Journal articles: 'Fog Computing'

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Bhatt, Chintan, and C. K. Bhensdadia. "Fog Computing." International Journal of Grid and High Performance Computing 9, no. 4 (October 2017): 105–13. http://dx.doi.org/10.4018/ijghpc.2017100107.

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The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

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Chen, Songqing, Tao Zhang, and Weisong Shi. "Fog Computing." IEEE Internet Computing 21, no. 2 (March 2017): 4–6. http://dx.doi.org/10.1109/mic.2017.39.

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Pagel, Peter, and Stefan Schulte. "Fog Computing." Informatik Spektrum 42, no. 4 (August 2019): 233–35. http://dx.doi.org/10.1007/s00287-019-01211-z.

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Matt, Christian. "Fog Computing." Business & Information Systems Engineering 60, no. 4 (April 19, 2018): 351–55. http://dx.doi.org/10.1007/s12599-018-0540-6.

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Mangla, Cherry, Shalli Rani, and Henry Kwame Atiglah. "Secure Data Transmission Using Quantum Cryptography in Fog Computing." Wireless Communications and Mobile Computing 2022 (January 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/3426811.

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Fog computing’s idea is to bring virtual existence into objects used on a daily basis. The “objects” layer of fog architecture is also known as the smart object layer (SOL). SOL has provided the fog network with a strong platform to outperform. Although the fog architecture decentralizes data, uses more data centers, and collects and transmits it to adjacent servers for faster processing in fog networks, it faces several security challenges. The security problems of fog computing need to be alleviated for the exploitation of all benefits of fog computing in classical networks. This article has addressed the security challenges in fog computing, potential solutions via quantum cryptography, a use case portraying the importance of quantum cryptography in fog computing along future scope, and research directions.

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Sookhak, Mehdi, F. Richard Yu, Ying He, Hamid Talebian, Nader Sohrabi Safa, Nan Zhao, Muhammad Khurram Khan, and Neeraj Kumar. "Fog Vehicular Computing: Augmentation of Fog Computing Using Vehicular Cloud Computing." IEEE Vehicular Technology Magazine 12, no. 3 (September 2017): 55–64. http://dx.doi.org/10.1109/mvt.2017.2667499.

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R, Dhivya Sree. "Fog Computing in IoT." International Journal of Research Publication and Reviews 4, no. 4 (April 2023): 3214–15. http://dx.doi.org/10.55248/gengpi.4.423.36600.

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Ahuja, Sanjay P., and Niharika Deval. "From Cloud Computing to Fog Computing." International Journal of Fog Computing 1, no. 1 (January 2018): 1–14. http://dx.doi.org/10.4018/ijfc.2018010101.

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This article describes how in recent years, Cloud Computing has emerged as a fundamental computing paradigm that has significantly changed the approach of enterprises as well as end users towards implementation of Internet technology. The key characteristics such as on-demand resource provision, scalability, rapid elasticity, higher flexibility, and significant cost savings have influenced enterprises of all sizes in the wide and successful adoption of Cloud Computing. Despite numerous advantages, Cloud Computing has its fair share of downsides as well. One of those major concerns is latency issues which has relevance to the Internet of Things (IoT). A new computing paradigm has been proposed by Cisco in early 2014 and termed 'Fog Computing'. Fog Computing otherwise known as Edge Computing is the integration of Cloud Computing and IoT. Being located in close proximity to the IoT devices, the Fog assists with latency requirements of IoT related applications. It also meets the data processing needs of IoT devices which are resource constrained by bringing computation, communication, control and storage closer to the end users. Clouds continue to offer support for data analytics. One can think of the IoT-Fog-Cloud as being part of a continuum. This article surveys the current literature on Fog Computing and provides a discussion on the background, details and architecture of Fog Computing, as well as the application areas of Fog Computing. The article concludes with some recommendations in the areas of future research.

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Yacelga, Andres Leon, Nelson B. Arevalo, and Luis Albarracin Zambrano. "Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies." Fusion: Practice and Applications 13, no. 2 (2023): 91–105. http://dx.doi.org/10.54216/fpa.130208.

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The Industrial Internet of Things (IIoT) has ushered in a new era of connectivity and intelligence in industrial settings. At the heart of this transformative landscape lies Fog Computing, a distributed computing paradigm that brings processing power and intelligence closer to the edge of industrial networks. This paper provides a comprehensive survey of Fog Computing's pivotal role in IIoT, elucidating its significance, challenges, emerging trends, and strategies for successful implementation. We delve into the challenges that industrial environments present for Fog Computing, encompassing issues such as scalability, cybersecurity, data management, and interoperability. Strategies for mitigating these challenges are explored, ranging from efficient resource management to robust cybersecurity measures. Furthermore, we investigate recent developments and innovations in Fog Computing, including the integration of Edge AI, 5G networks, and hybrid cloud-fog architectures, shaping the landscape of IIoT. Promising research areas and opportunities are identified, with a focus on optimizing edge AI, secure data sharing, and sustainable Fog Computing practices.

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Menon, Varun G., and Joe Prathap. "Vehicular Fog Computing." International Journal of Vehicular Telematics and Infotainment Systems 1, no. 2 (July 2017): 15–23. http://dx.doi.org/10.4018/ijvtis.2017070102.

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In recent years Vehicular Ad Hoc Networks (VANETs) have received increased attention due to its numerous applications in cooperative collision warning and traffic alert broadcasting. VANETs have been depending on cloud computing for networking, computing and data storage services. Emergence of advanced vehicular applications has led to the increased demand for powerful communication and computation facilities with low latency. With cloud computing unable to satisfy these demands, the focus has shifted to bring computation and communication facilities nearer to the vehicles, leading to the emergence of Vehicular Fog Computing (VFC). VFC installs highly virtualized computing and storage facilities at the proximity of these vehicles. The integration of fog computing into VANETs comes with a number of challenges that range from improved quality of service, security and privacy of data to efficient resource management. This paper presents an overview of this promising technology and discusses the issues and challenges in its implementation with future research directions.

