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Bernardos 9 UC3M 10 November 4, 2019 12 RAW use cases 13 draft-bernardos-raw-use-cases-01 15 Abstract 17 The wireless medium presents significant specific challenges to 18 achieve properties similar to those of wired deterministic networks. 19 At the same time, a number of use cases cannot be solved with wires 20 and justify the extra effort of going wireless. This document 21 presents wireless use cases demanding reliable and available 22 behavior. 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 May 7, 2020. 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 . . . . . . . . . . . . . . . . . . . . . . . . 3 59 2. Amusement Parks . . . . . . . . . . . . . . . . . . . . . . . 4 60 2.1. Use Case Description . . . . . . . . . . . . . . . . . . 5 61 2.2. Specifics . . . . . . . . . . . . . . . . . . . . . . . . 5 62 2.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 6 63 2.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 6 64 3. Wireless for Industrial Applications . . . . . . . . . . . . 7 65 3.1. Use Case Description . . . . . . . . . . . . . . . . . . 7 66 3.2. Specifics . . . . . . . . . . . . . . . . . . . . . . . . 7 67 3.2.1. Control Loops . . . . . . . . . . . . . . . . . . . . 7 68 3.2.2. Unmeasured Data . . . . . . . . . . . . . . . . . . . 7 69 3.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 8 70 3.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 8 71 4. Pro Audio and Video . . . . . . . . . . . . . . . . . . . . . 9 72 4.1. Use Case Description . . . . . . . . . . . . . . . . . . 9 73 4.2. Specifics . . . . . . . . . . . . . . . . . . . . . . . . 9 74 4.2.1. Uninterrupted Stream Playback . . . . . . . . . . . . 9 75 4.2.2. Synchronized Stream Playback . . . . . . . . . . . . 10 76 4.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 10 77 4.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 10 78 5. Wireless Gaming . . . . . . . . . . . . . . . . . . . . . . . 10 79 5.1. Use Case Description . . . . . . . . . . . . . . . . . . 10 80 5.2. Specifics . . . . . . . . . . . . . . . . . . . . . . . . 11 81 5.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 11 82 5.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 11 83 6. UAV platooning and control . . . . . . . . . . . . . . . . . 12 84 6.1. Use Case Description . . . . . . . . . . . . . . . . . . 12 85 6.2. Specifics . . . . . . . . . . . . . . . . . . . . . . . . 12 86 6.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 13 87 6.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 13 88 7. Edge Robotics control . . . . . . . . . . . . . . . . . . . . 13 89 7.1. Use Case Description . . . . . . . . . . . . . . . . . . 13 90 7.2. Specifics . . . . . . . . . . . . . . . . . . . . . . . . 14 91 7.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 14 92 7.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 14 93 8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14 94 9. Security Considerations . . . . . . . . . . . . . . . . . . . 14 95 10. Informative References . . . . . . . . . . . . . . . . . . . 14 96 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16 98 1. Introduction 100 Based on time, resource reservation, and policy enforcement by 101 distributed shapers, Deterministic Networking provides the capability 102 to carry specified unicast or multicast data streams for real-time 103 applications with extremely low data loss rates and bounded latency, 104 so as to support time-sensitive and mission-critical applications on 105 a converged enterprise infrastructure. 107 Deterministic Networking in the IP world is an attempt to eliminate 108 packet loss for a committed bandwidth while ensuring a worst case 109 end-to-end latency, regardless of the network conditions and across 110 technologies. It can be seen as a set of new Quality of Service 111 (QoS) guarantees of worst-case delivery. IP networks become more 112 deterministic when the effects of statistical multiplexing (jitter 113 and collision loss) are mostly eliminated. This requires a tight 114 control of the physical resources to maintain the amount of traffic 115 within the physical capabilities of the underlying technology, e.g., 116 by the use of time-shared resources (bandwidth and buffers) per 117 circuit, and/or by shaping and/or scheduling the packets at every 118 hop. 120 Key attributes of Deterministic Networking include: 122 o time synchronization on all the nodes, 124 o centralized computation of network-wide deterministic paths, 126 o multi-technology path with co-channel interference minimzation, 128 o frame preemption and guard time mechanisms to ensure a worst-case 129 delay, and 131 o new traffic shapers within and at the edge to protect the network. 