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介绍COGNIFOG框架：架构、构建模块及认知连接之路。

Presenting the COGNIFOG Framework: Architecture, Building Blocks and Road toward Cognitive Connectivity.

作者信息

Adame Toni, Amri Emna, Antonopoulos Grigoris, Azaiez Selma, Berne Alexandre, Camargo Juan Sebastian, Kakoulidis Harry, Kleisarchaki Sofia, Llamedo Alberto, Prasinos Marios, Psara Kyriaki, Shumaiev Klym

机构信息

Fundació i2CAT, Gran Capità 2-4, 08034 Barcelona, Spain.

CYSEC SA, EPFL Innovation Park Batiment A, 1015 Lausanne, Switzerland.

出版信息

Sensors (Basel). 2024 Aug 15;24(16):5283. doi: 10.3390/s24165283.

DOI:10.3390/s24165283
PMID:39204979
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11360436/
Abstract

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT-edge-cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments.

摘要

在普适计算时代，对实时数据处理、安全性和能源效率日益增长的需求所带来的挑战，需要创新的解决方案。雾计算的出现提供了一种很有前景的范式，通过将计算资源更靠近数据源来应对这些挑战。尽管雾计算具有诸多优势，但在异构环境中，其特性在资源分配与管理、资源供应、安全性和连接性等方面带来了挑战。本文介绍了COGNIFOG，这是一个目前正在开发的新型认知雾框架，其设计旨在利用智能、分散的决策过程、机器学习算法和分布式计算原理，以实现物联网边缘到云连续体的自主运行、适应性和可扩展性。通过集成认知能力，预计COGNIFOG将提高下一代计算环境的效率和可靠性，有可能在物理世界和数字世界之间提供无缝桥梁。使用一组有限的与连接性相关的COGNIFOG构建模块进行的初步实验结果表明，在基于现实世界的物联网场景中，网络资源利用率有了令人鼓舞的提升。总体而言，这项工作为该框架的进一步发展铺平了道路，其目标是使其更加智能、有弹性，并与下一代计算环境不断演变的需求保持一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/55beda7e2502/sensors-24-05283-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/fce029532a2e/sensors-24-05283-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/cb492396965a/sensors-24-05283-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/236d2046a2a7/sensors-24-05283-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/55beda7e2502/sensors-24-05283-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/fce029532a2e/sensors-24-05283-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/fcf38db55901/sensors-24-05283-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/cadd56158d2e/sensors-24-05283-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/f2b68935aea5/sensors-24-05283-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/cb492396965a/sensors-24-05283-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/236d2046a2a7/sensors-24-05283-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9afe/11360436/55beda7e2502/sensors-24-05283-g010.jpg

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