocol doesn’t provide safety in healthcare industry. To resolve this problem, to propose as Unification of fog-cloud computing for data agitation and guard intensification (DA-GI) in Industrial Health Care Security. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and the Fog layer based on the guard intensity and the key is provoked for ingress the file. The activity logs are controlled and monitoring from cloud serves sustains the Activity Log, Risk Table and Health Records. To introduce a cryptographic approach based on advanced encryption standards (AES) to protect data and authenticity verification server. The key verification process based on security gateway, Fog cloud server depends on user access rights provided to the user. During the key validation, role of permission to the user s verified and agitate to allow access rights to the verified service access. The proposed system produce high security compared to the other system as well in all security concerns of role, authentication and verification to process data safely.

Cooperative Control of Air–Ground Swarms Under DoS Attacks via Cloud–Fog Computing

Cooperative Control of Air–Ground Swarms Under DoS Attacks via Cloud–Fog Computing

Cloud-fog architecture-based control of smart island microgrid in master-slave organization using disturbance observer-based hybrid backstepping sliding mode controller

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units. To balance the production power and loads in a smart island with a stable voltage/frequency, a hybrid backstepping sliding mode controller (BSMC) with disturbance observer (DO) is suggested to control voltage/frequency and current in the MG-based master-slave organization. Therefore, this paper proposes a DO-driven BSMC for controlling voltage/frequency, and power of energy sources within a Master-Slave organization; in addition, the study proposes a clod-fog computing for enhancing performance, reducing transferred data volume, and processing information on time. In the extensive simulations, the suggested controller shows a reduction in steady-state error, a fast response, and a lower total harmonic distortion (THD) for nonlinear and linear loads less than 0.33 %. The fog layer serves as a local processing level, so it reduces the exchanged data between cloud and fog nodes.

A Research on Genetic Algorithm-Based Task Scheduling in Cloud-Fog Computing Systems

A Research on Genetic Algorithm-Based Task Scheduling in Cloud-Fog Computing Systems

Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing

Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects.

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

HKCHB: Meta-heuristic Algorithm for Task Scheduling and Load Balancing in Cloud-fog Computing

HKCHB: Meta-heuristic Algorithm for Task Scheduling and Load Balancing in Cloud-fog Computing

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

AbstractVirtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic‐based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.

Statistical pathways to low-carbon cities: Analyzing renewable integration, energy-efficient design, and job creation

To address the existing gap in understanding the intricate dynamics of resource management in Cloud Fog Computing (CFC)-driven Smart Grids (SG), this study introduces an innovative adaptive framework integrating Genetic Optimization Algorithm (GOA) and Cuckoo Search Optimization Algorithm (CSOA), aiming to optimize response times and enhance overall efficiency. In the domain of contemporary energy grids, the implementation of intelligent grids (IG) has ushered in a new era of dependable, streamlined, eco-friendly, and economically viable electric services. These grids constitute a fundamental facet of present-day energy resources. Moreover, the advent of cloud and mist computing (CMC) provides on-demand access to computational resources. Overcoming associated challenges, CMC not only delivers advantages like cost-effectiveness, energy conservation, scalability, flexibility, and adaptability but also presents an innovative model for resource management in IG. This study introduces a CMC-driven strategy for managing resources in IG by digitally replicating key grid components. The proposed framework aims to furnish diverse computational services for resource management within IG through a hierarchical arrangement of CMCs. Additionally, this investigation scrutinizes and simulates two optimization approaches: the cuckoo search and grasshopper optimization algorithms. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy in the context of Renewable Integration, Energy-Efficient Design, and Job Creation.

