Problem Statement of IoT integrated with Edge Computing
draft-hong-iot-edge-computing-01
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Authors | Jungha Hong , Yong-Geun Hong , Joo-Sang Youn | ||
Last updated | 2018-10-22 (Latest revision 2018-07-02) | ||
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draft-hong-iot-edge-computing-01
#x27;s centralized Cloud computing models alone [Chiang]. 4.1. Strict Latency Many industrial control systems, such as manufacturing systems, smart grids, oil and gas systems, etc., often require end-to-end latency between the sensor and control node remains within a few milliseconds and some other IoT applications may require latency below a few tens of milliseconds [Weiner]. These requirements for latency are difficult to achieve by today's Cloud services. Hong, et al. Expires April 25, 2019 [Page 4] Internet-Draft IoT with Edge computing October 2018 4.2. Constrained Network Bandwidth With an exponential rate, IoT data is generated by the massive things connected into the Internet [Kelly] and extremely high network bandwidth is required to send all the data to the cloud. Since 90% of the IoT data generated by the endpoints will be stored and processed locally rather than in the cloud, sending all the IoT data to the cloud is often unnecessary. Or sometimes it is prohibited due to regulations and data privacy concerns. 4.3. Constrained Devices Many IoT things such as sensors, data collectors, actuators, controllers, cars, drones, etc., have very limited hardware resources. Many constrained IoT things cannot rely solely on their limited resources to meet all their computing needs. It is not practical to require everyone to interact directly with the cloud. This is because these interactions require resource-intensive processing and complex protocols. 4.4. Uninterrupted Services with Intermittent Connectivity to the Cloud Cloud services will have difficulty providing uninterrupted services to devices and systems such as vehicles, drones, and oil rigs that have intermittent network connectivity to the cloud. 4.5. Privacy and Security When IoT services are deployed at home, personal information can be learned from detected usage data. For example, one can easily guess whether a home is empty by reading its electricity or water usage. In this case, the way to support services without exposing personal information is a challenge. When IoT data is sent to the cloud which is the end point in the traditional end-to-end communication system, privacy of the data is a challenge since it may travel across multiple routers to the cloud. 5. IoT integrated with Edge Computing 5.1. IoT Data in Edge Computing As described in section 4, new challenges for supporting IoT services exist and Edge computing is one of the candidates to satisfy these challenges. The concept of Edge computing is very intuitive. The definition of Edge computing from ISO is 'Form of distributed computing in which significant processing and data storage takes place on nodes which are at the edge of the network' [ISO_TR]. And Hong, et al. Expires April 25, 2019 [Page 5] Internet-Draft IoT with Edge computing October 2018 the similar concept of Fog computing from Open Fog Consortium is 'A horizontal, system-level architecture that distributes computing, storage, control and networking functions closer to the users along a cloud-to-thing continuum' [OpenFog]. Based on these definitions, we can summarize a general philosophy of IoT Edge computing as "Distribute the required functions close to users and data". As an aspect of IoT, Edge computing can provide many capabilities for IoT services because IoT systems are based on sensors and actuator devices in edge area and IoT data generated from sensors and actuator devices are gathered through a gateway [ISO_TR]. Besides on IoT data, other functions such as computing, control and network functions are also very remarkable to support IoT services. In this draft, we will first concentrate on IoT data's aspect because the benefit of Edge computing with IoT data is very big in a use cases. 5.1.1. Data Storage As tremendous IoT sensors, IoT actuators, and IoT devices are connected to the Internet, IoT data volume from these things are expected to increase explosively. And it is expected that much of this high volume of IoT data is produced and/or consumed within edge networks, not to traverse through cloud networks. Until now, mainly IoT data generated IoT things are transferred and accumulated in a remote server and to store IoT data in a remote server requires expensive cost of transmission and storage. To mitigate the cost of transmission and storage, it is required to divide IoT data into two types of data; one is stored in edge networks and the other is stored in cloud networks. The effect of Edge computing is revealed with the handling IoT data in edge networks. 