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2 RAW G. Papadopoulos
3 Internet-Draft IMT Atlantique
4 Intended status: Standards Track P. Thubert
5 Expires: January 6, 2020 Cisco
6 F. Theoleyre
7 CNRS
8 CJ. Bernardos
9 UC3M
10 July 5, 2019
12 RAW use cases
13 draft-bernardos-raw-use-cases-00
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 deterministic wireless use cases, in continuation to the
22 DetNet Use Cases document.
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 January 6, 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 . . . . . . . . . . . . . . . . . . 4
61 2.2. Specificities . . . . . . . . . . . . . . . . . . . . . . 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. Specificities . . . . . . . . . . . . . . . . . . . . . . 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. Specificities . . . . . . . . . . . . . . . . . . . . . . 9
74 4.2.1. Uninterrupted Stream Playback . . . . . . . . . . . . 9
75 4.2.2. Synchronized Stream Playback . . . . . . . . . . . . 9
76 4.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 10
77 4.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 10
78 5. Gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
79 5.1. Use Case Description . . . . . . . . . . . . . . . . . . 10
80 5.2. Specificities . . . . . . . . . . . . . . . . . . . . . . 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. Specificities . . . . . . . . . . . . . . . . . . . . . . 12
86 6.3. The Need for Wireless . . . . . . . . . . . . . . . . . . 12
87 6.4. Requirements for RAW . . . . . . . . . . . . . . . . . . 13
88 7. Edge Robotics control . . . . . . . . . . . . . . . . . . . . 13
89 7.1. Use Case Description . . . . . . . . . . . . . . . . . . 13
90 7.2. Specificities . . . . . . . . . . . . . . . . . . . . . . 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. Scheduling transmissions enables to
136 alleviate those effects by leveraging diversity in the spatial, time
137 and frequency domains, enabling Reliable and Available Wireless
138 (RAW).
140 The wireless and wired media are fundamentally different at the
141 physical level, and while the generic Problem Statement [RFC8557] for
142 DetNet applies to the wired as well as the wireless medium, the
143 methods to achieve RAW necessarily differ from those used to support
144 Time-Sensitive Networking over wires.
146 So far, Open Standards for Deterministic Networking have prevalently
147 been focused on wired media, with Audio/Video Bridging (AVB) and Time
148 Sensitive Networking (TSN) at the IEEE and DetNet
149 [I-D.ietf-detnet-architecture] at the IETF. But wires cannot be used
150 in a number of cases, including mobile or rotating devices,
151 rehabilitated industrial buildings, wearable or in-body sensory
152 devices, vehicle automation and multiplayer gaming.
154 Purpose-built wireless technologies such as [ISA100], which
155 incorporates IPv6, were developped and deployed to cope for the lack
156 of open standards, but they yield a high cost in OPEX and CAPEX and
157 are limited to very few industries, e.g., process control, concert
158 instruments or racing.
160 This is now changing:
162 o IMT-2020 has recognized Ultra-Reliable Low-Latency Communication
163 (URLLC) as a key functionality for the upcoming 5G,
165 o IEEE 802.11 has identified a set of real-applications
166 [ieee80211-rt-tig] which may use the IEEE802.11 standards. They
167 typically emphasize strict end-to-end delay requirements.
169 o the IETF has produced an IPv6 stack for IEEE Std. 802.15.4
170 TimeSlotted Channel Hopping (TSCH) and an architecture
171 [I-D.ietf-6tisch-architecture] that enables Reliable and Available
172 Wireless (RAW) on a shared MAC.
174 This draft extends the "Deterministic Networking Use Cases" [RFC8578]
175 and describes a number of additional use cases which require
176 deterministic flows over wireless links and possibly complex multi-
177 hop paths called Tracks. This is covered mainly by the "Wireless for
178 Industrial Applications" use case, as the "Cellular Radio" is mostly
179 dedicated to the (wired) transport part of a Radio Access Network
180 (RAN). Whereas the "Wireless for Industrial Applications" use case
181 certainly covers an area of interest for RAW, it is limited to
182 6TiSCH, and thus its scope is narrower than the use cases described
183 next in this document.
185 2. Amusement Parks
187 2.1. Use Case Description
189 The digitalization of Amusement Parks is expected to decrease
190 significantly the cost for maintaining the attractions. By
191 monitoring in real-time the machines, predictive maintenance will
192 help to reduce the repairing cost as well as the downtime. Besides,
193 the attractions may use wireless transmissions to interconnect
194 sensors and actuators, to privilege reconfigurability, and
195 standardization.