133 Wireless operates on a shared medium, and transmissions cannot be 134 fully deterministic due to uncontrolled interferences, including 135 self-induced multipath fading. RAW (Reliable and Available Wireless) 136 is an effort to provide Deterministic Networking on across a path 137 that include a wireless physical layer. Making Wireless Reliable and 138 Available is even more challenging than it is with wires, due to the 139 numerous causes of loss in transmission that add up to the congestion 140 losses and the delays caused by overbooked shared resources. 142 The wireless and wired media are fundamentally different at the 143 physical level, and while the generic Problem Statement [RFC8557] for 144 DetNet applies to the wired as well as the wireless medium, the 145 methods to achieve RAW necessarily differ from those used to support 146 Time-Sensitive Networking over wires. 148 So far, Open Standards for Deterministic Networking have prevalently 149 been focused on wired media, with Audio/Video Bridging (AVB) and Time 150 Sensitive Networking (TSN) at the IEEE and DetNet [RFC8655] at the 151 IETF. But wires cannot be used in a number of cases, including 152 mobile or rotating devices, rehabilitated industrial buildings, 153 wearable or in-body sensory devices, vehicle automation and 154 multiplayer gaming. 156 Purpose-built wireless technologies such as [ISA100], which 157 incorporates IPv6, were developped and deployed to cope for the lack 158 of open standards, but they yield a high cost in OPEX and CAPEX and 159 are limited to very few industries, e.g., process control, concert 160 instruments or racing. 162 This is now changing [I-D.thubert-raw-technologies]: 164 o IMT-2020 has recognized Ultra-Reliable Low-Latency Communication 165 (URLLC) as a key functionality for the upcoming 5G. 167 o IEEE 802.11 has identified a set of real-applications 168 [ieee80211-rt-tig] which may use the IEEE802.11 standards. They 169 typically emphasize strict end-to-end delay requirements. 171 o The IETF has produced an IPv6 stack for IEEE Std. 802.15.4 172 TimeSlotted Channel Hopping (TSCH) and an architecture 173 [I-D.ietf-6tisch-architecture] that enables Reliable and Available 174 Wireless (RAW) on a shared MAC. 176 This draft extends the "Deterministic Networking Use Cases" document 177 [RFC8578] and describes a number of additional use cases which 178 require "reliable/predictable and available" flows over wireless 179 links and possibly complex multi-hop paths called Tracks. This is 180 covered mainly by the "Wireless for Industrial Applications" use 181 case, as the "Cellular Radio" is mostly dedicated to the (wired) 182 transport part of a Radio Access Network (RAN). Whereas the 183 "Wireless for Industrial Applications" use case certainly covers an 184 area of interest for RAW, it is limited to 6TiSCH, and thus its scope 185 is narrower than the use cases described next in this document. 187 2. Amusement Parks 188 2.1. Use Case Description 190 The digitalization of Amusement Parks is expected to decrease 191 significantly the cost for maintaining the attractions. By 192 monitoring in real-time the machines, predictive maintenance will 193 help to reduce the repairing cost as well as the downtime. Besides, 194 the attractions may use wireless transmissions to interconnect 195 sensors and actuators, to privilege reconfigurability, and 196 standardization. 198 Attractions may rely on a large set of sensors and actuators, which 199 react in real time. Typical applications comprise: 201 o Emergency: safety has to be preserved, and must stop the 202 attraction when a failure is detected. 204 o Video: augmented and virtual realities are integrated in the 205 attraction. Wearable devices (e.g., glasses, virtual reality 206 headset) need to offload one part of the processing tasks. 208 o Real-time interactions: visitors may interact with an attraction, 209 like in a real-time video game. The visitors may virtually 210 interact with their environment, triggering actions in the real 211 world (through actuators) [robots]. 213 o Geolocation: visitors are tracked with a personal wireless tag so 214 that their user experience is improved. 216 o Predictive maintenance: statistics are collected to predict the 217 future failures, or to compute later more complex statistics about 218 the attraction's usage, the downtime, its popularity, etc. 220 2.2. Specifics 222 Amusement parks comprise a variable number of attractions, mostly 223 outdoor, over a large geographical area. The IT infrastructure is 224 typically multi-scale: 226 o Local area: the sensors and actuators controlling the attractions 227 are co-located. Control loops trigger only local traffic, with a 228 small end-to-end delay, typically inferior than 10 milliseconds, 229 like classical industrial systems [ieee80211-rt-tig]. 