Pith. sign in

REVIEW 2 major objections 1 minor 89 references

Fog Computing Applications: Taxonomy and Requirements

T0 review · 2 major / 1 minor · reviewed 2026-05-24 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A study of thirty fog computing applications identifies the requirements a general-purpose fog platform should support.

desk verdict This paper compiles a taxonomy and requirements list from 30 fog applications, but the undocumented selection process undercuts the claim that the requirements are general. read the letter →

arxiv 1907.11621 v1 pith:3SJAEZIJ submitted 2019-07-26 cs.DC

Arif Ahmed , HamidReza Arkian , Davaadorj Battulga , Ali J. Fahs , Mozhdeh Farhadi , Dimitrios Giouroukis , Adrien Gougeon , Felipe Oliveira Gutierrez
show 4 more authors
Guillaume Pierre Paulo R. Souza Jr Mulugeta Ayalew Tamiru Li Wu
This is my paper
classification cs.DC
keywords fogcomputingapplicationsrequirementstaxonomyIoTlatency-criticalaugmentedrealityplatformdesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper examines a representative set of thirty fog computing applications to determine the requirements that a general-purpose fog computing platform must fulfill. Fog computing was initially designed to address latency issues in applications such as augmented reality and to process large volumes of data from IoT devices locally rather than sending them to distant cloud centers. The work extends this by showing how a wider range of applications now impose their own demands on such platforms. A sympathetic reader would care because the resulting requirements list provides concrete guidance for building versatile fog systems that can handle varied real-world workloads.

What carries the argument

The taxonomy of fog computing applications together with the derived set of platform requirements extracted from the thirty cases.

What would settle it

Identification of a fog application whose requirements are not addressed by the derived set would show that the sample is not representative.

Watch

Extended reading notes

Core claim

By analyzing thirty representative fog computing applications the paper establishes the requirements that a general-purpose fog computing platform should support to accommodate both latency-critical uses and the broader opportunities created by local data handling.

Load-bearing premise

The thirty selected applications form a representative sample of the full range of fog computing applications and their requirements.

Editorial extensions

If this is right

  • A general-purpose fog platform must incorporate support for strict latency constraints found in applications such as augmented reality.
  • Platforms need mechanisms for local processing of high data volumes to limit transmission to remote clouds.
  • The platform must accommodate a wider variety of application types that extend past the original IoT and augmented-reality focus.
  • Common requirements across the applications allow one platform design to serve multiple distinct use cases.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The extracted requirements could inform efforts to create standardized interfaces for fog platforms.
  • This work suggests fog computing is evolving from a narrow solution into a general layer in distributed systems.
  • Designers of edge hardware may use the requirements to anticipate needed capabilities in future devices.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper claims to present a study of a representative set of 30 fog computing applications, along with a taxonomy of those applications and the requirements that a general-purpose fog computing platform should support to accommodate them.

Significance. If the selection of applications is shown to be representative and the analysis method is made explicit, the resulting requirements list could serve as a useful reference for fog platform designers in distributed systems. The absence of documented selection criteria, however, prevents the central claim from being evaluated or generalized.

major comments (2)
  1. [Abstract] Abstract: the central claim that the 30 applications are 'representative' and therefore yield general platform requirements is not supported by any description of selection criteria, search strings, inclusion/exclusion rules, domain-coverage metrics, or sampling frame; without these the derived requirements list cannot be verified as comprehensive or unbiased.
  2. The manuscript provides no section detailing the analysis method used to extract requirements from the 30 applications or any verification that the set spans the space of fog use-cases (e.g., industrial control, vehicular, or other domains beyond well-published AR/IoT examples).
minor comments (1)
  1. The taxonomy presentation would benefit from explicit mapping between individual applications and the derived requirements to allow readers to trace each requirement back to source applications.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting the need for explicit methodology. We agree that the manuscript lacks a documented description of how the 30 applications were selected and how requirements were extracted. We will add a new section detailing the selection process, inclusion criteria, and analysis method in the revised version. This will strengthen the claim of representativeness by making the approach verifiable.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the 30 applications are 'representative' and therefore yield general platform requirements is not supported by any description of selection criteria, search strings, inclusion/exclusion rules, domain-coverage metrics, or sampling frame; without these the derived requirements list cannot be verified as comprehensive or unbiased.