197 Attractions may rely on a large set of sensors and actuators, which
198 react in real time. Typical applications comprise:
200 o emergency: safety has to be preserved, and must stop the
201 attraction when a failure is detected;
203 o video: augmented and virtual realities are integrated in the
204 attraction. Wearable devices (e.g. glasses, virtual reality
205 headset) need to offload one part of the processing tasks.
207 o real-time interactions: visitors may interact with an attraction,
208 like in a real-time video game. The vistors may virtually
209 interact with their environment, triggering actions in the real
210 world (through actuators) [robots]
212 o geolocation: vistors are tracked with a personal wireless tag so
213 that their user experience is improved.
215 o predictive maintenance: statistics are collected to predict the
216 future failures, or to compute later more complex statistics about
217 the attraction's usage, the downtime, its popularity, etc.
219 2.2. Specificities
221 Amusement parks comprise a variable number of attractions, mostly
222 outdoor, over a large geographical area. The IT infrastructure is
223 typically multi-scale:
225 o local area: the sensors and actuators controling the attractions
226 are co-located. Control loops trigger only local traffic, with a
227 small end-to-end delay, typically inferior than 10 milliseconds,
228 like classical industrial systems [ieee80211-rt-tig]
230 o wearable devices are free to move in the park. They exchange
231 traffic locally (identification, personalization, multimedia) or
232 globally (billing, child tracking);
234 o computationally intensive applications offload some tasks to a
235 cloud, and data analytics rely on a centralized infrastructure
236 (predictive maintenance, marketing).
238 2.3. The Need for Wireless
240 Amusement parks cover large areas and a global interconnection would
241 require a huge length of cables. Wireless also increases the
242 reconfigurability, enabling to update cheaply the attractions. The
243 frequent renewal helps to increase customer loyalty.
245 Some parts of the attraction are mobile, e.g. trucks of a
246 rollercoaster, robots. Since cables are prone to frequent failures
247 in this situation, wireless transmissions are recommended.
249 Wearable devices are extensively used for a user experience
250 personalisation. They typically need to support wireless
251 transmissions. Personal tags may help to reduce the operating costs
252 [disney-VIP] and to increase the number of charged services provided
253 to the audience (VIP tickets, interactivity, etc.) Some applications
254 rely on more sophisticated wearable devices such as digital glasses
255 or VR headests for an immersive experience.
257 2.4. Requirements for RAW
259 The network infrastructure has to support heterogenous traffic, with
260 very different criticalities. Thus, flow isolation has to be
261 provided.
263 We have to schedule appropriately the transmissions, even in presence
264 of mobile devices. While the [I-D.ietf-6tisch-architecture] already
265 proposes an architecture for synchronized, IEEE Std. 802.15.4 Time-
266 Slotted Channel Hopping (TSCH) networks, 6TiSCH doesn't address real-
267 time IPv6 flows. RAW might provide mechanisms helping to
268 automatically adapt the network (i.e., schedule appropriately the
269 transmissions, accross hetereogeneous technologies, with strict SLA
270 requirements).
272 Nowadays, long-range wireless transmissions are used for best-effort
273 traffic, and [IEEE802.1TSN] is used for critical flows using Ethernet
274 devices. However, we need an IP enabled technology to interconnect
275 large areas, independent of the PHY and MAC layer to maximize the
276 compliancy.
278 We expect to deploy several different technologies (long vs. short
279 range) which have to cohabit in the same area. Thus, we need to
280 schedule appropriately the transmissions to limit the co-technology
281 interference, so that an end-to-end delay accross multiple
282 technologies can be guaranteed. It is needed to understand which
283 technologies RAW will cover and how they can be used cohabitating in
284 the same area.
286 3. Wireless for Industrial Applications
288 3.1. Use Case Description
290 A major use case for networking in Industrial is the control networks
291 where periodic control loops operate between a sensor that measures a
292 physical property such as the temperature of a fluid, a Programmable
293 Logic Controller that decides an action such as warm up the mix, and
294 an actuator that performs the required action, e.g., inject power in
295 a resistor.
297 3.2. Specificities
299 3.2.1. Control Loops
301 Process Control designates continous processing operations, e.g.,
302 heating Oil in a refinery or mixing drinking soda. Control loops in
303 the Process Control industry operate at a very low rate, typically 4
304 times per second. Factory Automation, on the other hand, deal with
305 discrete goods such as individual automobile parts, and requires
306 faster loops, in the order of 10ms. Motion control that monitors
307 dynamic activities may require even faster rates in the order of a
308 few ms. Finally, some industries exhibit hybrid behaviours, like
309 canned soup that will start as a process industry while mixing the
310 food and then operate as a discrete manufacturing when putting the
311 final product in cans and shipping them.