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Rashid Abdulqadir, Hindreen, Subhi R. M. Zeebaree, Hanan M. Shukur, Mohammed Mohammed Sadeeq, Baraa Wasfi Salim, Azar Abid Salih, and Shakir Fattah Kak. "A Study of Moving from Cloud Computing to Fog Computing." Qubahan Academic Journal 1, no. 2 (April 27, 2021): 60–70. http://dx.doi.org/10.48161/qaj.v1n2a49.

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The exponential growth of the Internet of Things (IoT) technology poses various challenges to the classic centralized cloud computing paradigm, including high latency, limited capacity, and network failure. Cloud computing and Fog computing carry the cloud closer to IoT computers in order to overcome these problems. Cloud and Fog provide IoT processing and storage of IoT items locally instead of sending them to the cloud. Cloud and Fog provide quicker reactions and better efficiency in conjunction with the cloud. Cloud and fog computing should also be viewed as the safest approach to ensure that IoT delivers reliable and stable resources to multiple IoT customers. This article discusses the latest in cloud and Fog computing and their convergence with IoT by stressing deployment's advantages and complexities. It also concentrates on cloud and Fog design and new IoT technologies, enhanced by utilizing the cloud and Fog model. Finally, transparent topics are addressed, along with potential testing recommendations for cloud storage and Fog computing, and IoT.

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Jiang, Jiafu, Linyu Tang, Ke Gu, and WeiJia Jia. "Secure Computing Resource Allocation Framework For Open Fog Computing." Computer Journal 63, no. 4 (January 30, 2020): 567–92. http://dx.doi.org/10.1093/comjnl/bxz108.

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Abstract Fog computing has become an emerging environment that provides data storage, computing and some other services on the edge of network. It not only can acquire data from terminal devices, but also can provide computing services to users by opening computing resources. Compared with cloud computing, fog devices can collaborate to provide users with powerful computing services through resource allocation. However, as many of fog devices are not monitored, there are some security problems. For example, since fog server processes and maintains user information, device information, task parameters and so on, fog server is easy to perform illegal resource allocation for extra benefits. In this paper, we propose a secure computing resource allocation framework for open fog computing. In our scheme, the fog server is responsible for processing computing requests and resource allocations, and the cloud audit center is responsible for auditing the behaviors of the fog servers and fog nodes. Based on the proposed security framework, our proposed scheme can resist the attack of single malicious node and the collusion attack of fog server and computing devices. Furthermore, the experiments show our proposed scheme is efficient. For example, when the number of initial idle service devices is 40, the rejection rate of allocated tasks is 10% and the total number of sub-tasks is changed from 150 to 200, the total allocation time of our scheme is only changed from 15 ms to 25 ms; additionally, when the task of 5000 order matrix multiplication is tested on 10 service devices, the total computing time of our scheme is $\sim$250 s, which is better than that of single computer (where single computer needs more than 1500 s). Therefore, our proposed scheme has obvious advantages when it faces some tasks that require more computational cost, such as complex scientific computing, distributed massive data query, distributed image processing and so on.

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Al-khafajiy, Mohammed, Thar Baker, Hilal Al-Libawy, Zakaria Maamar, Moayad Aloqaily, and Yaser Jararweh. "Improving fog computing performance via Fog-2-Fog collaboration." Future Generation Computer Systems 100 (November 2019): 266–80. http://dx.doi.org/10.1016/j.future.2019.05.015.

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Gautam, Monika, and Jyoti . "Fog Computing: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4003–7. http://dx.doi.org/10.22214/ijraset.2022.45952.

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Abstract: Fog computing provides different types of services & all these services are accessed through different AP (access points) or STB (set top boxes). The fog computing infrastructure provides different services close to client or user. In some way fog computing behaves similar to cloud computing. Both computing technologies provide application, storage, data and computing services to their registered clients. But fog computing provides services close to its end users as compared to cloud computing that provides services remotely. Also fog computing provides thick geographical distribution and having support for mobility. This paper provides literature review on Fog Computing Techniques.

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Sakhi, Abdlehak, Salah-Eddine Mansour, and Abderrahim Sekkaki. "Enhancing security mechanisms for robot-fog computing networks." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (March 1, 2024): 1660. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1660-1666.

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The evolution from conventional Internet usage to the internet of things (IoT) is reshaping communication norms significantly. Cloud computing, while prevalent, faces challenges like limited capacity, high latency, and network failures, especially when handling connected objects, leading to the emergence of fog computing as a more suitable approach for IoT. However, establishing secure connections among heterogeneous IoT entities is complex due to resource disparities and the unsuitability of existing security protocols for resource-constrained devices. This article explores fog computing's architecture, drawing comparisons with cloud computing while emphasizing its significance within the realm of IoT. Moreover, it delves into the practical application of fog computing within the context of the robot teacher project. Subsequently, our exploration introduces an advanced mutual authentication protocol, centered around hashed message authentication code (HMAC), aimed at enhancing the security infrastructure between the robot and the fog computing server.

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., Shruti, Himanshi Babbar, and Shalli Rani. "Security Architecture and Its Methodology for Fog Computing." ECS Transactions 107, no. 1 (April 24, 2022): 4549–62. http://dx.doi.org/10.1149/10701.4549ecst.