S2DN: Design of robust authentication protocol with session key establishment in multi-controller based software-defined VANETs

Advancements in technology and the adoption of innovative developments have significantly simplified our lives. One notable innovation in the networking domain is Software Defined Networking (SDN), which revolutionizes the network layer by enabling centralized network administration and re-programmability. SDN technology finds application in diverse fields such as Vehicular Ad-hoc Networks (VANETs), Wireless Sensor Networks (WSNs), Internet of Things (IoT) communications, and cloud-fog computing. The integration of SDN technology into VANET systems has notably improved network performance but has also raised security concerns. To tackle this issue, this paper focuses on devising an authentication mechanism to facilitate secure communication across various VANET levels by establishing a shared session key. Moreover, the research proposes a method to mitigate the challenge of single points of failure, a typically difficult issue to address. To ensure the security of all confidential information during protocol execution, simulations are conducted using the Scyther tool. Additionally, an informal security analysis is performed, demonstrating the robustness of the proposed protocol. The proposed protocol also performs outstanding in terms of performance results

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.

The Microverse: A Task-Oriented Edge-Scale Metaverse

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.

SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing

Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.

Star-Quake: A New Operator in Multi-Objective Gravitational Search Algorithm for Task Scheduling in IoT-Based Cloud–Fog Computing System

Star-Quake: A New Operator in Multi-Objective Gravitational Search Algorithm for Task Scheduling in IoT-Based Cloud–Fog Computing System

Balancing the Load and Scheduling the Tasks using Zebra Optimizer in IoT based Cloud Computing for Big-data Applications.

Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is projected for the integration of fog besides cloud networks to address these expectations, given the proliferation of IoT sensors and the requirement for fast and dependable information access. Given the complexity of job scheduling, it can be difficult to determine the best course of action, particularly for big data systems. The behaviour of zebras in the wild serves as the primary basis of stimulus for the development of the Zebra Optimisation Algorithm (ZOA), a novel bio-inspired metaheuristic procedure presented in this study. ZOA mimics zebras' feeding habits and their defence mechanisms against predators. Various activities are analysed and processed using an optimised scheduling model based on ZOA to minimise energy expenditures and end-to-end delay. To reduce makespan and increase resource consumption, the technique uses a multi-objective strategy. By using a regional exploratory search strategy, the optimisation algorithm may better utilise data and stays out of local optimisation ruts. The analysis revealed that the suggested ZOA outperformed other well-known algorithms. It was advantageous for big data task scheduling scenarios since it converged more quickly than other techniques. It also produced improvements of 18.43% in several outcomes, including resource utilisation, energy consumption, and make span.

Prioritized Task Offloading Mechanism in Cloud-Fog Computing Using Improved Asynchronous Advantage Actor Critic Algorithm

Prioritized Task Offloading Mechanism in Cloud-Fog Computing Using Improved Asynchronous Advantage Actor Critic Algorithm

Optimizing Task Offloading for Collaborative Unmanned Aerial Vehicles (UAVs) in Fog—Cloud Computing Environments

Optimizing Task Offloading for Collaborative Unmanned Aerial Vehicles (UAVs) in Fog—Cloud Computing Environments

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

A Framework of Real-Time Intelligent Transportation System Based on Hybrid Fog-Cloud Computing

Recent years have witnessed a mounting increase in the loss of lives and properties resulted from low-efficient and unsafe traffic management. However, traditional Intelligent Transportation System (ITS) cannot meet the strict requirements of transportation management on the efficiency, especially in terms of the latency. The emergence of fog computing provides a feasible solution to the aforementioned contradiction. In this paper, a framework of Real-time Intelligent Transportation System based on Hybrid Fog-cloud Computing System (RITS-HF) is proposed. All the components of RITS-HF, including the user layer, the fog layer, and the cloud layer in RITS-HF are presented clearly. Subsequently, main functions of RITS-HF are described, which are expected to greatly improve traffic efficiency and strengthen safety. In the meantime, the detailed processes and functions of each layer under different traffic configurations are presented. Finally, the challenges faced by RITS-HF are pointed out and analyzed in detail. RITS-HF is expected to play a vital role in improving the driving safety, increasing traffic efficiency, along with reducing energy consumption to some extent.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling