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Youn 6 DONG-EUI Univ 7 March 11, 2019 9 Problem Statement of IoT integrated with Edge Computing 10 draft-hong-iot-edge-computing-02 12 Abstract 14 This document describes new challenges such as strict latency, 15 constrained network bandwidth and devices, intermittent connectivity, 16 privacy and security, for IoT services originated from the IoT 17 environmental changes. In order to address those new challenges, the 18 integration of Edge computing and IoT has been emerged as a promising 19 solution. This document discribes the concept of IoT integrated with 20 Edge computing as well as its use cases. It also discusses benefits 21 and challenges of Edge computing. The direction of Edge computing 22 for IoT should be discussed in the IETF/IRTF. 24 Status of This Memo 26 This Internet-Draft is submitted in full conformance with the 27 provisions of BCP 78 and BCP 79. 29 Internet-Drafts are working documents of the Internet Engineering 30 Task Force (IETF). Note that other groups may also distribute 31 working documents as Internet-Drafts. The list of current Internet- 32 Drafts is at https://datatracker.ietf.org/drafts/current/. 34 Internet-Drafts are draft documents valid for a maximum of six months 35 and may be updated, replaced, or obsoleted by other documents at any 36 time. It is inappropriate to use Internet-Drafts as reference 37 material or to cite them other than as "work in progress." 39 This Internet-Draft will expire on September 12, 2019. 41 Copyright Notice 43 Copyright (c) 2019 IETF Trust and the persons identified as the 44 document authors. All rights reserved. 46 This document is subject to BCP 78 and the IETF Trust's Legal 47 Provisions Relating to IETF Documents 48 (https://trustee.ietf.org/license-info) in effect on the date of 49 publication of this document. Please review these documents 50 carefully, as they describe your rights and restrictions with respect 51 to this document. Code Components extracted from this document must 52 include Simplified BSD License text as described in Section 4.e of 53 the Trust Legal Provisions and are provided without warranty as 54 described in the Simplified BSD License. 56 Table of Contents 58 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 59 2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3 60 3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3 61 3.1. Internet of Things (IoT) . . . . . . . . . . . . . . . . 3 62 3.2. IoT with Cloud computing . . . . . . . . . . . . . . . . 4 63 3.3. IoT Environmental changes . . . . . . . . . . . . . . . . 4 64 4. New challenges of IoT . . . . . . . . . . . . . . . . . . . . 4 65 4.1. Strict Latency . . . . . . . . . . . . . . . . . . . . . 5 66 4.2. Constrained Network Bandwidth . . . . . . . . . . . . . . 5 67 4.3. Constrained Devices . . . . . . . . . . . . . . . . . . . 5 68 4.4. Uninterrupted Services with Intermittent Connectivity to 69 the Cloud . . . . . . . . . . . . . . . . . . . . . . . . 5 70 4.5. Privacy and Security . . . . . . . . . . . . . . . . . . 5 71 5. IoT integrated with Edge Computing . . . . . . . . . . . . . 6 72 5.1. IoT Data in Edge Computing . . . . . . . . . . . . . . . 6 73 5.1.1. Data Storage . . . . . . . . . . . . . . . . . . . . 6 74 5.1.2. Data Processing . . . . . . . . . . . . . . . . . . . 6 75 5.1.3. Data Analyzing . . . . . . . . . . . . . . . . . . . 7 76 5.2. IoT Device Management in Edge Computing . . . . . . . . . 7 77 5.3. Edge Computing in IoT . . . . . . . . . . . . . . . . . . 8 78 6. Architecture of IoT integrated with Edge Computing . . . . . 8 79 7. Use Cases of Edge Computing in IoT . . . . . . . . . . . . . 10 80 7.1. Smart Constructions . . . . . . . . . . . . . . . . . . . 10 81 7.2. Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 10 82 7.3. Smart Water System . . . . . . . . . . . . . . . . . . . 11 83 7.4. Smart Buildings . . . . . . . . . . . . . . . . . . . . . 11 84 7.5. Smart Cities . . . . . . . . . . . . . . . . . . . . . . 11 85 7.6. Connected Vehicles . . . . . . . . . . . . . . . . . . . 11 86 8. Security Considerations . . . . . . . . . . . . . . . . . . . 11 87 9. References . . . . . . . . . . . . . . . . . . . . . . . . . 11 88 9.1. Normative References . . . . . . . . . . . . . . . . . . 11 89 9.2. Informative References . . . . . . . . . . . . . . . . . 