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With the rise of Internet of Things (IoT) based applications, cloud computing faced lots of challenges like bandwidth limitation and latency in real time applications. Therefore, fog computing came into existence. It is not a replacement of cloud computing but complements it. The services of fog computing are extended to the edge of the network making communication fast and secure. On the other hand, Software Defined Network (SDN) is also discussed, which is helpful in providing network virtualization. In this paper, study about fog computing and SDN, their characteristics, architecture, applications, key technologies of fog computing are over-viewed, and how fog computing in collaboration with these technologies will be deployed in different applications and areas. Along with it, security of fog computing and a methodology to secure a fog computing environment is proposed.

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Zhang, Jiayi, Abdelkader Ouda, and Raafat Abu-Rukba. "Authentication and Key Agreement Protocol in Hybrid Edge–Fog–Cloud Computing Enhanced by 5G Networks." Future Internet 16, no. 6 (June 14, 2024): 209. http://dx.doi.org/10.3390/fi16060209.

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The Internet of Things (IoT) has revolutionized connected devices, with applications in healthcare, data analytics, and smart cities. For time-sensitive applications, 5G wireless networks provide ultra-reliable low-latency communication (URLLC) and fog computing offloads IoT processing. Integrating 5G and fog computing can address cloud computing’s deficiencies, but security challenges remain, especially in Authentication and Key Agreement aspects due to the distributed and dynamic nature of fog computing. This study presents an innovative mutual Authentication and Key Agreement protocol that is specifically tailored to meet the security needs of fog computing in the context of the edge–fog–cloud three-tier architecture, enhanced by the incorporation of the 5G network. This study improves security in the edge–fog–cloud context by introducing a stateless authentication mechanism and conducting a comparative analysis of the proposed protocol with well-known alternatives, such as TLS 1.3, 5G-AKA, and various handover protocols. The suggested approach has a total transmission cost of only 1280 bits in the authentication phase, which is approximately 30% lower than other protocols. In addition, the suggested handover protocol only involves two signaling expenses. The computational cost for handover authentication for the edge user is significantly low, measuring 0.243 ms, which is under 10% of the computing costs of other authentication protocols.

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Ms. Meenu and Dr. Devender Kumar. "Integration Of Blockchain With Fog Computing To Improvise Security & Privacy Issues." international journal of engineering technology and management sciences 8, no. 1 (2024): 292–98. http://dx.doi.org/10.46647/ijetms.2024.v08i01.038.

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The concept of fog computing was suggested to help cloud computing for the fast data processing of Internet of Things (IoT) based applications. Even, fog computing faces many challenges such as Security, Privacy & Storage. One way to handle these challenges is to integrate blockchain with fog computing. There are several applications of blockchain and fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. Also we have to systematically review the literature on both the technologies (blockchain & fog computing). The purposes of integrating blockchain and fog computing is to tailored search criteria established from the research questions. In this research, the combination of blockchain and fog computing approach for several purposes such as security, privacy, access control, and trust management. By Insufficient laws and standard, it is difficult for blockchain and fog computing to be integrated in the future. Particularly in light of newly developed technologies like quantum computing and artificial intelligence has more power. In this paper we tried to minimize some security issues in fog computing via using the technology of blockchain.

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Zhurylo, Oleh, and Oleksii Liashenko. "Architecture and iot security systems based on fog computing." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 1 (27) (July 2, 2024): 54–66. http://dx.doi.org/10.30837/itssi.2024.27.054.

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The subject of the study is is the security architecture of the Internet of Things (IoT) based on fog computing, which allows providing efficient and secure services for many IoT users. The goal is to investigate the security architecture for IoT systems based on fog computing. To achieve the goal, the following tasks were solved: the concept of fog computing is proposed, its architecture is considered and a comparative analysis of fog and cloud computing architectures is made; the principles of designing and implementing the architecture of a fog computing system are outlined; multi-level security measures based on fog computing are investigated; and the areas of use of fog computing-based Internet of Things networks are described. When performing the tasks, such research methods were used as: theoretical analysis of literature sources; analysis of the principles of designing and implementing the security architecture of the Internet of Things; analysis of security measures at different levels of the architecture. The following results were obtained: the architecture of fog computing is considered and compared with the cloud architecture; the principles of designing and implementing the architecture of fog computing systems are formulated; multi-level IoT security measures based on fog computing are proposed. Conclusions: research on IoT security systems based on fog computing has important theoretical implications. The fog computing architecture, in contrast to the cloud architecture, better meets the demand for high traffic and low latency of mobile applications, providing more advantages for systems that require real-time information processing. When designing and implementing the architecture of fog computing systems, the factors of memory capacity, latency, and utility should be taken into account to effectively integrate fog technologies with IoT. To ensure a high level of system security, multi-level security measures should be implemented using both software and hardware solutions.

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SURESHKUMAR, SWETHA, and DR N. GOB. "FOG COMPUTING IN CLOUD SYSTEM." International Journal of Research Publication and Reviews 5, no. 3 (March 9, 2024): 3362–71. http://dx.doi.org/10.55248/gengpi.5.0324.0760.

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Kumar, M. Santhosh, K. Ganesh Reddy, and Rakesh Kumar Donthi. "SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing." International Journal of Information Technology and Computer Science 16, no. 1 (February 8, 2024): 1–12. http://dx.doi.org/10.5815/ijitcs.2024.01.01.

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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.

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Bhardwaj, Akashdeep. "Novel Taxonomy to Select Fog Products and Challenges Faced in Fog Environments." International Journal of Fog Computing 1, no. 1 (January 2018): 35–49. http://dx.doi.org/10.4018/ijfc.2018010103.

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This article describes how the rise of fog computing to improve cloud computing performance and the acceptance of smart devices is slowly but surely changing our future and shaping the computing environment around us. IoT integrated with advances in low cost computing, storage and power, along with high speed networks and big data, supports distributed computing. However, much like cloud computing, which are under constant security attacks and issues, distributed computing also faces similar challenges and security threats. This can be mitigated to a great extent using fog computing, which extends the limits of Cloud services to the last mile edge near to the nodes and networks, thereby increasing the performance and security levels. Fog computing also helps increase the reach and comes across as a viable solution for distributed computing. This article presents a review of the academic literature research work on the Fog Computing. The authors discuss the challenges in Fog environment and propose a new taxonomy.

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Kuchuk, Heorhii, and Eduard Malokhvii. "INTEGRATION OF IOT WITH CLOUD, FOG, AND EDGE COMPUTING: A REVIEW." Advanced Information Systems 8, no. 2 (June 4, 2024): 65–78. http://dx.doi.org/10.20998/2522-9052.2024.2.08.

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Purpose of review. The paper provides an in-depth exploration of the integration of Internet of Things (IoT) technologies with cloud, fog, and edge computing paradigms, examining the transformative impact on computational architectures. Approach to review. Beginning with an overview of IoT's evolution and its surge in global adoption, the paper emphasizes the increasing importance of integrating cloud, fog, and edge computing to meet the escalating demands for real-time data processing, low-latency communication, and scalable infrastructure in the IoT ecosystem. The survey meticulously dissects each computing paradigm, highlighting the unique characteristics, advantages, and challenges associated with IoT, cloud computing, edge computing, and fog computing. The discussion delves into the individual strengths and limitations of these technologies, addressing issues such as latency, bandwidth consumption, security, and data privacy. Further, the paper explores the synergies between IoT and cloud computing, recognizing cloud computing as a backend solution for processing vast data streams generated by IoT devices. Review results. Challenges related to unreliable data handling and privacy concerns are acknowledged, emphasizing the need for robust security measures and regulatory frameworks. The integration of edge computing with IoT is investigated, showcasing the symbiotic relationship where edge nodes leverage the residual computing capabilities of IoT devices to provide additional services. The challenges associated with the heterogeneity of edge computing systems are highlighted, and the paper presents research on computational offloading as a strategy to minimize latency in mobile edge computing. Fog computing's intermediary role in enhancing bandwidth, reducing latency, and providing scalability for IoT applications is thoroughly examined. Challenges related to security, authentication, and distributed denial of service in fog computing are acknowledged. The paper also explores innovative algorithms addressing resource management challenges in fog-IoT environments. Conclusions. The survey concludes with insights into the collaborative integration of cloud, fog, and edge computing to form a cohesive computational architecture for IoT. The future perspectives section anticipates the role of 6G technology in unlocking the full potential of IoT, emphasizing applications such as telemedicine, smart cities, and enhanced distance learning. Cybersecurity concerns, energy consumption, and standardization challenges are identified as key areas for future research.

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Aljawarneh, Nader Mohammad, Mohamad M. Taamneh, Nouh Alhndawi, Khaled Abed AlQader alomari, and Fawzieh Masa'd. "Fog computing-based logistic supply chain management and organizational agility: The mediating role of user satisfaction." Uncertain Supply Chain Management 9, no. 3 (2021): 767–78. http://dx.doi.org/10.5267/j.uscm.2021.4.001.

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Although fog computing-based logistic supply chain management (Fog computing-based LSCM) is an emerging technology that proved a high impact on services and products, little research has focused on fog computing-based LSCM. Drawing on the Kano model and organization's theory this paper investigates the effect of fog computing-based LSCM on organizational agility. And the role of user satisfaction as mediator between fog computing-based LSCM and organizational agility. A quantitative approach was used, a questionnaire was designed for data collection, Cronbach's Alpha test was performed on a pilot study to examine the internal consistency of questionnaire items. Fog computing-based LSCM was studied based on Supply chain awareness, Connectivity and Logistics, Integration Process, Seamless Supply Chain, Integration of Processes. Data was collected from a random sample of 550 employees of Al-Hassan industrial city‎ in Jordan. Building on the proposed model, Researchers show that fog computing-based LSCM has a positive impact on organizational agility, fog computing-based LSCM has a positive impact on user satisfaction and finally user satisfaction mediates the relationship between fog computing-based LSCM and organizational agility. Implications for the model are discussed.

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Mohammed, Zainab Khalid. "Enhancing IoNT performance with fog computing: A hybrid architecture for real-time data processing." Computer and Telecommunication Engineering 2, no. 4 (December 18, 2024): 3028. https://doi.org/10.54517/cte3028.

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The rapid evolution of the Internet of Nano Things (IoNT) and Fog Computing presents new opportunities for creating advanced smart systems that are both efficient and responsive. Integrating IoNT with Fog Computing offers a powerful paradigm that can address the limitations of cloud-centric architectures, particularly in terms of latency, bandwidth, and real-time processing. The paper explores the synergistic combination of IoNT and Fog Computing, focusing on the development of a hybrid architecture that leverages the proximity and computational capabilities of fog nodes to process data generated by nanoscale devices. Key challenges such as resource management, data processing efficiency, and security concerns are addressed in this study. The proposed architecture not only enhances the performance of smart systems by reducing latency and optimizing resource utilization but also ensures robust security and privacy for the vast amounts of generated data. A comprehensive dataset was generated to assess the integration of the IoNT with Fog Computing, focusing on environmental parameters such as Temperature, Humidity, and Wind Speed, as collected from five IoNT sensors. Python was employed to generate and augment this dataset, ensuring the accurate representation of varied environmental conditions. The data transmission between IoNT sensors and FogNode_1 successfully demonstrated the framework’s ability to capture and process real-time environmental information. Aggregated data from the fog and cloud layers confirmed the system’s efficiency in reducing latency and maintaining data integrity. Furthermore, the implementation of advanced communication protocols and effective resource management highlights the robustness of the integration, contributing to the real-time monitoring and decision-making processes in environmental applications. As well as, this study compares Fog Computing and Cloud Computing, concluding that Fog Computing offers significant advantages in areas like latency, bandwidth utilization, resource efficiency, security, privacy, real-time processing, and edge intelligence. These benefits make Fog Computing particularly suitable for applications requiring low latency, local data processing, enhanced security, and the ability to leverage edge intelligence. While Cloud Computing may have advantages in certain areas, Fog Computing’s overall performance and versatility make it a compelling choice for those seeking to optimize their computing infrastructure. This research aims to pave the way for more resilient and intelligent smart systems that can operate effectively in various domains, including healthcare, environmental monitoring, and industrial automation.

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Journal, IJSREM. "Fog Computing: A Systematic Review of Architecture, IoT Integration, Algorithms and Research Challenges with Insights into Cloud Computing Integration." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (October 19, 2024): 1–6. http://dx.doi.org/10.55041/ijsrem38045.

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With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

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Malik, Babur Hayat, Faisal Mahmood, Sohail Shahzad, Muhammad Bilawal Arif, Waseem Ur Rehman Khan, Sadaf Ilyas, and Muhammad Hassan. "From cloud computing to fog computing in Healthcare big data." MATEC Web of Conferences 189 (2018): 03011. http://dx.doi.org/10.1051/matecconf/201818903011.

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In Healthcare big data, data is originated from various heterogeneous sources. Numerous novel base particular healthcare applications offered to handling source of data from electronic health record (EHR) to medical images. Imaging, Electronic Health Report, technology in light of sensor and numerous different procedures created an immense measure of Healthcare data. Cloud computing development was an excellent paradigm to substantiate big data which incited find of imperceptible examples. Cloud computing is a developing new registering design intended to answer different contending administrations on the Web. Fog Computing is a design style in which arrange segments amongst devices and the cloud execute application-particular rationale. We in this paper investigate, characterize, and talk about various application of cloud and fog computing. We talk about the impact of cloud computing and fog computing on healthcare big data. Cloud base framework for Homediagnosis Service, Fog computing architecture and the justification of moving from cloud to Fog presented comprehensively in this paper.

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Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Fog Computing: A Primer." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 405. http://dx.doi.org/10.23956/ijarcsse.v7i7.165.

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Fog computing (FC) was proposed in 2012 by Cisco as the ideal computing model for providing real-time computing services and storage to support the resource-constrained Internet of Things (IoT) devices. Thus, FC may be regarded as the convergence of the IoT and the Cloud, combining the data-centric IoT services and pay-as-you-go characteristics of clouds. This paper provides a brief introduction of fog computing.

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Papageorgiou, Nikos, Yiannis Verginadis, Dimitris Apostolou, and Gregoris Mentzas. "Fog computing context analytics." IEEE Instrumentation & Measurement Magazine 22, no. 6 (December 2019): 53–59. http://dx.doi.org/10.1109/mim.2019.8917904.

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shoker, Asmaa, Mohammed Amoon, ,. Ayman M. Bahaa-Eldin, and Nirmeen A. El-Bahnasawy. "Resource Allocation Strategy in Fog Computing: Task Scheduling in Fog Computing Systems." Journal of Communication Sciences and Information Technology 1, no. 1 (July 1, 2023): 1–11. http://dx.doi.org/10.21608/jcsit.2023.306757.

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Bala, Mohammad Irfan, and Mohammad Ahsan Chishti. "Survey of applications, challenges and opportunities in fog computing." International Journal of Pervasive Computing and Communications 15, no. 2 (June 3, 2019): 80–96. http://dx.doi.org/10.1108/ijpcc-06-2019-059.

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Purpose Fog computing is a new field of research and has emerged as a complement to the cloud which can mitigate the problems inherent to the cloud computing model such as unreliable latency, bandwidth constraints, security and mobility. This paper aims to provide detailed survey in the field of fog computing covering the current state-of-the-art in fog computing. Design/methodology/approach Cloud was developed for IT and not for Internet of Things (IoT); as a result, cloud is unable to meet the computing, storage, control and networking demands of the IoT applications. Fog is a companion for the cloud and aims to extend the cloud capabilities to the edge of the network. Findings Lack of survey papers in the area of fog computing was an important motivational factor for writing this paper. This paper highlights the capabilities of the fog computing and where it fits in between IoT and cloud. This paper has also presented architecture of the fog computing model and its characteristics. Finally, the challenges in the field of fog computing have been discussed in detail which need to be overcome to realize its full potential. Originality/value This paper presents the current state-of-the-art in fog computing. Lack of such papers increases the importance of this paper. It also includes challenges and opportunities in the fog computing and various possible solutions to overcome those challenges.

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An, Xingshuo, Fuhong Lin, Shenggang Xu, Li Miao, and Chao Gong. "A Novel Differential Game Model-Based Intrusion Response Strategy in Fog Computing." Security and Communication Networks 2018 (August 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/1821804.

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Fog computing is an emerging network paradigm. Due to its characteristics (e.g., geo-location and constrained resource), fog computing is subject to a broad range of security threats. Intrusion detection system (IDS) is an essential security technology to deal with the security threats in fog computing. We have introduced a fog computing IDS (FC-IDS) framework in our previous work. In this paper, we study the optimal intrusion response strategy in fog computing based on the FC-IDS scheme proposed in our previous work. We postulate the intrusion process in fog computing and describe it with a mathematical model based on differential game theory. According to this model, the optimal response strategy is obtained corresponding to the optimal intrusion strategy. Theoretical analysis and simulation results demonstrate that our security model can effectively stabilize the intrusion frequency of the invaders in fog computing.

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Raman, Arumugam So. "Potentials of Fog Computing in Higher Education." International Journal of Emerging Technologies in Learning (iJET) 14, no. 18 (September 30, 2019): 194. http://dx.doi.org/10.3991/ijet.v14i18.10765.