    Authors: We accept the point. The original manuscript stated the set was representative without providing the underlying selection process. In revision we will add a Methods section that describes the literature search strategy, search strings, inclusion/exclusion rules, and any domain-coverage considerations used to arrive at the 30 applications. The abstract will be updated to reference this section. revision: yes

  2. Referee: [—] The manuscript provides no section detailing the analysis method used to extract requirements from the 30 applications or any verification that the set spans the space of fog use-cases (e.g., industrial control, vehicular, or other domains beyond well-published AR/IoT examples).

    Authors: We agree that an explicit analysis-method section is missing. The requirements were obtained by systematically reviewing each application description for its functional and non-functional needs and then synthesizing the common requirements across the set. In the revision we will document this extraction procedure, provide a domain breakdown of the 30 applications (including any coverage of industrial control, vehicular, and other domains), and note any limitations in span. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Descriptive survey with no derivations, fits, or self-referential steps

full rationale

The paper performs a literature-based survey of 30 fog applications to extract platform requirements. No equations, parameters, predictions, or uniqueness theorems appear in the provided text or abstract. The central claim rests on the (potentially debatable) representativeness of the chosen set, but this is an external validity issue rather than any reduction of outputs to inputs by definition, fitting, or self-citation. No load-bearing step matches any of the enumerated circularity patterns.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

The central claim rests on the assumption that the chosen applications adequately represent fog computing use cases; no free parameters, axioms, or invented entities are introduced.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Fog Computing Applications: Taxonomy and Requirements." pith.science (2026). https://pith.science/paper/3SJAEZIJ

@misc{pith2026190711621,
  author       = {Pith},
  title        = {Pith review of: Fog Computing Applications: Taxonomy and Requirements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3SJAEZIJ}},
  note         = {Machine review of arXiv:1907.11621}
}
read the original abstract

Fog computing was designed to support the specific needs of latency-critical applications such as augmented reality, and IoT applications which produce massive volumes of data that are impractical to send to faraway cloud data centers for analysis. However this also created new opportunities for a wider range of applications which in turn impose their own requirements on future fog computing platforms. This article presents a study of a representative set of 30 fog computing applications and the requirements that a general-purpose fog computing platform should support.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

89 extracted references · 89 canonical work pages

  1. [1]

    All one needs to know about fog computing and related edge computing paradigms: A complete survey ,

    A. Yousefpour, C. Fung, T. Nguyen, K. Kadiyala, F. Jalali , A. Niakanlahiji, J. Kong, and J. P. Jue, “All one needs to know about fog computing and related edge computing paradigms: A complete survey ,” Journal of Systems Architecture, 2019

    work page 2019

  2. [2]

    Fog compu ting and its role in the internet of things,

    F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog compu ting and its role in the internet of things,” in Proc. workshop on Mobile computing, 2012

    work page 2012

  3. [3]

    Finding your way in the fog: Towards a compre hen- sive definition of fog computing,

    L. Vaquero, “Finding your way in the fog: Towards a compre hen- sive definition of fog computing,” ACM SIGCOMM Computer Communication Review, vol. 44, no. 5, 2014

    work page 2014

  4. [4]

    OpenFog reference architecture f or fog computing,

    OpenFog Consortium, “OpenFog reference architecture f or fog computing,” 2017, https://www.openfogconsortium.org/r a/

    work page 2017

  5. [5]

    IEEE 1934-2018 – IEEE standard for adoption of OpenFog ref- erence architecture for fog computing,