231 o Wearable devices are free to move in the park. They exchange 232 traffic locally (identification, personalization, multimedia) or 233 globally (billing, child tracking). 235 o Computationally intensive applications offload some tasks to a 236 cloud, and data analytics rely on a centralized infrastructure 237 (predictive maintenance, marketing). 239 2.3. The Need for Wireless 241 Amusement parks cover large areas and a global interconnection would 242 require a huge length of cables. Wireless also increases the 243 reconfigurability, enabling to update cheaply the attractions. The 244 frequent renewal helps to increase customer loyalty. 246 Some parts of the attraction are mobile, e.g., trucks of a roller- 247 coaster, robots. Since cables are prone to frequent failures in this 248 situation, wireless transmissions are recommended. 250 Wearable devices are extensively used for a user experience 251 personalization. They typically need to support wireless 252 transmissions. Personal tags may help to reduce the operating costs 253 [disney-VIP] and to increase the number of charged services provided 254 to the audience (VIP tickets, interactivity, etc.) Some applications 255 rely on more sophisticated wearable devices such as digital glasses 256 or Virtual Reality (VR) headsets for an immersive experience. 258 2.4. Requirements for RAW 260 The network infrastructure has to support heterogeneous traffic, with 261 very different critical requirements. Thus, flow isolation has to be 262 provided. 264 We have to schedule appropriately the transmissions, even in presence 265 of mobile devices. While the [I-D.ietf-6tisch-architecture] already 266 proposes an architecture for synchronized, IEEE Std. 802.15.4 Time- 267 Slotted Channel Hopping (TSCH) networks, 6TiSCH does not address 268 real-time IPv6 flows. RAW might provide mechanisms helping to 269 automatically adapt the network (i.e., schedule appropriately the 270 transmissions, across heterogeneous technologies, with strict SLA 271 requirements). 273 Nowadays, long-range wireless transmissions are used for best-effort 274 traffic, and [IEEE802.1TSN] is used for critical flows using Ethernet 275 devices. However, we need an IP enabled technology to interconnect 276 large areas, independent of the PHY and MAC layer to maximize the 277 compliancy. 279 We expect to deploy several different technologies (long vs. short 280 range) which have to cohabit in the same area. Thus, we need to 281 schedule appropriately the transmissions to limit the co-technology 282 interference, so that an end-to-end delay across multiple 283 technologies can be guaranteed. It is needed to understand which 284 technologies RAW will cover and how they can be used cohabitating in 285 the same area. 287 3. Wireless for Industrial Applications 289 3.1. Use Case Description 291 A major use case for networking in Industrial environments is the 292 control networks where periodic control loops operate between a 293 sensor that measures a physical property such as the temperature of a 294 fluid, a Programmable Logic Controller (PLC) that decides an action 295 such as warm up the mix, and an actuator that performs the required 296 action, e.g., inject power in a resistor. 298 3.2. Specifics 300 3.2.1. Control Loops 302 Process Control designates continuous processing operations, e.g., 303 heating Oil in a refinery or mixing drinking soda. Control loops in 304 the Process Control industry operate at a very low rate, typically 4 305 times per second. Factory Automation, on the other hand, deal with 306 discrete goods such as individual automobile parts, and requires 307 faster loops, in the order of 10ms. Motion control that monitors 308 dynamic activities may require even faster rates in the order of a 309 few ms. Finally, some industries exhibit hybrid behaviours, like 310 canned soup that will start as a process industry while mixing the 311 food and then operate as a discrete manufacturing when putting the 312 final product in cans and shipping them. 314 In all those cases, a packet must flow reliably between the sensor 315 and the PLC, be processed by the PLC, and sent to the actuator within 316 the control loop period. In some particular use cases that inherit 317 from analog operations, jitter might also alter the operation of the 318 control loop. A rare packet loss is usually admissible, but 319 typically 4 losses in a row will cause an emergency halt of the 320 production and incur a high cost for the manufacturer. 