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

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 res...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 24; číslo 16; s. 5283
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 15.08.2024 MDPI
Predmet:	Algorithms Artificial intelligence Cloud computing cognitive connectivity Computer centers Connectivity Data analysis Data processing Distributed processing Edge computing Energy consumption fog computing Internet of Things IoT IoT-edge-cloud continuum service orchestration Software fog computing confidential computing edge computing IoT-edge-cloud continuum service orchestration intelligent systems cognitive connectivity IoT
ISSN:	1424-8220, 1424-8220
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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.
AbstractList	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. 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.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.
Author	Kakoulidis, Harry Amri, Emna Camargo, Juan Sebastian Llamedo, Alberto Prasinos, Marios Shumaiev, Klym Adame, Toni Antonopoulos, Grigoris Azaiez, Selma Berne, Alexandre Kleisarchaki, Sofia Psara, Kyriaki
AuthorAffiliation	6 Kentyou, Cours Berriat 93, 38000 Grenoble, France; sofia.kleisarchaki@kentyou.com 2 CYSEC SA, EPFL Innovation Park Batiment A, 1015 Lausanne, Switzerland; emna.amri@cysec.com 1 Fundació i2CAT, Gran Capità 2-4, 08034 Barcelona, Spain; juan.camargo@i2cat.net 9 Siemens Aktiengesellschaft, Werner-von-Siemens-Straße 1, 80333 Munich, Germany; klym.shumaiev@siemens.com 3 Netcompany-Intrasoft, Fragkokklisias 13, 15125 Maroussi, Greece; grigoris.antonopoulos@netcompany.com 7 ATOS IT, Ronda de Europa 5, 28760 Tres Cantos, Spain; alberto.llamedo@eviden.com 8 eBOS Technologies Limited, Arch. Makariou III and Mesaorias 1, Lakatamia 2322, Cyprus; kyriakip@ebos.com.cy 4 Commissariat à l’Énergie Atomique et aux Énergies Alternatives, Rue Leblanc 25, 75015 Paris, France; selma.azaiez@cea.fr (S.A.); alexandre.berne@cea.fr (A.B.) 5 Telematic Medical Applications, Skra 1-3, 17673 Kallithea, Greece; hariskakoulidis@tma.gr (H.K.); mprasinos@tma.gr (M.P.)
AuthorAffiliation_xml	– name: 2 CYSEC SA, EPFL Innovation Park Batiment A, 1015 Lausanne, Switzerland; emna.amri@cysec.com – name: 9 Siemens Aktiengesellschaft, Werner-von-Siemens-Straße 1, 80333 Munich, Germany; klym.shumaiev@siemens.com – name: 8 eBOS Technologies Limited, Arch. Makariou III and Mesaorias 1, Lakatamia 2322, Cyprus; kyriakip@ebos.com.cy – name: 3 Netcompany-Intrasoft, Fragkokklisias 13, 15125 Maroussi, Greece; grigoris.antonopoulos@netcompany.com – name: 1 Fundació i2CAT, Gran Capità 2-4, 08034 Barcelona, Spain; juan.camargo@i2cat.net – name: 6 Kentyou, Cours Berriat 93, 38000 Grenoble, France; sofia.kleisarchaki@kentyou.com – name: 4 Commissariat à l’Énergie Atomique et aux Énergies Alternatives, Rue Leblanc 25, 75015 Paris, France; selma.azaiez@cea.fr (S.A.); alexandre.berne@cea.fr (A.B.) – name: 5 Telematic Medical Applications, Skra 1-3, 17673 Kallithea, Greece; hariskakoulidis@tma.gr (H.K.); mprasinos@tma.gr (M.P.) – name: 7 ATOS IT, Ronda de Europa 5, 28760 Tres Cantos, Spain; alberto.llamedo@eviden.com
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SubjectTerms	Algorithms Artificial intelligence Cloud computing cognitive connectivity Computer centers Connectivity Data analysis Data processing Distributed processing Edge computing Energy consumption fog computing Internet of Things IoT IoT-edge-cloud continuum service orchestration Software
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Title	Presenting the COGNIFOG Framework: Architecture, Building Blocks and Road toward Cognitive Connectivity
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