    IEEE Standards Association, “IEEE 1934-2018 – IEEE standard for adoption of OpenFog ref- erence architecture for fog computing,” 2018, https://standards.ieee.org/standard/1934-2018.html

    work page 1934

  6. [6]

    Decentralized computation offloading game for m obile cloud computing,

    X. Chen, “Decentralized computation offloading game for m obile cloud computing,” IEEE Transactions on Parallel and Dis- tributed Systems, vol. 26, no. 4, 2014

    work page 2014

  7. [7]

    Fog computing and the In- ternet of Things: a review,

    H. Atlam, R. Walters, and G. Wills, “Fog computing and the In- ternet of Things: a review,” Big Data and Cognitive Computing , vol. 2, no. 2, 2018

    work page 2018

  8. [8]

    Mahmud, R

    R. Mahmud, R. Kotagiri, and R. Buyya, Fog Computing: A Taxonomy, Survey and Future Directions. Springer, 2018

    work page 2018

Show all 89 references
  1. [9]

    Fog computing: Survey of trends, architectures, requirements , and research directions,

    R. K. Naha, S. K. Garg, D. Georgekopolous, P. P. Jayaraman, L. Gao, Y. Xiang, and R. Ranjan, “Fog computing: Survey of trends, architectures, requirements , and research directions,” CoRR, vol. abs/1807.00976, 2018. [Online]. A vailable: http://arxiv.org/abs/1807.00976

    2018

  2. [10]

    Live synthesis of vehicle-sourced data over 4G LTE,

    W. Hu, Z. Feng, Z. Chen, J. Harkes, P. Pillai, and M. Satya - narayanan, “Live synthesis of vehicle-sourced data over 4G LTE,” in Proc. ACM MSWiM, 2017

    2017

  3. [11]

    Towards wearable cognitive assistance,

    K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satya- narayanan, “Towards wearable cognitive assistance,” in Proc. MobiSys, 2014

    2014

  4. [12]

    Out of the fog: Use case scenarios (live video broadcasting),

    OpenFog Consortium, “Out of the fog: Use case scenarios (live video broadcasting),” 2018, http://www.fogguru.eu/tmp/OpenFog-Use-Cases.zip

    2018

  5. [13]

    Visual security and surveillance scenario (3.2),

    ——, “Visual security and surveillance scenario (3.2), ” 2017, https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf

    2017

  6. [14]

    Transportation scenario: Smart cars and traffic control (3.1),

    ——, “Transportation scenario: Smart cars and traffic control (3.1),” 2017, https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf

    2017

  7. [15]

    Out of the fog: Use case scenar- ios (high-scale drone package delivery),

    ——, “Out of the fog: Use case scenar- ios (high-scale drone package delivery),” 2018, http://www.fogguru.eu/tmp/OpenFog-Use-Cases.zip

    2018

  8. [16]

    Process manufacturing âĂŞ beverage industry ,

    ——, “Process manufacturing âĂŞ beverage industry ,” 20 18, http://www.fogguru.eu/tmp/OpenFog-Use-Cases.zip

  9. [17]

    Smart cities scenario (3.3),

    ——, “Smart cities scenario (3.3),” 2017, https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf

    2017

  10. [18]

    Real-time subsurface imaging,

    ——, “Real-time subsurface imaging,” 2018, http://www.fogguru.eu/tmp/OpenFog-Use-Cases.zip

    2018

  11. [19]

    Patient monitoring,

    ——, “Patient monitoring,” 2018, http://www.fogguru.eu/tmp/OpenFog-Use-Cases.zip

    2018

  12. [20]

    Autonomous driving,

    ——, “Autonomous driving,” 2018, http://www.fogguru.eu/tmp/OpenFog-Use-Cases.zip

    2018

  13. [21]

    Robotic SLAM: A review from fog computing and mobile edge computing perspective,

    S. Dey and A. Mukherjee, “Robotic SLAM: A review from fog computing and mobile edge computing perspective,” in Proc. MOBIQUITOUS, 2016

    2016

  14. [22]

    Enabling MaaS through a distributed IoT data fa bric, fog computing and network protocols,

    Cisco, “Enabling MaaS through a distributed IoT data fa bric, fog computing and network protocols,” White paper, 2018, https://alln-extcloud-storage.cisco.com/ciscoblogs/5c0a6ea91edbb.pdf

    2018

  15. [23]

    ARQuake: An outdoor/indoor augmented reality first person application,

    B. Thomas, B. Close, J. Donoghue, J. Squires, P. D. Bondi , M. Morris, and W. Piekarski, “ARQuake: An outdoor/indoor augmented reality first person application,” in Proc. Interna- tional Symposium on Wearable Computers, 2000

    2000

  16. [24]

    Survey on fog com- puting: architecture, key technologies, applications and open issues,

    P. Hu, S. Dhelim, H. Ning, and T. Qiu, “Survey on fog com- puting: architecture, key technologies, applications and open issues,” Journal of Network and Computer Applications , vol. 98, Nov. 2017

    2017

  17. [25]

    eW ALL: an intelligent caring home environment offering personalized context-awa re applications based on advanced sensing,

    S. Kyriazakos, M. Mihaylov, B. Anggorojati, A. Mihovsk a, R. Craciunescu, O. Fratu, and R. Prasad, “eW ALL: an intelligent caring home environment offering personalized context-awa re applications based on advanced sensing,” Wireless Personal Communications, vol. 87, no. 3, 2016

    2016

  18. [26]

    SSL: Smart street lamp based on fog computing for smarter cities,

    G. Jia et al., “SSL: Smart street lamp based on fog computing for smarter cities,” in IEEE Transactions on Industrial Informatics, vol. 14, no. 11, 2018

    2018

  19. [27]

    Design and im- plementation of a power consumption management system for smart home over fog-cloud computing,

    Y.-D. Chen, M. Z. Azhari, and J.-S. Leu, “Design and im- plementation of a power consumption management system for smart home over fog-cloud computing,” in Proc. International Conference on Intelligent Green Building and Smart Grid , 2018

    2018

  20. [28]

    Vehicular fog computing for video crowdsourcing: Applications, feasibility , and challenges,

    C. Zhu et al., “Vehicular fog computing for video crowdsourcing: Applications, feasibility , and challenges,” in IEEE Communica- tions Magazine, vol. 56, no. 10, Oct. 2018

    2018

  21. [29]

    Vehicular pollution monito ring and controlling using fog computing and clustering algorithm,

    R. Rajesh and V. Shijimol, “Vehicular pollution monito ring and controlling using fog computing and clustering algorithm, ” in International Journal of New Innovations in Engineering an d Technology, Mar. 2016. 15

    2016

  22. [30]

    FogLearn: Leveraging fog-based machine lea rn- ing for smart system big data analytics,

    R. K. Barik, R. Priyadarshini, H. Dubey , V. Kumar, and K. Mankodiya, “FogLearn: Leveraging fog-based machine lea rn- ing for smart system big data analytics,” International Journal of Fog Computing, vol. 1, no. 1, 2018

    2018

  23. [31]

    A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography ,

    H. A. A. Hamid, S. M. M. Rahman, M. S. Hossain, hmad Almogren, and A. Alamri, “A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography ,” IEEE Access, vol. 5, 2017

    2017

  24. [32]

    Smart water management platform,

    SW AMP Project, “Smart water management platform,” 201 8, swamp-project.org/communication/WRNP2018_lamina_SW AMP_EN.pdf

  25. [33]

    Sensing as a service model for smart cities supported by Int er- net of Things,

    C. Perera, A. Zaslavsky , P. Christen, and D. Georgakopo ulos, “Sensing as a service model for smart cities supported by Int er- net of Things,” Transactions on Emerging Telecommunications Technologies, 2013, https://doi.org/10.1002/ett.2704

    2013 doi

  26. [34]

    Dynamic urban surveillance video stream process ing using fog computing,

    N. Chen, Y. Chen, Y. You, H. Ling, P. Liang, and R. Zim- mermann, “Dynamic urban surveillance video stream process ing using fog computing,” in Proc. BigMM, 2016

    2016

  27. [35]

    Enabling GPU-assisted antivirus protec tion on android devices through edge offloading,

    D. Deyannis, R. Tsirbas, G. Vasiliadis, R. Montella, S. Kosta, and S. Ioannidis, “Enabling GPU-assisted antivirus protec tion on android devices through edge offloading,” in Proc. EdgeSys, 2018

    2018

  28. [36]

    Cachier: Edge-caching for recognition applications,

    U. Drolia, K. Guo, J. Tan, R. Gandhi, and P. Narasimhan, “Cachier: Edge-caching for recognition applications,” in Proc. ICDCS, 2017

    2017

  29. [37]

    Edgecourier: An e dge- hosted personal service for low-bandwidth document synchr o- nization in mobile cloud storage services,

    P. Hao, Y. Bai, X. Zhang, and Y. Zhang, “Edgecourier: An e dge- hosted personal service for low-bandwidth document synchr o- nization in mobile cloud storage services,” in Proc. ACM/IEEE SEC, 2017

    2017

  30. [38]

    CloudFog: Leveraging fog to extend c loud gaming for thin-client MMOG with high quality of service,

    Y. Lin and H. Shen, “CloudFog: Leveraging fog to extend c loud gaming for thin-client MMOG with high quality of service,” IEEE Transactions on Parallel and Distributed Systems , vol. 28, no. 2, 2017

    2017

  31. [39]

    Understanding performance of edge content caching for mob ile video streaming,

    G. Ma, Z. Wang, M. Zhang, J. Ye, M. Chen, and W. Zhu, “Understanding performance of edge content caching for mob ile video streaming,” IEEE Journal on Selected Areas in Communi- cations, vol. 35, no. 5, 2017

    2017

  32. [40]

    Mobile fog: A programming model for large-sc ale applications on the internet of things,

    K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder , and B. Koldehofe, “Mobile fog: A programming model for large-sc ale applications on the internet of things,” in Proc. workshop on Mobile cloud computing , 2013

    2013

  33. [41]

    Latency âĂŞ the sine qua non of AR and VR,

    M. Abrash, “Latency âĂŞ the sine qua non of AR and VR,” 201 2, http://blogs.valvesoftware.com/abrash/latency-the-sine-qua-non-of-ar-and-vr/

  34. [42]

    Delivers some home truths on latency ,

    OculusRift, “Delivers some home truths on latency ,” https://oculusrift-blog.com/john-carmacks-message-o f-latency/682/

  35. [43]

    Planetary-scale cloud latency audi ting plat- form,

    CLAudit Project, “Planetary-scale cloud latency audi ting plat- form,” 2016, http://bit.do/bS4js

    2016

  36. [44]

    Internet of things (iot) data continues to explode exponentially . who is using that data and how?

    Cisco Inc., “Internet of things (iot) data continues to explode exponentially . who is using that data and how?” 2019, https://blogs.cisco.com/datacenter/internet-of-things-iot-data-continues-to-explode-exponentially-who- is-using-that-data-and-how

    2019

  37. [45]

    Big, big, big data: The rise of HD video surveillance cameras spurs information explosion,

    IHS Inc, “Big, big, big data: The rise of HD video surveillance cameras spurs information explosion,” 2013, https://news.ihsmarkit.com/press-release/design-supply-chain-media/big-big-big-data-rise-hd-video-surv eillance-cameras-spurs-in

    2013

  38. [46]

    IoT devic es recognition through network traffic analysis,

    M. R. Shahid, G. Blanc, Z. Zhang, and H. Debar, “IoT devic es recognition through network traffic analysis,” in Proc. Big Data, Dec. 2018