322 3.2.2. Unmeasured Data 324 A secondary use case deals with monitoring and diagnostics. This so- 325 called unmeasured data is essential to improve the performances of a 326 production line, e.g., by optimizing real-time processing or 327 maintenance windows using Machine Learning predictions. For the lack 328 of wireless technologies, some specific industries such as Oil and 329 Gas have been using serial cables, literally by the millions, to 330 perform their process optimization over the previous decades. But 331 few industries would afford the associated cost and the Holy Grail of 332 the Industrial Internet of Things is to provide the same benefits to 333 all industries, including SmartGrid, Transportation, Building, 334 Commercial and Medical. This requires a cheap, available and 335 scalable IP-based access technology. 337 Inside the factory, wires may already be available to operate the 338 Control Network. But unmeasured data are not welcome in that network 339 for a number of reasons. On the one hand it is rich and 340 asynchronous, meaning that using they may influence the deterministic 341 nature of the control operations and impact the production. On the 342 other hand, this information must be reported to the carpeted floor 343 over IP, which means the potential for a security breach via the 344 interconnection of the Operational Technology (OT) network with the 345 Internet technology (IT) network and possibly enable a rogue access. 347 3.3. The Need for Wireless 349 Ethernet cables used on a robot arm are prone to breakage after a few 350 thousands flexions, a lot faster than a power cable that is wider inn 351 diameter, and more resilient. In general, wired networking and 352 mobile parts are not a good match, mostly in the case of fast and 353 recurrent activities, as well as rotation. 355 When refurbishing older premises that were built before the Internet 356 age, power is usually available everywhere, but data is not. It is 357 often impractical, time consuming and expensive to deploy an Ethernet 358 fabric across walls and between buildings. Deploying a wire may take 359 months and cost tens of thousands of US Dollars. 361 Even when wiring exists, e.g., in an existing control network, 362 asynchronous IP packets such as diagnostics may not be welcome for 363 operational and security reasons (see Section 3.2.1). An alternate 364 network that can scale with the many sensors and actuators that equip 365 every robot, every valve and fan that are deployed on the factory 366 floor and may help detect and prevent a failure that could impact the 367 production. IEEE Std. 802.15.4 Time-Slotted Channel Hopping (TSCH) 368 [RFC7554] is a promising technology for that purpose, mostly if the 369 scheduled operations enable to use the same network by asynchronous 370 and deterministic flows in parallel. 372 3.4. Requirements for RAW 374 As stated by the "Deterministic Networking Problem Statement" 375 [RFC8557], a Deterministic Network is backwards compatible with 376 (capable of transporting) statistically multiplexed traffic while 377 preserving the properties of the accepted deterministic flows. While 378 the [I-D.ietf-6tisch-architecture] serves that requirement, the work 379 at 6TiSCH was focused on best-effort IPv6 packet flows. RAW should 380 be able to lock so-called hard cells for use by a centralized 381 scheduler, and program so-called end-to-end Tracks over those cells. 383 Over the course of the recent years, major Industrial Protocols, 384 e.g., [ODVA] with EtherNet/IP [EIP] and [Profinet], have been 385 migrating towards Ethernet and IP. In order to unleash the full 386 power of the IP hourglass model, it should be possible to deploy any 387 application over any network that has the physical capacity to 388 transport the industrial flow, regardless of the MAC/PHY technology, 389 wired or wireless, and across technologies. RAW mechanisms should be 390 able to setup a Track over a wireless access segment such as TSCH and 391 a backbone segment such as Ethernet or WI-Fi, to report a sensor data 392 or a critical monitoring within a bounded latency. 394 4. Pro Audio and Video 396 4.1. Use Case Description 398 Many devices support audio and video streaming by employing 802.11 399 wireless LAN. Some of these applications require low latency 400 capability. For instance, when the application provides interactive 401 play, or when the audio takes plays in real time (i.e. live) for 402 public addresses in train stations or in theme parks. 