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This paper is documenting the potential of Fog Computing in Education. First, this study explores the difference between cloud computing and Fog Computing. Then the features of computing explained briefly. A tremendous increase in Internet usage among the people does not allow the sustainability to continue depending on Cloud Computing as a centralized web server, due to the truth that Cloud Computing system allows access to internet data as well as therefore making it feasible for users to availability, share along with store information in remote servers. With Fog Computing, multiple users, gadgets such as automobiles, wearable gizmos, sensing units, wise gadgets, an organization can accept one another utilizing their very own Fog facilities. In the educational sector, Fog computing technology boosts educational operations and provides a platform with agility, versus slowing them down or quitting them. Fog computing is a modern technology that is set for high development in the future, as well as will substantially improve day-to-day procedures for many sectors, including education. Finally, security issues and challenges of implementation Fog computing discussed.

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Alzoubi, Yehia Ibrahim, Ahmad Al-Ahmad, and Ashraf Jaradat. "Fog computing security and privacy issues, open challenges, and blockchain solution: An overview." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5081. http://dx.doi.org/10.11591/ijece.v11i6.pp5081-5088.

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Due to the expansion growth of the IoT devices, Fog computing was proposed to enhance the low latency IoT applications and meet the distribution nature of these devices. However, Fog computing was criticized for several privacy and security vulnerabilities. This paper aims to identify and discuss the security challenges for Fog computing. It also discusses blockchain technology as a complementary mechanism associated with Fog computing to mitigate the impact of these issues. The findings of this paper reveal that blockchain can meet the privacy and security requirements of fog computing; however, there are several limitations of blockchain that should be further investigated in the context of Fog computing.

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Qureshi, Ramsha, Muhammad Asad, Saima Tunio, Sirajuddin Qureshi, Mughees Ahmed, and Ali Ghulam. "A Survey on Security Issues and Attacks of Fog Computing." VFAST Transactions on Software Engineering 11, no. 1 (February 26, 2023): 1–11. http://dx.doi.org/10.21015/vtse.v11i1.1309.

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There is a link between the cloud and the Internet of Things (IoT). The layer that makes up the dispersed network environment is exactly what it is. Cloud computing is brought out to the edge of the network through the type of networking topology referred as fog computing. Users can benefit greatly from fog computing. Fog's primary role, similar to cloud computing, is to allow people mobility. Fog computing is becoming more and more popular, whereas at the same time, security dangers are growing every day. Users' identification & verification are crucial. The fact of fog computing cannot effectively utilize the security and privacy solutions provided by cloud computing must be emphasized. The risks, issues, and solutions linked to security in fog computing are outlined throughout this study. The poll then includes information on ongoing research projects as well as open security and safety concerns for fog computing.

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Pg. Ali Kumar, Dk Siti Nur Khadhijah, S. H. Shah Newaz, Fatin Hamadah Rahman, Gyu Myoung Lee, Gour Karmakar, and Thien-Wan Au. "Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach." Electronics 11, no. 4 (February 16, 2022): 608. http://dx.doi.org/10.3390/electronics11040608.

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Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance.

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Kumar, Vishal, Asif Ali Laghari, Shahid Karim, Muhammad Shakir, and Ali Anwar Brohi. "Comparison of Fog Computing & Cloud Computing." International Journal of Mathematical Sciences and Computing 5, no. 1 (January 8, 2019): 31–41. http://dx.doi.org/10.5815/ijmsc.2019.01.03.

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Jain, Jaishree, and Ajit Singh. "Survey on Fog Computing and Cloud Computing." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 752–56. http://dx.doi.org/10.26438/ijcse/v7i5.752756.

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Pasupathy, Dr S. "Empowering Edge Intelligence: A Comprehensive Exploration of Fog Computing in the Era of Real-time Data Processing and the Internet of Things." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 25, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28339.

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Fog computing, a paradigm that extends cloud computing closer to the edge of the network, has emerged as a transformative solution to address the growing demands of latency-sensitive applications and the massive influx of data from the Internet of Things (IoT). Unlike traditional cloud computing, which centralizes data processing and storage in distant data centers, fog computing leverages a distributed architecture, bringing computation, storage, and networking resources closer to the end-users and devices. This abstract explores the key concepts, principles, and advantages of fog computing. By distributing computing resources across a continuum from the cloud to the edge, fog computing minimizes latency, enhances efficiency, and optimizes network bandwidth. The seamless integration of edge devices into the computing infrastructure facilitates real-time processing of data, enabling timely decision-making and improved user experiences in applications ranging from autonomous vehicles to smart cities. Furthermore, the abstract delves into the architectural components of fog computing, including fog nodes, gateways, and the interaction with cloud resources. The dynamic nature of fog environments allows for scalable and flexible deployments, catering to the diverse requirements of modern applications. Security and privacy concerns are also addressed, emphasizing the need for robust mechanisms to protect data integrity and user confidentiality in decentralized computing environments. As the digital landscape continues to evolve, fog computing emerges as a pivotal enabler of edge intelligence, empowering organizations to harness the benefits of real-time data processing and analysis at the edge of the network. The adoption of fog computing signifies a paradigm shift in the way we approach data management and processing, opening new avenues for innovation and efficiency in the era of the Internet of Things. Keywords: IOT, Fog Computing, Autonomous Vehicles.

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Ndungi, Rebeccah, and Bambang Sugiantoro. "THE FUTURE OF FOG COMPUTING IN KENYA." American Journal of Computing and Engineering 4, no. 2 (November 26, 2021): 1–9. http://dx.doi.org/10.47672/ajce.857.