    2018

  39. [47]

    The fog bal ancing: Load distribution for small cell cloud computing,

    J. Oueis, E. C. Strinati, and S. Barbarossa, “The fog bal ancing: Load distribution for small cell cloud computing,” in Proc. VTC, 2015

    2015

  40. [48]

    Fusion: managing health - care records at cloud scale,

    S. Basu, A. H. Karp, J. Li, J. Pruyne, J. Rolia, S. Singhal , J. Suermondt, and R. Swaminathan, “Fusion: managing health - care records at cloud scale,” Computer, vol. 45, no. 11, 2012

    2012

  41. [49]

    Healthcare under attack: What happens to stolen medical records?

    Trend Micro, “Healthcare under attack: What happens to stolen medical records?” 2016, https://www.trendmicro.com/vinfo/pl/security/news/cyber-attacks/healthcare-under-attack-stolen-medical -records

    2016

  42. [50]

    Fog compu ting: Mitigating insider data theft attacks in the cloud,

    S. J. Stolfo, M. B. Salem, and A. D. Keromytis, “Fog compu ting: Mitigating insider data theft attacks in the cloud,” in Proc. IEEE symposium on security and privacy workshops , 2012

    2012

  43. [51]

    IoT and cloud convergence : Opportunities and challenges,

    A. R. Biswas and R. Giaffreda, “IoT and cloud convergence : Opportunities and challenges,” in Proc. IEEE World Forum on Internet of Things , 2014

    2014

  44. [52]

    Resource aware placement of IoT application modules in fog-cloud computing paradigm,

    M. Taneja and A. Davy , “Resource aware placement of IoT application modules in fog-cloud computing paradigm,” in Proc. Symposium on Integrated Network and Service Management , 2017

    2017

  45. [53]

    Patient monitoring,

    OpenFog Consortium, “Patient monitoring,” 2019, https://www.openfogconsortium.org/wp-content/uploads/Patient-Monitoring-Short.pdf

    2019

  46. [54]

    Gre en iot: An investigation on energy saving practices for 2020 an d beyond,

    R. Arshad, S. Zahoor, M. A. Shah, A. Wahid, and H. Yu, “Gre en iot: An investigation on energy saving practices for 2020 an d beyond,” IEEE Access, vol. 5, 2017

    2020

  47. [55]

    Inte rcon- necting fog computing and microgrids for greening IoT,

    F. Jalali, A. Vishwanath, J. De Hoog, and F. Suits, “Inte rcon- necting fog computing and microgrids for greening IoT,” in Proc. ISGT-Asia, 2016

    2016

  48. [56]

    Buyya, M

    R. Buyya, M. Pathan, and A. Vakali, Content delivery networks . Springer, 2008, vol. 9

    2008

  49. [57]

    A taxonomy and survey of content delivery networks,

    A.-M. K. Pathan and R. Buyya, “A taxonomy and survey of content delivery networks,” Grid Computing and Distributed Systems Laboratory, University of Melbourne, Technical Report, vol. 4, 2007

    2007

  50. [58]

    A survey on content-oriented networking for efficient content delivery ,

    J. Choi, J. Han, E. Cho, T. Kwon, and Y. Choi, “A survey on content-oriented networking for efficient content delivery ,” IEEE Communications Magazine, vol. 49, no. 3, 2011

    2011

  51. [59]

    A c loud- oriented content delivery network paradigm: Modeling and a s- sessment,

    C. Papagianni, A. Leivadeas, and S. Papavassiliou, “A c loud- oriented content delivery network paradigm: Modeling and a s- sessment,” IEEE Transactions on Dependable and Secure Com- puting, vol. 10, no. 5, 2013

    2013

  52. [60]

    Social-aware edge cachin g in fog radio access networks,

    X. Wang, S. Leng, and K. Yang, “Social-aware edge cachin g in fog radio access networks,” IEEE Access, vol. 5, 2017

    2017

  53. [61]

