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Security and privacy- As we know in case of Fog computing we don't have to traverse the data through the public Internet which will reduce the chances of risk and increase the security and privacy. Sensitive data is processed locally.
Security and privacy- As we know in case of Fog computing we don't have to traverse the data through the public Internet which will reduce the chances of risk and increase the security and privacy. Sensitive data is processed locally.


Cost effectiveness- By this method we reduce the amount of data sent to the cloud. Which will lower the transmission cost and decrease the overall cost of the cloud storage and also the resources required for processing. <ref name=Fog Computing IOT>{{Cite web|url=https://iot.pdfpng.com/2024/03/fog-computing-in-internet-of-things.html|title=Fog Computing in Internet of Things|website=iot.pdfpng.com}}</ref>
Cost effectiveness- By this method we reduce the amount of data sent to the cloud. Which will lower the transmission cost and decrease the overall cost of the cloud storage and also the resources required for processing.
<ref name=FogComputing >{{Cite web|url=https://iot.pdfpng.com/2024/03/fog-computing-in-internet-of-things.html|title=Fog Computing in Internet of Things|website=iot.pdfpng.com}}</ref>


==See also==
==See also==

Revision as of 08:45, 22 March 2024

The OpenFog Consortium was an association of major tech companies aimed at standardizing and promoting fog computing.

Fog computing[1][2] or fog networking, also known as fogging,[3][4] is an architecture that uses edge devices to carry out a substantial amount of computation (edge computing), storage, and communication locally and routed over the Internet backbone.

Concept

In 2011, the need to extend cloud computing with fog computing emerged, in order to cope with huge number of IoT devices and big data volumes for real-time low-latency applications.[5] Fog computing, also called edge computing, is intended for distributed computing where numerous "peripheral" devices connect to a cloud. The word "fog" refers to its cloud-like properties, but closer to the "ground", i.e. IoT devices.[6] Many of these devices will generate voluminous raw data (e.g., from sensors), and rather than forward all this data to cloud-based servers to be processed, the idea behind fog computing is to do as much processing as possible using computing units co-located with the data-generating devices, so that processed rather than raw data is forwarded, and bandwidth requirements are reduced. An additional benefit is that the processed data is most likely to be needed by the same devices that generated the data, so that by processing locally rather than remotely, the latency between input and response is minimized. This idea is not entirely new: in non-cloud-computing scenarios, special-purpose hardware (e.g., signal-processing chips performing fast Fourier transforms) has long been used to reduce latency and reduce the burden on a CPU.

Fog networking consists of a control plane and a data plane. For example, on the data plane, fog computing enables computing services to reside at the edge of the network as opposed to servers in a data-center. Compared to cloud computing, fog computing emphasizes proximity to end-users and client objectives (e.g. operational costs, security policies,[7] resource exploitation), dense geographical distribution and context-awareness (for what concerns computational and IoT resources), latency reduction and backbone bandwidth savings to achieve better quality of service (QoS)[8] and edge analytics/stream mining, resulting in superior user-experience[9] and redundancy in case of failure while it is also able to be used in Assisted Living scenarios.[10][11][12][13][14][15]

Fog networking supports the Internet of Things (IoT) concept, in which most of the devices used by humans on a daily basis will be connected to each other. Examples include phones, wearable health monitoring devices, connected vehicle and augmented reality using devices such as the Google Glass.[16][17][18][19][20] IoT devices are often resource-constrained and have limited computational abilities to perform cryptography computations. A fog node can provide security for IoT devices by performing these cryptographic computations instead.[21]

SPAWAR, a division of the US Navy, is prototyping and testing a scalable, secure Disruption Tolerant Mesh Network to protect strategic military assets, both stationary and mobile. Machine-control applications, running on the mesh nodes, "take over", when Internet connectivity is lost. Use cases include Internet of Things e.g. smart drone swarms.[22]

The University of Melbourne is addressing the challenges of collecting and processing data from cameras, ECG devices, laptops, smartphones, and IoT devices with its project FogBus 2, which uses edge/fog and Oracle Cloud Infrastructure to process data in real-time.[23]

ISO/IEC 20248 provides a method whereby the data of objects identified by edge computing using Automated Identification Data Carriers (AIDC), a barcode and/or RFID tag, can be read, interpreted, verified and made available into the "Fog" and on the "Edge," even when the AIDC tag has moved on.[24]

History

The term "fog computing" was first developed by Cisco in 2012.[25] November 19, 2015, Cisco Systems, ARM Holdings, Dell, Intel, Microsoft, and Princeton University, founded the OpenFog Consortium to promote interests and development in fog computing.[26] Cisco Sr. Managing-Director Helder Antunes became the consortium's first chairman and Intel's Chief IoT Strategist Jeff Fedders became its first president.[27]

Definition

Both cloud computing and fog computing provide storage, applications, and data to end-users. However, fog computing is closer to end-users and has wider geographical distribution.[28]

'Cloud computing' is the practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer.[29]

Also known as edge computing or fogging, fog computing facilitates the operation of compute, storage, and networking services between end devices and cloud computing data centers.

National Institute of Standards and Technology in March 2018 released a definition of fog computing adopting much of Cisco's commercial terminology as NIST Special Publication 500-325, Fog Computing Conceptual Model, that defines fog computing as a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud computing or data center.[6] This paradigm supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated, distributed computing, storage, and network connectivity. Thus, fog computing is most distinguished by distance from the edge. In the theoretical model of fog computing, fog computing nodes are physically and functionally operative between edge nodes and centralized cloud.[30] Much of the terminology is undefined, including key architectural terms like "smart", and the distinction between fog computing from edge computing is not generally agreed.

Differences with edge computing and cloud computing

While edge computing is typically referred to the location where services are instantiated, fog computing implies distribution of the communication, computation, storage resources, and services on or close to devices and systems in the control of end-users.[31][32] Fog computing is a medium weight and intermediate level of computing power.[33] Rather than a substitute, fog computing often serves as a complement to cloud computing.[34] Fog computing is more energy-efficient than cloud computing.[35]

Standards

IEEE adopted the fog computing standards proposed by OpenFog Consortium.[36]

Where is Fog Computing Used

Low latency requirements- In IoT modern applications we require quick data analysis and action. Such as in the case of autonomous vehicles which are controlled automatically or monitoring systems which work based on real time. Fog computing will ensure the minimal delay due to proximity. The near the storage the lesser the time taken to send and retrieve data. 

Bandwidth conservation- We sometimes have a vast amount of data which cannot be sent to cloud due to bandwidth limitations at a time. With the help of Fog computing we can use Pre-processing of data. Because the data is already pre processed so it will reduce the amount of data that's going to be transmitted.

Geographical Distribution- In the cases where we need data in several different locations instead of a single location. We can use fog computing for this purpose. In IOT, applications like supply chain tracking or smart grid systems. Fog computing  will also provide localized processing. With help of which we can complete the specific needs of each area without the need to sending it to the main cloud.

Security and privacy- As we know in case of Fog computing we don't have to traverse the data through the public Internet which will reduce the chances of risk and increase the security and privacy. Sensitive data is processed locally.

Cost effectiveness- By this method we reduce the amount of data sent to the cloud. Which will lower the transmission cost and decrease the overall cost of the cloud storage and also the resources required for processing. 

[37]

