Advantages of Fog Computing: A Comparative Analysis with Cloud Computing for Enhanced Edge Computing Capabilities

Hunko, Mykhailo; Tkachov, Vitalii; Kovalenko, Andriy; Kuchuk, Heorhii

doi:10.1109/khpiweek61412.2023.10312948

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The architecture of fog computing encompasses computing, processing, storage, and networking services distributed across multiple end devices, marking a departure from traditional cloud computing [38]. It serves as a conduit that brings the cloud and end devices into closer proximity, achieved by deploying computing, storage, and networking resources in close proximity to end devices [39]. In essence, fog computing complements cloud computing by performing some processing tasks near the edge of the network, proximate to end-users.…”

Section: Fog Computingmentioning

confidence: 99%

“…In essence, fog computing complements cloud computing by performing some processing tasks near the edge of the network, proximate to end-users. This paradigm operates on a topology characterized by geographically dispersed nodes that execute computational tasks while providing networking and storage functions [39]. The devices integrated into the network are termed fog nodes and can be deployed wherever there is a network connection.…”

Section: Fog Computingmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Iot With Cloud, Fog, and Edge Computing: A Review

Kuchuk,

Malokhvii

2024

A.I.S.

View full text Add to dashboard Cite

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.

show abstract

Section: Fog Computingmentioning

confidence: 99%

Section: Fog Computingmentioning

confidence: 99%