404 The professional audio and video industry ("ProAV") includes: 406 o Virtual Reality / Augmented Reality (VR/AR) 408 o Public address, media and emergency systems at large venues 409 (airports, train stations, stadiums, theme parks). 411 4.2. Specifics 413 4.2.1. Uninterrupted Stream Playback 415 Considering the uninterrupted audio or video stream, a potential 416 packet losses during the transmission of audio or video flows cannot 417 be tackled by re-trying the transmission, as it is done with file 418 transfer, because by the time the packet lost has been identified it 419 is too late to proceed with packet re-transmission. Buffering might 420 be employed to provide a certain delay which will allow for one or 421 more re-transmissions, however such approach is not efficient in 422 application where delays are not acceptable. 424 4.2.2. Synchronized Stream Playback 426 In the context of ProAV, latency is the time between the transmitted 427 signal over a stream and its reception. Thus, for sound to remain 428 synchronized to the movement in the video, the latency of both the 429 audio and video streams must be bounded and consistent. 431 4.3. The Need for Wireless 433 The devices need the wireless communication to support video 434 streaming via 802.11 wireless LAN for instance. 436 During the public address, the deployed announcement speakers, for 437 instance along the platforms of the train stations, need the wireless 438 communication to forward the audio traffic in real time. 440 4.4. Requirements for RAW 442 The network infrastructure needs to support heterogeneous types of 443 traffic (including QoS). 445 Content delivery with bounded (lowest possible) latency. 447 The deployed network topology should allow for multipath. This will 448 enable for multiple streams to have different (and multiple) paths 449 through the network to support redundancy. 451 5. Wireless Gaming 453 5.1. Use Case Description 455 The gaming industry includes [IEEE80211RTA] real-time mobile gaming, 456 wireless console gaming and cloud gaming. For RAW, wireless console 457 gaming is the most relevant one. We next summarize the three: 459 o Real-time Mobile Gaming: Different from traditional games, real 460 time mobile gaming is very sensitive to network latency and 461 stability. The mobile game can connect multiple players together 462 in a single game session and exchange data messages between game 463 server and connected players. Real-time means the feedback should 464 present on screen as users operate in game. For good game 465 experience, the end to end latency plus game servers processing 466 time should not be noticed by users as they play the game. 468 o Wireless Console Gaming: Playing online on a console has 2 types 469 of internet connectivity, which is either wired or Wi-Fi. Most of 470 the gaming consoles today support Wi-Fi 5. But Wi-Fi has an 471 especially bad reputation among the gaming community. The main 472 reasons are high latency, lag spikes and jitter. 474 o Cloud Gaming: The cloud gaming requires low latency capability as 475 the user commands in a game session need to be sent back to the 476 cloud server, the cloud server would update game context depending 477 on the received commands, and the cloud server would render the 478 picture/video to be displayed at user devices and stream the 479 picture/video content to the user devices. User devices might 480 very likely be connected wirelessly. 482 5.2. Specifics 484 While a lot of details can be found on [IEEE80211RTA], we next 485 summarize the main requirements in terms of latency, jitter and 486 packet loss: 488 o Intra BSS latency: less than 5 ms. 490 o Jitter variance: less than 2 ms. 492 o Packet loss: less than 0.1 percent. 494 5.3. The Need for Wireless 496 It is clear that gaming is evolving towards wireless, as players 497 demand being able to play anywhere. Besides, the industry is 498 changing towards playing from mobile phones, which are inherently 499 connected via wireless technologies. 501 5.4. Requirements for RAW 503 o Time sensitive networking extensions. Extensions, such as time- 504 aware shaping and redundancy (FRE) can be explored to address 505 congestion and reliability problems present in wireless networks. 507 o Priority tagging (Stream identification). One basic requirement 508 to provide better QoS for time-sensitive traffic is the capability 509 to identify and differentiate time-sensitive packets from other 510 (e.g. best-effort) traffic. 