12 90 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13 92 1. Introduction 94 Nowadays, most IoT services are based on Cloud computing since it can 95 provide virtually unlimited storage and processing power. The 96 integration of IoT with Cloud computing brings many advantages such 97 as flexibility, efficiency, and ability to store and use data. 99 However, the IoT environment is changing in such a way that vast 100 amounts of data are created at edge/local networks and about a half 101 of data is stored, processed, analyzed and acted upon close to the 102 data producer. Thus, emerging IoT services introduce new challenges 103 that cannot be addressed by today's centralized Cloud computing 104 models alone. 106 In this document, we describe new challenges for emerging IoT 107 services such as strict latency, constrained network bandwidth, 108 constrained devices, uninterrupted services with intermittent 109 connectivity, privacy and security due to the IoT environmental 110 changes. 112 In order to address those new challenges for IoT services, the 113 integration of Edge computing with IoT has been emerged as a 114 promising solution. In this document, we describe the concept of IoT 115 integrated with Edge computing as well as its use cases to discuss 116 the benefits and challenges of Edge computing mainly focused on IoT 117 data. The purpose of this document is to bring up the issues of Edge 118 computing for IoT services in IETF/IRTF. 120 2. Conventions and Terminology 122 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 123 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 124 document are to be interpreted as described in [RFC2119]. 126 3. Background 128 3.1. Internet of Things (IoT) 130 Since the phrase 'Internet of Things (IoT)' was coined by Kevin 131 Ashton in 1999 working on Radio-frequency identification (RFID) 132 technology at the Auto-ID Center of the Massachusetts Institute of 133 Technology (MIT) [Ashton], the concept of IoT has been that things 134 connected to the Internet can send and receive information collected 135 by sensors without human intervention, where things are various 136 embedded systems such as home appliances, mobile equipment, wearable 137 devices, etc. IoT has become one of the notable innovations playing 138 an important role in our daily lives [Lin]. 140 3.2. IoT with Cloud computing 142 IoT is generally characterized by real world small things that are 143 widely distributed but have limited storage and processing power. On 144 the other hand, Cloud computing is a predominant technology which has 145 virtually unlimited capacity in terms of storage and processing 146 power. Thus, the IoT with Cloud computing has been recognized as an 147 efficient way to overcome those IoT issues [Botta]. 149 The integration of IoT with Cloud computing brings many advantages 150 such as flexibility, efficiency, and capability to store and use IoT 151 data since Cloud computing has been a mature technology used to 152 provide computing services or IoT data storage over the Internet. 154 3.3. IoT Environmental changes 156 Now with IoT, we will reach the era of post-Clouds where 157 unprecedented volume and variety of data will be generated by things 158 at edge/local networks and many applications will be deployed on the 159 edge netwoks to consume these IoT data. Some of the applications may 160 need very short response times, some may contain personal data, and 161 others may generate vast amounts of data. Today's Cloud based 162 service models are not suitable for these applications. 164 It is predicted that by 2019, 45% of the data created in IoT will be 165 stored, processed, analyzed and acted close to, or at the edge of the 166 network and about 50 billion devices will connect to the Internet by 167 2020 [Evans]. So, moving all data from edge/local networks to the 168 cloud data center may not be an efficient way anymore to process vast 169 amounts of data. 171 In Cloud computing, users traditionally only consumed IoT data 172 through Cloud services. Now, however, users are also producing IoT 173 data with their mobile devices. This change requires more 174 functionality at edge/local networks [Shi]. 176 4. New challenges of IoT 178 As the IoT environment is changing in such a way that vast amounts of 179 data are created at edge/local networks and about a half of IoT data 180 is stored, processed, analyzed and acted close to the IoT data 181 producer, the emerging IoT services introduce new challenges that 182 cannot be addressed by today's centralized Cloud computing models 183 alone [Chiang]. 185 4.1. Strict Latency 187 Many industrial control systems, such as manufacturing systems, smart 188 grids, oil and gas systems, etc., often require end-to-end latency 189 between the sensor and control node remains within a few milliseconds 190 and some other IoT applications may require latency below a few tens 191 of milliseconds [Weiner]. These requirements for latency are 192 difficult to achieve by today's Cloud services. 194 4.2. Constrained Network Bandwidth 196 With an exponential rate, IoT data is generated by the massive things 197 connected into the Internet [Kelly] and extremely high network 198 bandwidth is required to send all the data to the cloud. Since 90% 199 of the IoT data generated by the endpoints will be stored and 200 processed locally rather than in the cloud, sending all the IoT data 201 to the cloud is often unnecessary. Or sometimes it is prohibited due 202 to regulations and data privacy concerns. 204 4.3. Constrained Devices 206 Many IoT things such as sensors, data collectors, actuators, 207 controllers, cars, drones, etc., have very limited hardware 208 resources. Many constrained IoT things cannot rely solely on their 209 limited resources to meet all their computing needs. It is not 210 practical to require everyone to interact directly with the cloud. 211 This is because these interactions require resource-intensive 212 processing and complex protocols. 214 4.4. Uninterrupted Services with Intermittent Connectivity to the Cloud 216 Cloud services will have difficulty providing uninterrupted services 217 to devices and systems such as vehicles, drones, and oil rigs that 218 have intermittent network connectivity to the cloud. 220 4.5. Privacy and Security 222 When IoT services are deployed at home, personal information can be 223 learned from detected usage data. For example, one can easily guess 224 whether a home is empty by reading its electricity or water usage. 225 In this case, the way to support services without exposing personal 226 information is a challenge. 228 When IoT data is sent to the cloud which is the end point in the 229 traditional end-to-end communication system, privacy of the data is a 230 challenge since it may travel across multiple routers to the cloud. 232 5. IoT integrated with Edge Computing 234 5.1. IoT Data in Edge Computing 236 As described in section 4, new challenges for supporting emerging IoT 237 services exist and Edge computing is one of the candidates to satisfy 238 these challenges. The concept of Edge computing is very intuitive. 239 The definition of Edge computing from ISO is 'Form of distributed 240 computing in which significant processing and data storage takes 241 place on nodes which are at the edge of the network' [ISO_TR]. And 242 the similar concept of Fog computing from Open Fog Consortium is 'A 243 horizontal, system-level architecture that distributes computing, 244 storage, control and networking functions closer to the users along a 245 cloud-to-thing continuum' [OpenFog]. Based on these definitions, we 246 can summarize a general philosophy of Edge computing as "Distribute 247 the required functions close to users and data". 249 As an aspect of IoT, Edge computing can provide many capabilities for 250 IoT services because IoT systems are based on sensors and actuator 251 devices in edge area and IoT data generated from sensors and actuator 252 devices are gathered through a gateway [ISO_TR]. Besides on IoT 253 data, other functions such as computing, control and network 254 functions are also very remarkable to support IoT services. In this 255 document, we will first concentrate on IoT data's aspect since the 256 benefit of Edge computing with IoT data is very big in use cases. 258 5.1.1. Data Storage 260 As tremendous IoT sensors, IoT actuators, and IoT devices are 261 connected to the Internet, IoT data volume from these things are 262 expected to increase explosively. And it is expected that much of 263 this high volume of IoT data is produced and/or consumed within edge/ 264 local networks, not to traverse through cloud networks. Until now, 265 most IoT data generated by IoT things is transferred and accumulated 266 in a remote server and storage of IoT data in a remote server is 267 expensive in transmission and storage. To mitigate the cost of 268 transmission and storage, it is required to divide IoT data into two 269 types of data; one is stored in edge/local networks and the other is 270 stored in cloud networks. The effect of Edge computing is revealed 271 with the handling IoT data in edge/local networks. 273 5.1.2. Data Processing 275 Until now, most network equipment such as routers, gateways, and 276 switches just forward data delivered from other network devices 277 without reading or modifying the content. In end-to-end 278 communication, data is acknowledged and proceed at a final 279 corresponding node. This is a typical usage of cloud computing and a 280 client-server communication. But, in the IoT environment, some IoT 281 data will be transferred to a cloud network and some will be 282 delivered to an edge node. The main reason of this separation is to 283 provide real-time processing and security enhancement in IoT. 284 Although there are many new technologies to reduce the delay and 285 transmission time, it is not easy to guarantee real-time processing. 286 The typical use case of this requirement is industrial Internet and 287 smart factory. Even though there are also several solutions to 288 provide security in IoT, the more basic rule is not to expose the 289 privacy data to public networks. If we separate IoT data into 290 private and non-private data, and keep private data within an edge/ 291 local network not to expose them in a public network, the security 292 and privacy in IoT cna be addressed by the separation. 294 5.1.3. Data Analyzing 296 If it is possible to separate IoT data in edge/local networks and 297 cloud networks, Edge computing can do more functions with IoT data in 298 edge/local networks. Because Edge computing has the capabilities to 299 handle IoT data in edge/local networks, it is also possible to 300 analyze IoT data to provide enhanced IoT services such as 301 intelligence. To analyze IoT data in an edge/local network, it is 302 required to have comparatively processing performance and this 303 requirement is not obstacle to deploy Edge computing due to the 304 development of H/W and S/W. 306 5.2. IoT Device Management in Edge Computing 308 If we consider new challenges of IoT services, not only the big 309 volume of IoT data but also the massive number of IoT things can be a 310 critical problem. Even though, we acknowledge this future problem, 311 the Internet architecture originally has the capability of 312 scalability and it will mitigate scalability issue in the IoT 313 environment. But, we cannot estimate the number of IoT things in the 314 future and we cannot guarantee the Internet architecture still 315 sustain the scalability issue in the IoT environment. Edge computing 316 will separate the scalability domain into edge/local networks and 317 outside network (e.g., cloud networks) and this separation of 318 scalability domain can provide more efficient way to tackle the 319 massive number of IoT things. 321 Because Edge computing can handle IoT data in an edge area and store 322 the IoT data in an edge node, and proceed IoT data if it is needed, 323 it can also separate the management domain into two parts. Edge 324 Computing can concentrate on management of IoT things in an edge area 325 and cooperate with the management of other outside networks. 327 5.3. Edge Computing in IoT 329 At an Edge computing discussion in IETF/IRTF meetings, the motivation 330 for IoT Edge computing is describe as follows; [IETF_Edge] 332 o Delay-sensitive 334 o High-volume 336 o Trust-sensitive 338 o (Intermittently) disconnected 340 o Energy-challenged 342 o Costly to transmit 344 As we described at previous sections, the above motivation for IoT 345 Edge computing could directly be benefits of Edge computing in the 346 IoT environment. The above motivation for IoT Edge computing is 347 mainly related to IoT data and other motivation for IoT Edge 348 computing can exist as other aspects of networking and communication. 350 In spite of its benefits, Edge computing in IoT services has 351 challenges such as programmability, naming, data abstraction, service 352 management, privacy and security and optimization metrics. 