See also

References

  1. ^ Bonomi, Flavio (September 19–23, 2011). "Connected Vehicles, the Internet of Things, and Fog Computing, The 8th ACM International Workshop on VehiculAr Inter-NETworking (VANET 2011), Las Vegas, NV, USA". www.sigmobile.org. Retrieved 2019-08-07.
  2. ^ Bonomi, Flavio (June 4–8, 2011). "Cloud and Fog Computing: Trade-offs and Applications. EON-2011 Workshop, International Symposium on Computer Architecture (ISCA 2011), San Jose, CA, USA". sites.google.com. Retrieved 2019-08-07.
  3. ^ "IoT, from Cloud to Fog Computing". blogs@Cisco - Cisco Blogs. 2015-03-25. Retrieved 2017-04-07.
  4. ^ "What Is Fog Computing? Webopedia Definition". www.webopedia.com. 18 December 2014. Retrieved 2017-04-07.
  5. ^ Bonomi, Flavio; Milito, Rodolfo; Zhu, Jiang; Addepalli, Sateesh (2012-08-17). "Fog computing and its role in the internet of things". Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM. pp. 13–16. doi:10.1145/2342509.2342513. ISBN 9781450315197. S2CID 207196503.
  6. ^ a b "Fog brings the cloud closer to the ground: Cisco innovates in fog computing". newsroom.cisco.com. Retrieved 2019-01-24.
  7. ^ Forti, Stefano; Ferrari, Gian-Luigi; Brogi, Antonio (January 2020). "Secure Cloud-Edge Deployments, with Trust". Future Generation Computer Systems. 102: 775–788. arXiv:1901.05347. doi:10.1016/j.future.2019.08.020.
  8. ^ Brogi, Antonio; Forti, Stefano (2017). "QoS-aware Deployment of IoT Applications Through the Fog" (PDF). IEEE Internet of Things Journal. PP (99): 1185–1192. doi:10.1109/JIOT.2017.2701408. ISSN 2327-4662. S2CID 2880664.
  9. ^ Cisco RFP-2013-078. Fog Computing, Ecosystem, Architecture and Applications: [1] Archived 2020-01-19 at the Wayback Machine Also available from the Internet Archive: [2].
  10. ^ Nikoloudakis, Y.; Panagiotakis, S.; Markakis, E.; Pallis, E.; Mastorakis, G.; Mavromoustakis, C. X.; Dobre, C. (November 2016). "A Fog-Based Emergency System for Smart Enhanced Living Environments". IEEE Cloud Computing. 3 (6): 54–62. doi:10.1109/mcc.2016.118. ISSN 2325-6095. S2CID 25475572.
  11. ^ "What Comes After the Cloud? How About the Fog?". IEEE Spectrum: Technology, Engineering, and Science News. Retrieved 2017-04-07.
  12. ^ "Is There a Buzz Over Fog Computing?". Channelnomics. Archived from the original on 2016-10-27. Retrieved 2017-04-07.
  13. ^ "New Solutions on the Horizon—"Fog" or "Edge" Computing?". The National Law Review. Retrieved 2017-04-07.
  14. ^ Cloud Evolution: Back to the Future?: [3] Archived 2015-10-09 at the Wayback Machine.
  15. ^ Arkian, Hamid Reza; Diyanat, Abolfazl; Pourkhalili, Atefe (2017-03-15). "MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications". Journal of Network and Computer Applications. 82: 152–165. doi:10.1016/j.jnca.2017.01.012.
  16. ^ Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. Fog Computing and its Role in the Internet of Things. In Proc of MCC (2012), pp. 13-16.[4].
  17. ^ Cisco-Delivers-Vision-of-Fog-Computing-to-Accelerate-Value-from-Billions-of-Connected-Devices: [5].
  18. ^ IoT: Out Of The Cloud & Into The Fog: [6] Archived 2015-12-23 at the Wayback Machine.
  19. ^ Distributed intelligence and IoT fog: [7].
  20. ^ Fog Computing Keeps Data Right Where the Internet of Things Needs It: [8].
  21. ^ Alrawais, Arwa; Alhothaily, Abdulrahman; Hu, Chunqiang; Cheng, Xiuzhen (March 2017). "Fog Computing for the Internet of Things: Security and Privacy Issues". IEEE Internet Computing. 21 (2): 34–42. doi:10.1109/MIC.2017.37. ISSN 1089-7801. S2CID 18074495.
  22. ^ [9].
  23. ^ Morris-Reade, Ryan (2022-02-16). "University of Melbourne uses Oracle Cloud to harness the power of IoT". SecurityBrief Australia. Retrieved 2022-04-18.
  24. ^ Huang, Dijiang; Wu, Huijun (2017-09-08). Mobile Cloud Computing: Foundations and Service Models. Morgan Kaufmann. ISBN 9780128096444.
  25. ^ Patel, Sandipkumar; Patel, Ritesh (2022). "Fog Computing: A Comprehensive Analysis of Simulation Tools, Applications and Research Challenges with Use Cases" (PDF). Journal of Engineering Science and Technology Review. 15 (3): 63–83. doi:10.25103/jestr.153.08. S2CID 251463942.
  26. ^ Janakiram, MSV (18 April 2016). "Is Fog Computing the Next Big Thing in the Internet of Things". Forbes Magazine. Retrieved 18 April 2016.
  27. ^ "Industrial Internet Consortium". www.iiconsortium.org.
  28. ^ F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ser. MCC'12. ACM, 2012, pp. 13–16.
  29. ^ "cloud computing | Definition of cloud computing in English by Oxford Dictionaries". Oxford Dictionaries | English. Archived from the original on September 27, 2016. Retrieved 2017-11-10.
  30. ^ Sarkar, S.; Misra, S. (2016). "Theoretical modelling of fog computing: a green computing paradigm to support IoT applications". IET Networks. 5 (2): 23–29. doi:10.1049/iet-net.2015.0034. ISSN 2047-4954.
  31. ^ Zhang, Chiang (2016). "Reliable capacity provisioning for distributed cloud/Edge/Fog computing applications". 2017 European Conference on Networks and Communications (EuCNC). Vol. 3. pp. 854–864. doi:10.1109/EuCNC.2017.7980667. hdl:11572/272828. ISBN 978-1-5386-3873-6. S2CID 19836815. {{cite book}}: |journal= ignored (help)
  32. ^ Ostberg; et al. (2017). "Reliable Capacity Provisioning for Distributed Cloud/Edge/Fog Computing Applications". Networks and Communications (EuCNC), 2017 European Conference on. 3 (6): 854–864. doi:10.1109/JIOT.2016.2584538. S2CID 207018722.
  33. ^ Perera, Charith; Qin, Yongrui; Estrella, Julio C.; Reiff-Marganiec, Stephan; Vasilakos, Athanasios V. (2017-10-09). "Fog Computing for Sustainable Smart Cities: A Survey" (PDF). ACM Computing Surveys. 50 (3): 32. arXiv:1703.07079. Bibcode:2017arXiv170307079P. doi:10.1145/3057266. ISSN 0360-0300. S2CID 12675271.
  34. ^ Matt, Christian (2018-04-19). "Fog Computing" (PDF). Business & Information Systems Engineering. 60 (4): 351–355. doi:10.1007/s12599-018-0540-6. ISSN 2363-7005. S2CID 51874973.
  35. ^ Sarkar, S.; Chatterjee, S.; Misra, S. (2018). "Assessment of the Suitability of Fog Computing in the Context of Internet of Things". IEEE Transactions on Cloud Computing. 6 (1): 46–59. doi:10.1109/TCC.2015.2485206. ISSN 2168-7161. S2CID 3823420.
  36. ^ "IEEE 1934-2018 - IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing". standards.ieee.org.
  37. ^ "Fog Computing in Internet of Things". iot.pdfpng.com.
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