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The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (1809.09972v1)

Published 26 Sep 2018 in cs.DC

Abstract: The Internet of Things needs for computing power and storage are expected to remain on the rise in the next decade. Consequently, the amount of data generated by devices at the edge of the network will also grow. While cloud computing has been an established and effective way of acquiring computation and storage as a service to many applications, it may not be suitable to handle the myriad of data from IoT devices and fulfill largely heterogeneous application requirements. Fog computing has been developed to lie between IoT and the cloud, providing a hierarchy of computing power that can collect, aggregate, and process data from/to IoT devices. Combining fog and cloud may reduce data transfers and communication bottlenecks to the cloud and also contribute to reduced latencies, as fog computing resources exist closer to the edge. This paper examines this IoT-Fog-Cloud ecosystem and provides a literature review from different facets of it: how it can be organized, how management is being addressed, and how applications can benefit from it. Lastly, we present challenging issues yet to be addressed in IoT-Fog-Cloud infrastructures.

The Internet of Things, Fog and Cloud Continuum: Integration and Challenges

In recent years, the integration of the Internet of Things (IoT) with fog and cloud computing has emerged as a prominent field of paper in computer science. The paper "The Internet of Things, Fog and Cloud Continuum: Integration and Challenges" provides a comprehensive overview of this integration, analyzing its organization, management, and the potential applications it may enable. This essay aims to summarize the multifaceted aspects of this IoT-Fog-Cloud ecosystem as discussed by the authors, highlighting the current state of the art, associated challenges, and future directions for this integration.

Overview

The paper begins by explaining that the exponential growth of IoT devices is leading to unprecedented data generation at the network's edge, necessitating new models for data processing and storage. Traditional cloud computing, while effective, falls short when faced with the low-latency and location-awareness requirements needed by certain IoT applications. The paradigm of fog computing fills this gap, offering a distributed approach that positions computing resources closer to the IoT devices. This ensures reduced data transfer times and alleviates communication bottlenecks associated with cloud-only infrastructures.

Infrastructure and Management

The paper dissects the IoT-Fog-Cloud continuum into infrastructure components and management needs:

  • Infrastructure: It discusses the hierarchical organization of fog nodes, which can serve as intermediaries between the cloud and the edge. This hierarchical distribution is critical for managing resources efficiently and improving application latency and bandwidth usage. The fog nodes can integrate with cloudlets or micro data centers, providing scalable resources to the edge devices. This heterogeneity in infrastructure necessitates adaptable networking technologies to ensure seamless communication between IoT devices, fog nodes, and cloud services, considering multitudes of wireless protocols.
  • Management: Key challenges exist in resource allocation within this ecosystem, most notably due to its inherent heterogeneity and the dynamic nature of workloads. The authors argue for sophisticated scheduling algorithms capable of rapid adaptation to changing conditions and diverse application requirements. Serverless computing, notably through the deployment of lightweight microservices, is identified as a promising solution for optimizing resource use across distributed fog nodes. Additionally, overarching concerns such as energy efficiency, data management, and secure orchestration remain areas of active research.

Applications and Implications

The paper reviews applications enabled by the IoT-Fog-Cloud integration, emphasizing urban computing, mobile applications, and the Industrial Internet of Things (IIoT):

  • Urban Computing: Leveraging data from IoT networks embedded in smart city infrastructure, fog computing can facilitate real-time analytics crucial for urban planning, traffic management, and environmental monitoring, thus improving the urban quality of life.
  • Mobile Applications: In scenarios characterized by high mobility, such as vehicular networks or wearable tech, the agility provided by fog computing ensures that computational tasks can be efficiently offloaded, reducing latency and energy consumption on mobile devices.
  • Industrial IoT: The framework supports innovative manufacturing processes within Industry 4.0. By integrating IoT devices directly into production environments, fog computing enables real-time, robust data processing necessary for developing intelligent, interconnected industrial systems.

Future Directions

The paper outlines several avenues for future research and development. These include advancing middleware and APIs for enhanced interoperability between fog and cloud platforms, refining resource allocation strategies that consider environmental and application-specific variables, and developing comprehensive standards for security, privacy, and trust management. The heterogeneity of the IoT-Fog-Cloud ecosystem poses significant challenges that necessitate cross-disciplinary approaches to fully realize the potential benefits of this integration.

Conclusion

Overall, this paper provides a detailed examination of how the convergence of IoT, fog, and cloud computing presents both opportunities and challenges. The proposed IoT-Fog-Cloud continuum promises a more responsive, efficient, and scalable solution for supporting the growing demands of IoT applications. Continued research in this area will be instrumental in addressing the complexities of such heterogeneous and dynamic environments, ultimately driving innovation in real-world applications across various sectors.

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Authors (10)
  1. Luiz F. Bittencourt (7 papers)
  2. Roger Immich (6 papers)
  3. Rizos Sakellariou (13 papers)
  4. Nelson L. S. da Fonseca (7 papers)
  5. Edmundo R. M. Madeira (6 papers)
  6. Marilia Curado (4 papers)
  7. Leandro Villas (2 papers)
  8. Luiz da Silva (1 paper)
  9. Craig Lee (1 paper)
  10. Omer Rana (41 papers)
Citations (266)