5.1.2. Data Processing Until now, most network equipment such as routers, gateways, and switches just forward data delivered from other network devices, not to read the content or modify them. Based on end-to-end communication, data is acknowledged and proceed at a final corresponding node. This is a typical usage of cloud computing and client-server communication. But, in the IoT environment, some IoT data will be transferred to a cloud network and some IoT data will be delivered to an edge node/fog node. The main reason of this separation is to provide real-time processing and security enhancement. Although, there are many new technologies to reduce the delay time and transmission time, it is not easy to guarantee real- time processing. The typical use case of this requirement is Industrial Internet and smart factory. And even though, there are power functions to provide security, the more basic rule is that not to expose the privacy data to public networks. If we separate IoT Hong, et al. Expires April 25, 2019 [Page 6] Internet-Draft IoT with Edge computing October 2018 data into private data and non-private data and keep private data within an edge network, not to expose them in a public network, it will reduce many weak points of security. 5.1.3. Data Analyzing If it is possible to separate IoT data in edge networks and cloud networks, Edge computing can do more functions with IoT data in edge networks. Because Edge computing has the capabilities to handle IoT data in edge networks, it is also possible to analyze IoT data to provide enhanced IoT services such as intelligence. To analyze IoT data in an edge network, it is required to have comparatively processing performance and this requirement is not obstacle to deploy Edge computing due to the development of H/W and S/W. 5.2. IoT Device Management in Edge Computing If we consider new challenges of IoT services, not only the big volume of IoT data but also the massive number of IoT things can be a critical problem. Even though, we acknowledge this future problem, the Internet architecture originally has the capability of scalability and it will mitigate scalability issue in the IoT environment. But, we cannot estimate the number of IoT things in the future and we cannot guarantee the Internet architecture still sustain the scalability issue in the IoT environment. Edge computing will separate the scalability domain into edge networks and outside network (e.g., cloud networks) and this separation of scalability domain can provide more efficient way to tackle the massive number of IoT things. Because Edge computing can handle IoT data in an edge area and store the IoT data in an edge/fog node, and proceed IoT data if it is needed, it can also separate the management domain into two parts. Edge Computing can concentrate on management of IoT things in an edge area and cooperate with the management of other outside networks. 5.3. Edge Computing in IoT At an Edge computing discussion in IETF/IRTF meetings, the motivation for IoT Edge computing is describe as follows; [IETF_Edge] o Delay-sensitive o High-volume o Trust-sensitive o (Intermittently) disconnected Hong, et al. Expires April 25, 2019 [Page 7] Internet-Draft IoT with Edge computing October 2018 o Energy-challenged o Costly to transmit As we described at previous sections, the above motivation for IoT Edge computing could directly be benefits of Edge computing in the IoT environment. The above motivation for IoT Edge computing is mainly related to IoT data and other motivation for IoT Edge computing can be exist as other aspects of networking and communication. In spite of its benefits, Edge computing in IoT services has challenges such as programmability, naming, data abstraction, service management, privacy and security and optimization metrics. Edge computing can support IoT services independently of Cloud computing. However, Edge computing is increasingly connected to Cloud computing in most IoT systems for processing and data storage. Thus, the relationship of Edge Computing to Cloud Computing is also another challenge of Edge Computing in IoT [ISO_TR]. 6. Use Cases of Edge Computing in IoT 6.1. Smart Constructions In traditional construction domain, there are many heavy equipment and machineries and dangerous elements. Even though human pay attention to risk elements, it is not easy to avoid them. If some accidents are happened in a construction site, it causes a loss of lives and property. To protect lives and property, nowadays, there are many trials in a construction area. Measurements of noise, vibration, and gas in a construction area are recorded on a remote server and reported to an inspector. Today, much of this type of information is collected by a gateway in a construction area and transferred to a remote server. This incurs transmission cost, e.g. over a LTE connection, and storage cost, e.g. when using Amazon Web Services. When an inspector wants to investigate some accidents, he/she checks the information stored in a server. If we deploy Edge computing in a construction area, the sensor data can be processed and analyzed in a gateway located within a construction area or near a construction area. And with the help