    IEEE standard for adopti on of OpenFog reference architecture for fog computing,

    IEEE Standards Association, “IEEE standard for adopti on of OpenFog reference architecture for fog computing,” 2018 , https://standards.ieee.org/standard/1934-2018.html

    2018

  54. [62]

    L ow throughput networks for the IoT: Lessons learned from industrial implementations,

    G. Margelis, R. Piechocki, D. Kaleshi, and P. Thomas, “L ow throughput networks for the IoT: Lessons learned from industrial implementations,” in Proc. WF-IoT, 2015

    2015

  55. [63]

    Long- range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios,

    M. Centenaro, L. Vangelista, A. Zanella, and M. Zorzi, “ Long- range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios,” IEEE Wireless Communica- tions, vol. 23, no. 5, 2016

    2016

  56. [64]

    A survey on LPW A technology: LoRa and NB-IoT,

    R. S. Sinha, Y. Wei, and S.-H. Hwang, “A survey on LPW A technology: LoRa and NB-IoT,” ICT Express, vol. 3, no. 1, 2017

    2017

  57. [65]

    Generations of mobile commu nica- tion,

    G. S. Nitesh and A. Kakkar, “Generations of mobile commu nica- tion,” International Journal of Advanced Research in Computer Science and Software Engineering, 2016

    2016

  58. [66]

    Early progress on rel-16 bands for 5G,

    3GPP, “Early progress on rel-16 bands for 5G,” 2019, https://www.3gpp.org/news-events/2025-early-progress-on-rel-16-bands-for-5g

    2019

  59. [67]

    Real world 4G LTE vs. 5G test benchmark: 14x bandwidth),

    E. Kauppalehti, “Real world 4G LTE vs. 5G test benchmark: 14x bandwidth),” 2018, https://react-etc.net/entry/real-world-4g-lte-vs-5g -test-benchmark-14x-bandwidth

    2018

  60. [68]

    A comparison between IEEE 802.11 n and ac standards,

    R. B. M. Abdelrahman, A. B. A. Mustafa, and A. A. Osman, “A comparison between IEEE 802.11 n and ac standards,” 2015

    2015

  61. [69]

    A comparison of 802.11 ah and 802.15. 4 for IoT,

    N. Ahmed, H. Rahman, and M. I. Hussain, “A comparison of 802.11 ah and 802.15. 4 for IoT,” ICT Express , vol. 2, no. 3, 2016

    2016

  62. [70]

    IEEE 802.11ax the sixth generation of Wi-Fi,

    Cisco, “IEEE 802.11ax the sixth generation of Wi-Fi,” 2 018, https://www.cisco.com/c/dam/en/us/products/collateral/wireless/white-paper-c11-740788.pdf

  63. [71]

    I nternet of things (iot) communication protocols,

    S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “I nternet of things (iot) communication protocols,” in Proc. ICIT, 2017

    2017

  64. [72]

    Do we all really know what a fog node is? current trends towards an open definition,

    E. Marín-Tordera, X. Masip-Bruin, J. García-Almiñana , A. Jukan, G.-J. Ren, and J. Zhu, “Do we all really know what a fog node is? current trends towards an open definition,” Com- puter Communications, vol. 109, 2017

    2017

  65. [73]

    FOGG: A fog computing based gateway to integrate sensor networks to internet,

    S. S. Adhatarao, M. Arumaithurai, and X. Fu, “FOGG: A fog computing based gateway to integrate sensor networks to internet,” in Proc. ITC, vol. 2, 2017

    2017

  66. [74]

    Fog computing conceptual model, recommen- dations of the national institute of standards and technolo gy ,

    M. Iorga, L. Feldman, R. Barton, M. Martin, N. Goren, and C. Mahmoudi, “Fog computing conceptual model, recommen- dations of the national institute of standards and technolo gy ,” NIST Special Publication, 2018

    2018

  67. [75]