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The study, titled "The Future of Fog Computing in Kenya," discusses fog computing, computing evolution, and the Internet of Things. It has layers ideal for fog networks. The architecture depicts the functions performed by each layer, the protocols, devices, and their functionality at various layers. Fog computing extends cloud computing and helps mitigate its difficulties. The study also goes forth to explain the various sectors where fog-computing technology is applied and the various merits associated with it. In order to improve present technology, this article will greatly help researchers in Kenya and beyond by giving ideas and suggestions as a way to focus on the COUNTRY’S VISION 2030.

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Al-Rubaie, Noor Razaq Obaied, Rafal Nader Neamah Kamel, and Raghda M. Alshemari. "Simulating fog computing in OMNeT++." Bulletin of Electrical Engineering and Informatics 12, no. 2 (April 1, 2023): 979–86. http://dx.doi.org/10.11591/eei.v12i2.4201.

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Fog computing is a technology architecture in which data from IoT devices is received in real time by a number of nodes. These nodes process the data they receive in real time, with millisecond reaction times. The nodes communicate analytical summary data to the cloud on a regular basis. Fog computing scenario demands higher output, reduced latency, and greater performance as demand and requirements for improving performance in IoT applications grow. The resources allocation in effective manner in the fog environment is also a major problem in IoT-fog computing. Fog computing has been considered as a necessity within several IoT resources domains. In this paper the proposed fog simulation environment is focused on IoT sensors, fog node, and cloud as the used network architecture. However, the network features are properly explored in the proposed system and they are evaluated based on the throughput, latency, and channel allocation.

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Bhavsar, Sejal Atit, and Kirit J. Modi. "Design and Development of Framework for Platform Level Issues in Fog Computing." International Journal of Electronics, Communications, and Measurement Engineering 8, no. 1 (January 2019): 1–20. http://dx.doi.org/10.4018/ijecme.2019010101.

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Fog computing is a paradigm that extends cloud computing services to the edge of the network. Fog computing provides data, storage, compute and application services to end users. The distinguishing characteristics of fog computing are its proximity to the end users. The application services are hosted on network edges like on routers, switches, etc. The goal of fog computing is to improve the efficiency and reduce the amount of data that needs to be transported to cloud for analysis, processing and storage. Due to heterogeneous characteristics of fog computing, there are some issues, i.e. security, fault tolerance, resource scheduling and allocation. To better understand fault tolerance, we highlighted the basic concepts of fault tolerance by understanding different fault tolerance techniques i.e. Reactive, Proactive and the hybrid. In addition to the fault tolerance, how to balance resource utilization and security in fog computing are also discussed here. Furthermore, to overcome platform level issues of fog computing, Hybrid fault tolerance model using resource management and security is presented by us.

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Shrestha, Hewan, Puviyarai T., Sana Sodanapalli, and Chandramohan Dhasarathan. "Evolution of Fog Computing Applications, Opportunities, and Challenges." International Journal of Fog Computing 4, no. 1 (January 2021): 1–17. http://dx.doi.org/10.4018/ijfc.2021010101.

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The emerging trend of internet of things in recent times is a blessing for various industries in the world. With the increasing amount of data generated by these devices, it makes it difficult for proper data flow and computation over the regular cloud architecture. Fog computing is a great alternative for cloud computing as it supports computation in devices over a large distributed geographical area, which is a plus for fog computing. Having applications in various domains including healthcare, logistics, design, marketing, manufacturing, and many more, fog computing is a great boon for the future. Evolving fog computing in various domains with different methods and techniques has shaped a clear future for it. Applicability of fog computing in vehicular communications and storage-as-a-service has made the term more popular these days. It is a review of all the possible fog computing-enabled applications and their future scope. It also prepares a basis for further research into fog computing domain-enabled services with low latency and minimum costs.

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Dhingra, Madhavi, Samta J. Goyal, and Rajeev Goyal. "Study on Fog Computing Enabled Data Processing." Electrical and Automation Engineering 3, no. 1 (September 6, 2024): 1–7. http://dx.doi.org/10.46632/eae/3/1/1.

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A study into the potential and advantages of fog computing in facilitating effective data processing at the network edge is presented in this abstract. The use of fog computing architectures and technologies to improve the effectiveness, speed, and scalability of data processing operations at the network edge is known as "fog computing enabled data processing." By bringing computer resources closer to data sources, enabling real-time processing, lowering latency, and optimizing bandwidth utilization, fog computing expands the possibilities of cloud computing. This method works especially well in situations where quick data processing, quick response times, and effective resource use are essential. By placing computational resources closer to data sources, fog computing systems improve data processing processes by lowering latency and enhancing overall system performance. The architecture of fog computing and data processing methods are covered in the study. The results open the door to novel applications and enhanced system performance across a range of areas by furthering our understanding of fog computing architectures and their function in enabling effective and safe data processing at the network edge.

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Nguyen, Binh Minh, Huynh Thi Thanh Binh, Tran The Anh, and Do Bao Son. "Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment." Applied Sciences 9, no. 9 (April 26, 2019): 1730. http://dx.doi.org/10.3390/app9091730.

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In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

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Panwar, Avnish. "Improved QoS in Fog Computing by Efficient Resource Allocation in an Internet of Things Environment." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 3 (December 17, 2018): 1082–89. http://dx.doi.org/10.17762/turcomat.v9i3.13897.

