What is Edge Computing and Why does it matter in IoT?
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Edge computing is a decentralized IT architecture that enhances IoT efficiency by processing data locally to address issues of cost, bandwidth, and latency. It offers numerous benefits including quick responses, low operating costs, and improved security, while facilitating modern and legacy systems integration. The document outlines key opportunities and challenges in implementing edge computing, along with a case study demonstrating its applications in manufacturing and enterprise networks.
![REFERENCES
[1] M. Armbrust et al., “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, pp. 50–58, 2010.
[2] S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google file system,” ACM SIGOPS Oper. Syst. Rev., vol. 37,
no. 5, pp. 29–43, 2003.
[3] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM,
vol. 51, no. 1, pp. 107–113, 2008.
[4] K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The hadoop distributed file system,” in Proc. IEEE 26th
Symp. Mass Storage Syst. Technol. (MSST), Incline Village, NV, USA, 2010, pp. 1–10.
[5] M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark: Cluster computing with
working sets,” in Proc. 2nd USENIX Conf. Hot Topics Cloud Comput., vol. 10. Boston, MA, USA, 2010, p. 10.
[6] K. Ashton, “That Internet of Things thing,” RFiD J., vol. 22, no. 7, pp. 97–114, 2009.](https://image.slidesharecdn.com/edgecomputing-theroleofedgecomputinginiot-190705082945/85/What-is-Edge-Computing-and-Why-does-it-matter-in-IoT-11-320.jpg)
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What is Edge Computing and Why does it matter in IoT?
- 1. Definition, Vision, Opportunities, Challenges and Case Study THE ROLE OF EDGE COMPUTING IN IOT - Sameer Ahmed
- 2. Edge computing is a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet,” according to research firm IDC. Edge computing is a distributed, open IT architecture that features decentralized processing power, enabling mobile computing and Internet of Things (IoT) technologies. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data centre DEFINATION - WHAT IS EDGE COMPUTING?
- 3. Edge Computing helps enterprises address cost, bandwidth and latency issues across a broad range of IoT applications. Here are three key reasons why you need Edge Computing: Reduce the Amount of Data Transmitted and Stored in the Cloud Reduce the Lag Time in Data Transmission/Processing Reduce the Signal to Noise Ratio WHY DO WE NEED EDGE COMPUTING?
- 4. CHALLENGES AND OPPORTUNITIES Challenges General purpose computing on edge nodes Discovering edge nodes Partitioning and offloading tasks Uncompromising Quality-of-service and experience Using edge nodes publicly and securely Opportunities Standards, benchmarking and marketplace Frameworks and languages Lightweight libraries and algorithms Micro operating systems and virtualization Industry-academic collaborations
- 5. EDGE COMPUTING ARCHITECTURE Let’s see the big picture below to understand the main components of this architecture. The diagram above shows the edge side and cloud side. In the edge side the things could be sensors, actuators, devices and a crucial thing called gateway. This gateway has the responsibility to establish communications between things and cloud services and also orchestrate the actions between the things.
- 6. BENEFITS OF EDGE COMPUTING
- 7. EDGE COMPUTING BENEFITS - KEY DRIVERS FOR SMART MANUFACTURING There are many advantages to organizations when they adopt the edge platform, so let’s see how edge computing is proving to be beneficial for enterprises: Quick responses – Due to high computational power at the edge of a device, the time taken to process data and send back to the host is very quick. There is no trip to the cloud for analysis which makes the process faster and highly responsive. Low operating cost – There are almost no costs involved due to smaller operations and very low data management expenses. Security of the highest level This technology also allows filtering of sensitive information and transfers only the important data, which provides an adequate amount of security. A pocket-friendly solution –Edge computing performs data analytics at the device location which saves the final costs of an overall IT solution. A true connection between legacy and modern
- 8. FMCG – CPG Line Monitoring: As packaging lines carry variety of old and new machines, many aren’t designed to share data. The solution demanded retrofitting of hardware and custom dashboards to visualize multiple lines and machines. Altizon, along with the hardware partner, designed an integrated IoT solution. It deployed high quality wireless object detection sensors on case sealers, wrapping machines, and box printers to capture real-time operating pulse. The 24/7 real-time machine data at Datonis Edge helped analyze equipment failure & generate alerts. The business intelligence reports with machine idle time, breakdown reason codes, and overall productivity/OEE data helped management in better planning and addressing issues.. USE CASES OF EDGE COMPUTING - FMCG Read More - https://altizon.com/cpg-fmcg-iot-case-study/
- 9. Deployment of Enterprise & Campus Networks: A reliable network that’s readily available is crucial for large enterprises or campus networks. Numerous individuals using the same network can result in high latency for the end users. MEC resolves that issue of high latency. MEC in an enterprise network will allow copious employees simultaneous access to a company’s intranet, in order to complete mass training without halting network speed. USE CASES OF EDGE COMPUTING
- 10. Edge computing has been implemented in a variety of IIoT deployments; however, the need to modernize edge architectures became apparent with the emergence of cloud computing. The rapid decline in processor and memory cost enables more advanced decision-logic closer to where the data is created, at the edge. The industry has learned that a “one-size-fits-all” approach has never been adequate for IIoT. The next phase of the work will be to address these concerns in the Technical Report. While we have tried to lay out the significance of edge computing of future IIoT systems, we know it is a never-ending task as new IIoT applications and new considerations appear every day. We intend this paper to trigger more in-depth conversations and invite your participation. This is not the end; rather, a beginning. CONCLUSION
- 11. REFERENCES [1] M. Armbrust et al., “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, pp. 50–58, 2010. [2] S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google file system,” ACM SIGOPS Oper. Syst. Rev., vol. 37, no. 5, pp. 29–43, 2003. [3] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM, vol. 51, no. 1, pp. 107–113, 2008. [4] K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The hadoop distributed file system,” in Proc. IEEE 26th Symp. Mass Storage Syst. Technol. (MSST), Incline Village, NV, USA, 2010, pp. 1–10. [5] M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark: Cluster computing with working sets,” in Proc. 2nd USENIX Conf. Hot Topics Cloud Comput., vol. 10. Boston, MA, USA, 2010, p. 10. [6] K. Ashton, “That Internet of Things thing,” RFiD J., vol. 22, no. 7, pp. 97–114, 2009.