    Docker container deployment in fog computing infrastructures,

    A. Ahmed and G. Pierre, “Docker container deployment in fog computing infrastructures,” in Proc. IEEE EDGE, 2018

    2018

  68. [76]

    De- mocratizing the network edge,

    L. Peterson, T. Anderson, S. Katti, N. McKeown, G. Parul kar, J. Rexford, M. Satyanarayanan, O. Sunay , and A. Vahdat, “De- mocratizing the network edge,” SIGCOMM Comput. Commun. Rev., vol. 49, no. 2, May 2019

    2019

  69. [77]

    Proximity-aware traffic routing i n dis- tributed fog computing platforms,

    A. Fahs and G. Pierre, “Proximity-aware traffic routing i n dis- tributed fog computing platforms,” in Proc. IEEE/ACM CC- Grid, 2019

    2019

  70. [78]

    Single board computer,

    Wikipedia, “Single board computer,” 2019, https://en.wikipedia.org/wiki/Single-board_computer

    2019

  71. [79]

    Affordable and energy-efficient cloud computing clus- ters: The Bolzano Raspberry Pi cloud cluster experiment,

    P. Abrahamsson, S. Helmer, N. Phaphoom, L. Nicolodi, N. Preda, L. Miori, M. Angriman, J. Rikkilä, X. Wang, K. Ham- ily et al., “Affordable and energy-efficient cloud computing clus- ters: The Bolzano Raspberry Pi cloud cluster experiment,” i n 16 Proc. Intl. Conference on Cloud C...

    2013

  72. [80]

    Carbone, G

    P. Carbone, G. E. Gévay , G. Hermann, A. Katsifodimos, J. Soto, V. Markl, and S. Haridi, Large-Scale Data Stream Processing Systems. Springer, 2017

    2017

  73. [81]

    A data streaming per- formance evaluation using resource constrained edge devic e,

    H. Lee, J. Oh, K. Kim, and H. Yeon, “A data streaming per- formance evaluation using resource constrained edge devic e,” in Proc. ICTC, 2017

    2017

  74. [82]

    A survey of state manage ment in big data processing systems,

    Q.-C. To, J. Soto, and V. Markl, “A survey of state manage ment in big data processing systems,” The VLDB Journal , vol. 27, no. 6, Dec. 2018

    2018

  75. [83]

    Message-oriented middleware,

    E. Curry , “Message-oriented middleware,” Middleware for com- munications, 2004

    2004

  76. [85]

    A vailable: http://arxiv.org/abs/1609.01190

    [Online]. A vailable: http://arxiv.org/abs/1609.01190

  77. [86]

    A brief introduction on bi g data 5Vs characteristics and Hadoop technology ,

    Ishwarappa and J. Anuradha, “A brief introduction on bi g data 5Vs characteristics and Hadoop technology ,” Procedia Computer Science, vol. 48, 2015

    2015

  78. [87]

    Beyond the hype: Big data con- cepts, methods, and analytics,

    A. Gandomi and M. Haider, “Beyond the hype: Big data con- cepts, methods, and analytics,” International Journal of Infor- mation Management, vol. 35, no. 2, 2015

    2015

  79. [88]

    Data is giving rise to a new economy ,

    Economist, “Data is giving rise to a new economy ,” 2017, https://www.economist.com/briefing/2017/05/06/data-i s-giving-rise-to-a-new-economy

    2017

  80. [89]

    Council regulation (EU) no 269/2014,

    Council of European Union, “Council regulation (EU) no 269/2014,” 2014, https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:L:2016:119:FULL&from=EL

    2014

  81. [90]

    The treacherous 12 – top threats to cloud computing + industry insights,

    Cloud Security Alliance, “The treacherous 12 – top threats to cloud computing + industry insights,” 2017, https://downloads.cloudsecurityalliance.org/assets/research/top-threats/treacherous-12-top-threats.pdf

    2017

Pith tools

Reviewed May 24, 2026 · model on record in the stance chip above.