512 o Time-aware shaping. This capability (defined in IEEE 802.1Qbv) 513 consists of gates to control the opening/closing of queues that 514 share a common egress port within an Ethernet switch. A scheduler 515 defines the times when each queue opens or close, therefore 516 eliminating congestion and ensuring that frames are delivered 517 within the expected latency bounds. 519 o Dual/multiple link. Due to the competitions and interference are 520 common and hardly in control under wireless network, in order to 521 improve the latency stability, dual/multiple link proposal is 522 brought up to address this issue. Two modes are defined: 523 duplicate and joint. 525 o Admission Control. Congestion is a major cause of high/variable 526 latency and it is well known that if the traffic load exceeds the 527 capability of the link, QoS will be degraded. QoS degradation 528 maybe acceptable for many applications today, however emerging 529 time-sensitive applications are highly susceptible to increased 530 latency and jitter. In order to better control QoS, it is 531 important to control access to the network resources. 533 6. UAV platooning and control 535 6.1. Use Case Description 537 Unmanned Aerial Vehicles (UAVs) are becoming very popular for many 538 different applications, including military and civil use cases. The 539 term drone is commonly used to refer to a UAV. 541 UAVs can be used to perform aerial surveillance activities, traffic 542 monitoring (e.g., Spanish traffic control has recently introduced a 543 fleet of drones for quicker reactions upon traffic congestion related 544 events), support of emergency situations, and even transportation of 545 small goods. 547 UAVs typically have various forms of wireless connectivity: 549 o cellular: for communication with the control center, for remote 550 manuevering as well as monitoring of the drone; 552 o IEEE 802.11: for inter-drone communications (e.g., platooning) and 553 providing connectivity to other devices (e.g., acting as Access 554 Point). 556 6.2. Specifics 558 Some of the use cases/tasks involving drones require coordination 559 among drones. Others involve complex compute tasks that might not be 560 performed using the limited computing resources that a drone 561 typically has. These two aspects require continuous connectivity 562 with the control center and among drones. 564 Remote maneuvering of a drone might be performed over a cellular 565 network in some cased, however, there are situations that need very 566 low latencies and deterministic behavior of the connectivity. 568 Examples involve platooning of drones or share of computing resources 569 among drones (e.g., a drone offload some function to a neighboring 570 drone). 572 6.3. The Need for Wireless 574 UAVs cannot be connected through any type of wired media, so it is 575 obvious that wireless is needed. 577 6.4. Requirements for RAW 579 The network infrastructure is actually composed by the UAVs 580 themselves, requiring self-configuration capabilities. 582 Heterogeneous types of traffic need to be supported, from extremely 583 critical ones requiring ultra low latency and high resiliency, to 584 traffic requiring low-medium latency. 586 When a given service is decomposed into functions -- hosted at 587 different drones -- chained, each link connecting two given functions 588 would have a well-defined set of requirements (latency, bandwith and 589 jitter) that have to be met. 591 7. Edge Robotics control 593 7.1. Use Case Description 595 The Edge Robotics scenario consists of several robots, deployed in a 596 given area (for example a shopping mall), inter-connected via an 597 access network to a network's edge device or a data center. The 598 robots are connected to the edge so complex computational activities 599 are not executed locally at the robots, but offloaded to the edge. 600 This brings additional flexibility in the type of tasks that the 601 robots do, as well as reducing the costs of robot manufacturing (due 602 to their lower complexity), and enabling complex tasks involving 603 coordination among robots (that can be more easily performed if 604 robots are centrally controlled). 606 A simple example of the use of multiples robots is cleaning, 607 delivering of goods from warehouses to shops or video surveillance. 608 Multiple robots are simultaneously instructed to perform individual 609 tasks by moving the robotic intelligence from the robots to the 610 network's edge (e.g., data center). That enables easy 611 synchronization, scalable solution and on-demand option to create 612 flexible fleet of robots. 614 Robots would have various forms of wireless connectivity: 616 o IEEE 802.11: for connection to the edge and also inter-robot 617 communications (e.g., for coordinated actions). 