354 Edge computing can support IoT services independently of Cloud 355 computing. However, Edge computing is increasingly connected to 356 Cloud computing in most IoT systems for processing and storaging 357 data. Thus, the relationship of Edge Computing to Cloud Computing is 358 also another challenge of Edge Computing in IoT [ISO_TR]. 360 6. Architecture of IoT integrated with Edge Computing 362 When we consider the implementation and deployment of Edge computing, 363 it can be mainly referred to an IoT Gateway. The role of an IoT 364 Gateway is to provide multiple accesses to the heterogeneous IoT 365 devices/sensors, handling IoT data and delivering the IoT data to the 366 final destinations such as cloud networks. Similar to an IoT 367 Gateway, an Edge computing architecture as an edge computing node 368 provides downside connectivity to IoT sensors and devices (southbound 369 connectivity) and upside connectivity to cloud networks (northbound 370 connectivity). Also, the architecture provides the function of data 371 storage. Beside these functions, the Edge computing architecture 372 should provide the computing functions, such as data processing, data 373 analyzing, and additional function of intelligence. 375 +---------------------------+ 376 | | 377 | Cloud networks | 378 | | 379 +------------+--------------+ 380 | 381 | 382 +----------------------+-----------------------+ 383 | | | 384 | +---------------+---------------+ | 385 | | | | 386 | | Edge gateway function | | 387 | | (Northbound) | | 388 | | | | 389 | +---------------+---------------+ | 390 | | | 391 | +---------------+---------------+ | 392 | | | | 393 | | Edge computing function | | 394 | | (Storage, Processing, | | 395 | | Analyzing, Intelligence) | | 396 | | | | 397 | +---------------+---------------+ | 398 | | | 399 | +---------------+---------------+ | 400 | | | | 401 | | Edge networking function | | 402 | | (Southbound) | | 403 | | | | 404 | +-------------------------------+ | 405 | | 406 | Edge computing node | 407 +-----+-------+------+-------+-------+-------+-+ 408 | | | | | | 409 | | | | | | 410 +---+----+ | +---+----+ | +---+----+ | 411 |Sensor 1| | |Sensor 2| .|.. |Sensor n| | 412 +--------+ | +--------+ | +--------+ | 413 | | | 414 | | | 415 +----+---+ +-----+--+ +-----+--+ 416 |Device 1| |Device 2| .... |Device n| 417 +--------+ +--------+ +--------+ 419 Figure 1: Architecture of IoT integrated with Edge computing 421 It is expected that the Edge computing architecture will play an 422 important role to deploy new IoT services with integration to big 423 data and AI services. 425 7. Use Cases of Edge Computing in IoT 427 7.1. Smart Constructions 429 In traditional construction domain, there are many heavy equipment 430 and machineries and dangerous elements. Even though human pay 431 attention to risk elements, it is not easy to avoid them. If some 432 accidents are happened in a construction site, it causes a loss of 433 lives and property. Thus, there have been many trials in a 434 construction area to protect lives and property. 436 Measurements of noise, vibration, and gas in a construction area are 437 recorded on a remote server and reported to an inspector. Today, 438 data produced bu such measurements is collected by a gateway in a 439 construction area and transferred to a remote server. This incurs 440 transmission cost, e.g. over a LTE connection, and storage cost, e.g. 441 when using Amazon Web Services. When an inspector wants to 442 investigate some accidents, he checks the information stored in a 443 server. 445 If we deploy Edge computing in a construction area, the sensor data 446 can be processed and analyzed in a gateway located within or near a 447 construction area. And with the help of a statistical analysis or 448 machine learning technologies, we can predict future accidents in 449 advance and this prediction can be used as an alarm in a construction 450 area and a notification to an inspector. 452 To determine the exact cause of some accident, not only sensor data 453 but also audio and video data are transferred to a remote server or 454 cloud networks. In this case, the data volume of audio and video is 455 quite big and the cost of transmission can be a problem. If Edge 456 computing can predict the time of accident, it can reduce the data 457 volume of transmission; in general period, it can transmit the audio 458 and video data with a low resolution/degree and in emergent period, 459 it transmits the audio and video data with a high resolution/degree. 