of a statistical analysis or machine learning technologies, we can predict future accidents in advance and this prediction can be used as an alarm in a construction area and a notification to an inspector. Hong, et al. Expires April 25, 2019 [Page 8] Internet-Draft IoT with Edge computing October 2018 To determine the exact cause of some accident, not only sensor data but also audio and video data are transferred to a remote server or cloud networks. In this case, the data volume of audio and video is quite big and the cost of transmission can be a problem. If Edge computing can predict the time of accident, it can reduce the data volume of transmission; in general period, it can transmit the audio and video data with a low resolution/degree and in emergent period, it transmits the audio and video data with a high resolution/degree. By adjusting the resolution/degree of audio and video data, it can reduce transmission cost significantly. 6.2. Smart Grid In future smart cities, Smart grids will be critical in ensuring availability and efficiency for energy saving and control in city- wide electricity management. Edge computing is expected to play a significant role in those systems to improve transmission efficiency of electricity, react and restore for power disturbances, reduce operation cost, reuse renewable energy effectively, save energy of electricity for future usage, and so on. In addition, Edge computing can help monitoring power generation and power demands, and making electrical energy storage decisions in the Smart grid system. 6.3. Smart Water System The Water system is one of the most important aspects for building smart city. Effective use of water, and cost-effective and environment-friendly treatment of water are critical for water control and management. This can be facilitated by Edge computing in Smart water systems, to help monitor water consumption, transportation, prediction of future water use, and so on. For example, water harvesting and ground water monitoring will be supported from Edge computing. Also, a Smart water system is able to analyze collected information related to water control and management, control the reduction of water losses and improve the city water system through Edge computing. 6.4. Smart Buildings [TBA] 6.5. Smart Cities [TBA] Hong, et al. Expires April 25, 2019 [Page 9] Internet-Draft IoT with Edge computing October 2018 6.6. Connected Vehicles [TBA] 7. Security Considerations [TBA] 8. References 8.1. Normative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, <https://www.rfc-editor.org/info/rfc2119>. 8.2. Informative References [Ashton] Ashton, K., "That Internet of Things thing", RFID J. vol. 22, no. 7, pp. 97-114, 2009. [Lin] Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and W. Zhao, "A survey on Internet of Things: Architecture, enabling technologies, security and privacy, and applications", IEEE Internet of Things J. vol. 4, no. 5, pp. 1125-1142, Oct. 2017. [Botta] Botta, A., Donato, W., Persico, V., and A. Pescape, "Integration of Cloud computing and Internet of Things: A survey", Future Gener. Comput. Syst. 56, pp. 684-700, 2016. [Evans] Evans, D., "The Internet of Things: How the next evolution of the Internet is changing everything", CISCO White Paper vol. 1, pp. 1-11, 2011. [Shi] Shi, W., Cao, J., Zhang, Q., Li, Y., and L. Xu, "Edge computing: vision and challenges", IEEE Internet of Things J. vol. 3, no. 5, pp. 637-646, Oct. 2016. [Chiang] Chiang , M. and T. Zhang, "Fog and IoT: An overview of research opportunities", IEEE Internet Things J. vol. 3, no. 6, pp. 854-864, Dec. 2016. Hong, et al. Expires April 25, 2019 [Page 10] Internet-Draft IoT with Edge computing October 2018 [Weiner] Weiner, M., Jorgovanovic, M., Sahai, A., and B. Nikolie, "Design of a low-latency, high-reliability wireless communication system for control applications", IEEE Int. Conf. Commun. (ICC) Sydney, NSW, Australia, pp. 3829-3835, 2014. [Kelly] Kelly, R., "Internet of Things Data to Top 1.6 Zettabytes by 2022", https://campustechnology.com/articles/2015/04/15/internet- of-thingsdata-to-top-1-6-zettabytes-by-2020.aspx , April 2016. [ISO_TR] "Information Technology - Cloud Computing - Edge Computing Landscape", ISO/IEC TR 23188 , April 2018. [OpenFog] "OpenFog Reference Architecture for Fog Computing", OpenFog Consortium , Feb. 2017. [IETF_Edge] Kutscher, D. and E. Schooler, "IoT Edge Computing Discussion @ IETF-98", slides-99-t2trg-edge-computing- summary-of-chicago-discussion-and-ideas-for-next- steps-00 , Mar. 2017. Authors' Addresses Jungha Hong ETRI 218 Gajeong-ro, Yuseung-Gu Daejeon 34129 Korea Phone: +82 42 860 0926 Email: jhong@etri.re.kr Yong-Geun Hong ETRI 218 Gajeong-ro, Yuseung-Gu Daejeon 34129 Korea Phone: +82 42 860 6557 Email: yghong@etri.re.kr Hong, et al. Expires April 25, 2019 [Page 11] Internet-Draft IoT with Edge computing October 2018 Joo-Sang Youn DONG-EUI University 176 Eomgwangno Busan_jin_gu Busan 614-714 Korea Phone: +82 51 890 1993 Email: joosang.youn@gmail.com Hong, et al. Expires April 25, 2019 [Page 12]