Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing

Ortiz, Guadalupe; Zouai, Meftah; Kazar, Okba; Garcia-de-Prado, Alfonso; Boubeta-Puig, Juan

doi:10.1016/j.csi.2021.103550

Cited by 41 publications

(21 citation statements)

References 47 publications

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“…The results of the Cold Start Challenge study in the fact that new users or new items entering the system adapt to changes in existing users or items [Kulkarni et al,2020]. Investigating the relationship between contextual information and how to use them in the recommendation process with MF methods [Bellogín et al,2021] and on the other hand applying the similarity measure in the preferences of the user [Chen et al,2021], demographic data [Natarajan et al,2020], social data [Boratto et al,2021,Ortiz et al,2022, etc. common ways to solve cold start.…”

Section: Rs Methods With Contextual Informationmentioning

confidence: 99%

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Multi-Objective Entropy FCSVD: Contextual Spectrum Analysis for Prototyping Recommender Systems

Mirabedini

Rodpysh

Banirostam

2022

Preprint

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In recent years explosive increase and variety of information due to the progress of the internet. Therefore, decision making in different fields has faced different challenges. Recommender systems provide personalized services to users by identifying users' interests, filtering information, and managing information, especially contextual information. Similarity criteria based on contextual information is a way to reduce the challenge of starting a cold. We argue that similarity is a vague concept, and we more realistic results in recommender systems using fuzzy logic. Applying fuzzy logic to contextual information can be an effective way to identify ambiguities and uncertainties in measuring the similarity of items and users. In this paper, we present a new multilevel contextual fuzzy similarity criterion with SVD matrix for recommending systems called FCSVD Which is based on a combination of similarity criteria PSS, fuzzy rules and contextual information. The results show an improvement in the accuracy of the method recommendations FVSVD compared to the CACF, CTLSVD, MF-LOD and CFSVD methods according to evaluation measure Precision, Recall, F1-score.

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Section: Rs Methods With Contextual Informationmentioning

confidence: 99%

“…Meanwhile,"context is any information that can be used to determine the status of an existing". In this way, Villegas et al The contextual information is divided into five general categories Individual context, Location context, Time context, Activity context, Relational context [Villegas et al,2018, Ortiz et al,2022.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Entropy FCSVD: Contextual Spectrum Analysis for Prototyping Recommender Systems

Mirabedini

Rodpysh

Banirostam

2022

Preprint

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“…In [35], the authors propose a Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing. In it, a three-tier software architecture is presented, split in the Cloud, the Fog, and the Edge layers.…”

Section: Definition Of Contextmentioning

confidence: 99%

Context Diffusion in Fog Colonies: Exploring Autonomous Fog Node Operation Using ECTORAS

Nikolopoulos

Νικολαϊδου

Voreakou

et al. 2022

IoT

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In Fog Computing, fog colonies are formed by nodes cooperating to provide services to end-users. To enable efficient operation and seamless scalability of fog colonies, decentralized control over participating nodes should be promoted. In such cases, autonomous Fog Nodes operate independently, sharing the context in which all colony members provide their services. In the paper, we explore different techniques of context diffusion and knowledge sharing between autonomous Fog Nodes within a fog colony, using ECTORAS, a publish/subscribe protocol. With ECTORAS, nodes become actively aware of their operating context, share contextual information and exchange operational policies to achieve self-configuration, self-adaptation and context awareness in an intelligent manner. Two different ECTORAS implementations are studied, one offering centralized control with the existence of a message broker, to manage colony participants and available topics, and one fully decentralized, catering to the erratic topology that Fog Computing may produce. The two schemes are tested as the Fog Colony size is expanding in terms of performance and energy consumption, in a prototype implementation based on Raspberry Pi nodes for smart building management.

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“…The scope of IoT has broadened since its advent and specifies a digital interconnection of devices and objects, capable of procuring and sharing information across platforms for added value [ 3 ]. The proliferation of IoT is consorted by an increased capacity, reduced communication cost, and astounding technological development.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing

Cited by 41 publications

References 47 publications

Multi-Objective Entropy FCSVD: Contextual Spectrum Analysis for Prototyping Recommender Systems

Multi-Objective Entropy FCSVD: Contextual Spectrum Analysis for Prototyping Recommender Systems

Context Diffusion in Fog Colonies: Exploring Autonomous Fog Node Operation Using ECTORAS

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

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