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Large-scale application migration to fog computing is now being seen in the IT industry. The IoT is a prototype for connecting everyday objects to the web, such as sensors, gadgets (including those used in healthcare), and smart cameras. By analysing the data produced by the device, the IoT proposes a paradigm that simplifies infrastructure management and disaster recovery, hence improving the quality of life for humans.Fog Computing is a new computing paradigm that has emerged in recent years to meet the needs of latency-sensitive, geographically dispersed applications with high computational requirements. Fog computing is popular because it may be deployed near to the IoT nodes. Fog computing expands the computational, storage, and network capabilities of the cloud and serves as an intermediary layer between IoT devices and sensors. The nature of fog nodes makes resource management more difficult in fog. With fog computing, services and resources may be made available outside the cloud, close to the end devices. The inclusion of several heterogeneous devices, some of which may be mobile, makes ensuring adequate quality of service (QoS) in a fog system very difficult. Several quality-of-service considerations are accounted for, and QoS-aware techniques are provided in various portions of the fog system. So, in this article, we take a look at what's been done so far to ensure quality of service in fog computing. FogQSYM (Fog Queuing System) is an analytical model for Fog applications that helps to partition the application into many tiers and efficiently distribute resources based on factors such as memory, network speed, and processing power. When the infrastructure is built with lightweight computing devices, effectively allocating resources in the fog environment becomes a challenge. In a unified fog computing setting, we discuss how to assign tasks and locate virtual machines.

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Artem, Volkov, Kovalenko Vadim, Ibrahim A. Elgendy, Ammar Muthanna, and Andrey Koucheryavy. "DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework." Future Internet 14, no. 1 (December 27, 2021): 13. http://dx.doi.org/10.3390/fi14010013.

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Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

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Javed, Zainab, and Waqas Mahmood. "A Survey Based Study on Fog Computing Awareness." International Journal of Information Technology and Computer Science 13, no. 2 (April 8, 2021): 49–62. http://dx.doi.org/10.5815/ijitcs.2021.02.05.

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In this day and age, the rise in technological advancements has the potential to improve and transform our lives every day. The rapid technology innovation can have a great impact on our business operations. Currently, Cloud computing services are popular and offer a wide range of opportunities for their customers. This paper presents a survey on a more recent computing architecture paradigm known as Fog Computing. Fog networking is a beneficial solution that offers the greater facility of data storage, enhanced computing, and networking resources. This new concept of fog complements cloud solution by facilitating its customers with better security, real-time analysis improved efficiency. To get a clear picture and understanding of how fog computing functions, we have performed an extensive literature review. We also presented a comparative study of fog computing with cloud and grid computing architectures. In this study, we have conducted a survey that led us to the conclusion that fog computing solution is still not applicable and implemented in most of the IoT industries due to the lack of awareness and the high architecture’s cost. Results of the study also indicate that optimized data storage and security are a few of the factors that can motivate organizations to implement the Fog computing architecture. Furthermore, the challenges related to fog computing solution are reviewed for progressive developments in the future.

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Alkayal, Entisar S., Nesreen M. Alharbi, Reem Alwashmi, and Waleed Ali. "Improving fog resource utilization with a dynamic round-robin load balancing approach." International Journal of ADVANCED AND APPLIED SCIENCES 11, no. 10 (October 2024): 196–205. http://dx.doi.org/10.21833/ijaas.2024.10.022.

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In fog computing, load balancing is an important research problem. It focuses on efficiently assigning tasks to fog nodes and minimizing delay in real-time applications. The traditional round-robin algorithm assigns tasks in a rotating manner among fog nodes, but it can send tasks to the cloud too early, leading to increased delays. To solve this problem, this paper introduces an improved round-robin algorithm that takes a dynamic approach to balancing the use of fog resources. The new model aims to improve load balancing in fog computing by distributing tasks more evenly among fog nodes, reducing dependence on cloud computing, and making better use of fog resources. The improved algorithm helps fog computing systems run more efficiently, reduces delays in real-time applications, and lowers the costs associated with cloud use. The results show that the proposed load balancing algorithm is key to optimizing fog resource use, improving system efficiency, and reducing task completion times in distributed computing systems.

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Volkov, A. N. "Routing Task in Dynamic Fog Computing Network." Proceedings of Telecommunication Universities 10, no. 4 (September 4, 2024): 27–37. http://dx.doi.org/10.31854/1813-324x-2024-10-4-27-37.

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Relevance. In the context of traffic growth, transition to IMT-2030 networks and Telepresence services, the tasks of efficient management of network and computing resources occupy a special place. Fog computing as the next stage of decomposition of the architecture of multi access edge cloud computing is designed to radically change the models and methods of distributing computing tasks, influencing, among other things, the user-operator interaction models. At the moment, there is a whole layer of scientific problems for revealing the possibilities of fog computing. They can be divided into a number of areas, such as: study of models and methods for implementing services of ultra-reliable and ultra-low latency communications, defined in IMT-2020 networks; study of models and methods for ensuring quality of service, including quality of experience; study of methods for live migration of microservices, as well as groups of typical microservices; study of models and methods for distributing resources of dynamic fog computing while ensuring the stability of fog computing forms (clusters, nebulae); one of the potentially effective areas is research in the field of combining federated learning with dynamic fog computing. This paper solves a routing problem that can be attributed to the direction of infrastructure research in dynamic fog computing.Problem statement: research and develop the effective methods for routes determination in a dynamic fog computing network, including tasks of migrating microservices of telepresence services. Goal of the work: research and development of an effective method for ways determination to migrate microservices in communication networks using fog computing technologies, which could take into account not only the characteristics of connections (edges of the network graph), but also the computing capabilities and limitations of fog computing devices, as well as their features - the dynamics of computing devices. Methods: in order to test the proposed method, the program model was developed in the NS-3 modeling environment. Result. Analysis of the results showed the effectiveness of the proposed method within the framework of the task and various application scenarios. Novelty. A microservice migration method has been developed as a new routing protocol in a dynamic fog computing environment, which differs from the known ones in that this method ensures the interaction of fog computing devices for migrating microservices, while achieving a reduction in energy consumption by fog computing devices by 41% and reducing the share of lost packages on average up to 34%. Practical significance: The developed method can be used to implement fog computing in conditions of mobility of end devices in order to achieve the requirements of promising services of IMT-2030 networks.

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Journal articles on the topic 'Fog Computing'

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