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Cited by 7IoT and Edge/Fog ComputingNetwork Packet Processing and OptimizationEnergy Efficiency in Computing

Modeling an edge computing arithmetic framework for IoT environments

Open access
Roig, Pedro Juan, Alcaraz, Salvador, Gilly, Katja, Bernad, Cristina, Juiz, Carlos·2022·Sensors
DOI 10.3390/s22031084PMC PMC8839237
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Abstract

IoT environments are forecasted to grow exponentially in the coming years thanks to the recent advances in both edge computing and artificial intelligence. In this paper, a model of remote computing scheme is presented, where three layers of computing nodes are put in place in order to optimize the computing and forwarding tasks. In this sense, a generic layout has been designed so as to easily achieve communications among the diverse layers by means of simple arithmetic operations, which may result in saving resources in all nodes involved. Traffic forwarding is undertaken by means of forwarding tables within network devices, which need to be searched upon in order to find the proper destination, and that process may be resource-consuming as the number of entries in such tables grow. However, the arithmetic framework proposed may speed up the traffic forwarding decisions as relaying on integer divisions and modular arithmetic, which may result more straightforward. Furthermore, two diverse approaches have been proposed to formally describe such a design by means of coding with Spin/Promela, or otherwise, by using an algebraic approach with Algebra of Communicating Processes (ACP), resulting in a explosion state for the former and a specified and verified model in the latter.

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  • The proposed method's loss function is significantly lower than methods [3], [8], and [13] for high regularization and object representation parameters.

    “Furthermore, it is plausible that the loss of the suggested method is significantly lower for high regularization and object representation parameters in the comparison research.”

    From Development of edge computing and classification using The Internet of Things with incremental learning for object detection (2023) · cites this paper as [13]

    Section 6 describes an arithmetic framework for routing in IoT environments and says its AI components are assumed to be already trained. Searches for a loss function, regularization, object-representation parameters, and comparative loss results found no passage addressing the attributed claim. The paper’s discussion of optimal routing does not establish lower loss than other methods. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • The loss numbers of current methods [3], [8], and [13] are substantially larger because edge detection methodology is not used.

    “However, since edge detection methodology is not used in the current method [3,8,13], the loss numbers are substantially larger.”

    From Development of edge computing and classification using The Internet of Things with incremental learning for object detection (2023) · cites this paper as [13]

    The Introduction describes a theoretical arithmetic framework for packet forwarding, and Section 6 models edge and fog nodes. Neither the supplied passages nor searches for loss numbers, edge detection, and packet loss address a comparison of losses caused by not using edge detection methodology. Our copy therefore does not verify the attributed claim. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 45

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Authors
Roig, P. J., Alcaraz, S., Gilly, K., Bernad, C., & Juiz, C.
Affiliations
  • Universitat de Miguel Hernández d'Elx (ES)
  • Universitat de les Illes Balears (ES)
Corresponding author
  • Roig, Pedro Juan
  • Gilly, Katja
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-01-30
Volume
22
Issue
3
Pages
1084
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22031084
PMC
PMC8839237
OpenAlex
W4210276978
Cited by
7
References
45
Citations by year
2026: 1 · 2024: 1 · 2023: 5
FWCI
0.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
edge computing, Internet of Things, traffic forwarding, forwarding tables, integer division, modular arithmetic, resource optimization, Algebra of Communicating Processes, formal verification

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