619 o Cellular: as an additional communication link to the edge, though 620 primarily as backup, since ultra low latencies are needed. 622 7.2. Specifics 624 Some of the use cases/tasks involving robots might benefit from 625 decomposition of a service in small functions that are distributed 626 and chained among robots and the edge. These require continuous 627 connectivity with the control center and among drones. 629 Robot control is an activity requiring very low latencies between the 630 robot and the location where the control intelligence resides (which 631 might be the edge or another robot). 633 7.3. The Need for Wireless 635 Deploying robots in scenarios such as shopping malls for the 636 aforementioned applications cannot be done via wired connectivity. 638 7.4. Requirements for RAW 640 The network infrastructure needs to support heterogeneous types of 641 traffic, from robot control to video streaming. 643 When a given service is decomposed into functions -- hosted at 644 different robots -- chained, each link connecting two given functions 645 would have a well-defined set of requirements (latency, bandwidth and 646 jitter) that have to be met. 648 8. IANA Considerations 650 N/A. 652 9. Security Considerations 654 N/A. 656 10. Informative References 658 [disney-VIP] 659 Wired, "Disney's $1 Billion Bet on a Magical Wristband", 660 March 2015, 661 . 663 [EIP] http://www.odva.org/, "EtherNet/IP provides users with the 664 network tools to deploy standard Ethernet technology (IEEE 665 802.3 combined with the TCP/IP Suite) for industrial 666 automation applications while enabling Internet and 667 enterprise connectivity data anytime, anywhere.", 668 . 672 [I-D.ietf-6tisch-architecture] 673 Thubert, P., "An Architecture for IPv6 over the TSCH mode 674 of IEEE 802.15.4", draft-ietf-6tisch-architecture-28 (work 675 in progress), October 2019. 677 [I-D.thubert-raw-technologies] 678 Thubert, P., Cavalcanti, D., Vilajosana, X., and C. 679 Schmitt, "Reliable and Available Wireless Technologies", 680 draft-thubert-raw-technologies-03 (work in progress), July 681 2019. 683 [IEEE802.1TSN] 684 IEEE standard for Information Technology, "IEEE 685 802.1AS-2011 - IEEE Standard for Local and Metropolitan 686 Area Networks - Timing and Synchronization for Time- 687 Sensitive Applications in Bridged Local Area Networks". 689 [ieee80211-rt-tig] 690 IEEE, "IEEE 802.11 Real Time Applications TIG Report", 691 Nov. 2018, 692 . 694 [IEEE80211RTA] 695 IEEE standard for Information Technology, "IEEE 802.11 696 Real Time Applications TIG Report", Nov 2018. 698 [ISA100] ISA/ANSI, "ISA100, Wireless Systems for Automation", 699 . 701 [ODVA] http://www.odva.org/, "The organization that supports 702 network technologies built on the Common Industrial 703 Protocol (CIP) including EtherNet/IP.". 705 [Profinet] 706 http://us.profinet.com/technology/profinet/, "PROFINET is 707 a standard for industrial networking in automation.", 708 . 710 [RFC7554] Watteyne, T., Ed., Palattella, M., and L. Grieco, "Using 711 IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the 712 Internet of Things (IoT): Problem Statement", RFC 7554, 713 DOI 10.17487/RFC7554, May 2015, 714 . 716 [RFC8557] Finn, N. and P. Thubert, "Deterministic Networking Problem 717 Statement", RFC 8557, DOI 10.17487/RFC8557, May 2019, 718 . 720 [RFC8578] Grossman, E., Ed., "Deterministic Networking Use Cases", 721 RFC 8578, DOI 10.17487/RFC8578, May 2019, 722 . 724 [RFC8655] Finn, N., Thubert, P., Varga, B., and J. Farkas, 725 "Deterministic Networking Architecture", RFC 8655, 726 DOI 10.17487/RFC8655, October 2019, 727 . 729 [robots] Kober, J., Glisson, M., and M. Mistry, "Playing catch and 730 juggling with a humanoid robot.", 2012, 731 . 733 Authors' Addresses 735 Georgios Z. Papadopoulos 736 IMT Atlantique 737 Office B00 - 114A 738 2 Rue de la Chataigneraie 739 Cesson-Sevigne - Rennes 35510 740 FRANCE 742 Phone: +33 299 12 70 04 743 Email: georgios.papadopoulos@imt-atlantique.fr 745 Pascal Thubert 746 Cisco Systems, Inc 747 Building D 748 45 Allee des Ormes - BP1200 749 MOUGINS - Sophia Antipolis 06254 750 FRANCE 752 Phone: +33 497 23 26 34 753 Email: pthubert@cisco.com 754 Fabrice Theoleyre 755 CNRS 756 ICube Lab, Pole API 757 300 boulevard Sebastien Brant - CS 10413 758 Illkirch 67400 759 FRANCE 761 Phone: +33 368 85 45 33 762 Email: theoleyre@unistra.fr 763 URI: http://www.theoleyre.eu 765 Carlos J. Bernardos 766 Universidad Carlos III de Madrid 767 Av. Universidad, 30 768 Leganes, Madrid 28911 769 Spain 771 Phone: +34 91624 6236 772 Email: cjbc@it.uc3m.es 773 URI: http://www.it.uc3m.es/cjbc/