460 By adjusting the resolution/degree of audio and video data, it can 461 reduce transmission cost significantly. 463 7.2. Smart Grid 465 In future smart cities, Smart grids will be critical in ensuring 466 availability and efficiency for energy saving and control in city- 467 wide electricity management. Edge computing is expected to play a 468 significant role in those systems to improve transmission efficiency 469 of electricity, react and restore for power disturbances, reduce 470 operation cost, reuse renewable energy effectively, save energy of 471 electricity for future usage, and so on. In addition, Edge computing 472 can help monitoring power generation and power demands, and making 473 electrical energy storage decisions in the Smart grid system. 475 7.3. Smart Water System 477 The Water system is one of the most important aspects for building 478 smart city. Effective use of water, and cost-effective and 479 environment-friendly treatment of water are critical for water 480 control and management. This can be facilitated by Edge computing in 481 Smart water systems, to help monitor water consumption, 482 transportation, prediction of future water use, and so on. For 483 example, water harvesting and ground water monitoring will be 484 supported from Edge computing. Also, a Smart water system is able to 485 analyze collected information related to water control and 486 management, control the reduction of water losses and improve the 487 city water system through Edge computing. 489 7.4. Smart Buildings 491 [TBA] 493 7.5. Smart Cities 495 [TBA] 497 7.6. Connected Vehicles 499 [TBA] 501 8. Security Considerations 503 [TBA] 505 9. References 507 9.1. Normative References 509 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate 510 Requirement Levels", BCP 14, RFC 2119, 511 DOI 10.17487/RFC2119, March 1997, 512 . 514 9.2. Informative References 516 [Ashton] Ashton, K., "That Internet of Things thing", RFID J. vol. 517 22, no. 7, pp. 97-114, 2009. 519 [Lin] Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and W. 520 Zhao, "A survey on Internet of Things: Architecture, 521 enabling technologies, security and privacy, and 522 applications", IEEE Internet of Things J. vol. 4, no. 5, 523 pp. 1125-1142, Oct. 2017. 525 [Botta] Botta, A., Donato, W., Persico, V., and A. Pescape, 526 "Integration of Cloud computing and Internet of Things: A 527 survey", Future Gener. Comput. Syst. 56, pp. 684-700, 528 2016. 530 [Evans] Evans, D., "The Internet of Things: How the next evolution 531 of the Internet is changing everything", CISCO White 532 Paper vol. 1, pp. 1-11, 2011. 534 [Shi] Shi, W., Cao, J., Zhang, Q., Li, Y., and L. Xu, "Edge 535 computing: vision and challenges", IEEE Internet of Things 536 J. vol. 3, no. 5, pp. 637-646, Oct. 2016. 538 [Chiang] Chiang , M. and T. Zhang, "Fog and IoT: An overview of 539 research opportunities", IEEE Internet Things J. vol. 3, 540 no. 6, pp. 854-864, Dec. 2016. 542 [Weiner] Weiner, M., Jorgovanovic, M., Sahai, A., and B. Nikolie, 543 "Design of a low-latency, high-reliability wireless 544 communication system for control applications", IEEE Int. 545 Conf. Commun. (ICC) Sydney, NSW, Australia, pp. 3829-3835, 546 2014. 548 [Kelly] Kelly, R., "Internet of Things Data to Top 1.6 Zettabytes 549 by 2022", 550 https://campustechnology.com/articles/2015/04/15/internet- 551 of-thingsdata-to-top-1-6-zettabytes-by-2020.aspx , April 552 2016. 554 [ISO_TR] "Information Technology - Cloud Computing - Edge Computing 555 Landscape", ISO/IEC TR 23188 , April 2018. 557 [OpenFog] "OpenFog Reference Architecture for Fog Computing", 558 OpenFog Consortium , Feb. 2017. 560 [IETF_Edge] 561 Kutscher, D. and E. Schooler, "IoT Edge Computing 562 Discussion @ IETF-98", slides-99-t2trg-edge-computing- 563 summary-of-chicago-discussion-and-ideas-for-next- 564 steps-00 , Mar. 2017. 566 Authors' Addresses 568 Jungha Hong 569 ETRI 570 218 Gajeong-ro, Yuseung-Gu 571 Daejeon 34129 572 Korea 574 Phone: +82 42 860 0926 575 Email: jhong@etri.re.kr 577 Yong-Geun Hong 578 ETRI 579 218 Gajeong-ro, Yuseung-Gu 580 Daejeon 34129 581 Korea 583 Phone: +82 42 860 6557 584 Email: yghong@etri.re.kr 586 Joo-Sang Youn 587 DONG-EUI University 588 176 Eomgwangno Busan_jin_gu 589 Busan 614-714 590 Korea 592 Phone: +82 51 890 1993 593 Email: joosang.youn@gmail.com