313 In all those cases, a packet must flow deterministically between the
314 sensor and the PLC, be processed by the PLC, and sent to the actuator
315 within the control loop period. In some particular use cases that
316 inherit from analog operations, jitter might also alter the operation
317 of the control loop. A rare packet loss is usually admissible, but
318 typically 4 losses in a row will cause an emergency halt of the
319 production and incur a high cost for the manufacturer.
321 3.2.2. Unmeasured Data
323 A secondary use case deals with monitoring and diagnostics. This so-
324 called unmeasured data is essential to improve the performances of a
325 production line, e.g., by optimizing real-time processing or
326 maintenance windows using Machine Learning predictions. For the lack
327 of wireless technologies, some specific industries such as Oil and
328 Gas have been using serial cables, literally by the millions, to
329 perform their process optimization over the previous decades. But
330 few industries would afford the associated cost and the Holy Grail of
331 the Industrial Internet of Things is to provide the same benefits to
332 all industries, including SmartGrid, Transportation, Building,
333 Commercial and Medical. This requires a cheap, available and
334 scalable IP-based access technology.
336 Inside the factory, wires may already be available to operate the
337 Control Network. But unmeasured data are not welcome in that network
338 for a number of reasons. On the one hand it is rich and
339 asynchronous, meaning that using they may influence the deterministic
340 nature of the control operations and impact the production. On the
341 other hand, this information must be reported to the carpeted floor
342 over IP, which means the potential for a security breach via the
343 interconnection of the Operational Technology (OT) network with the
344 Internet technology (IT) network and possibly enable a rogue access.
346 3.3. The Need for Wireless
348 Ethernet cables used on a robot arm are prone to breakage after a few
349 thousands flexions, a lot faster than a power cable that is wider inn
350 diameter, and more resilient. In general, wired networking and
351 mobile parts are not a good match, mostly in the case of fast and
352 recurrent activities, as well as rotation.
354 When refurbishing older premises that were built before the Internet
355 age, power is usually available everywhere, but data is not. It is
356 often impractical, time consuming and expensive to deploy an Ethernet
357 fabric across walls and between buildings. Deploying a wire may take
358 months and cost tens of thousands of US Dollars.
360 Even when wiring exists, e.g., in an existing control network,
361 asynchronous IP packets such as diagnostics may not be welcome for
362 operational and security reasons (see Section 3.2.1). An alternate
363 network that can scale with the many sensors and actuators that equip
364 every robot, every valve and fan that are deployed on the factory
365 floor and may help detect and prevent a failure that could impact the
366 production. IEEE Std. 802.15.4 Time-Slotted Channel Hopping (TSCH)
367 [RFC7554] is a promising technology for that purpose, mostly if the
368 scheduled operations enable to use the same network by asynchronous
369 and deterministic flows in parallel.
371 3.4. Requirements for RAW
373 As stated by the "Deterministic Networking Problem Statement"
374 [RFC8557], a Deterministic Network is backwards compatible with
375 (capable of transporting) statistically multiplexed traffic while
376 preserving the properties of the accepted deterministic flows. While
377 the [I-D.ietf-6tisch-architecture] serves that requirement, the work
378 at 6TiSCH was focused on best-effort IPv6 packet flows. RAW should
379 be able to lock so-called hard cells for use by a centralized
380 scheduler, and program so-called end-to-end Tracks over those cells.
382 Over the course of the recent years, major Industrial Protocols,
383 e.g., [ODVA] with EtherNet/IP [EIP] and [Profinet], have been
384 migrating towards Ethernet and IP. In order to unleash the full
385 power of the IP hourglass model, it should be possible to deploy any
386 application over any network that has the physical capacity to
387 transport the industrial flow, regardless of the MAC/PHY technology,
388 wired or wireless, and across technologies. RAW mechanisms should be
389 able to setup a Track over a wireless access segment such as TSCH and
390 a backbone segment such as Ethernet or WI-Fi, to report a sensor data
391 or a critical monitoring within a bouded latency.
393 4. Pro Audio and Video
395 4.1. Use Case Description
397 Many devices support audio and video streaming by employing 802.11
398 wireless LAN. Some of these applications require low latency
399 capability. For instance, when the application provides interactive
400 play, or when the audio takes plays in real time (i.e. live) for
401 public addresses in train stations or in theme parks.
403 The professional audio and video industry ("ProAV") includes:
405 o Virtual Reality / Augmented Reality (VR/AR)
407 o Public address, media and emergency systems at large venues
408 (airports, train stations, stadiums, theme parks).
410 4.2. Specificities
412 4.2.1. Uninterrupted Stream Playback
414 Considering the uninterrupted audio or video stream, a potential
415 packet losses during the transmission of audio or video flows cannot
416 be tackled by re-trying the transmission, as it is done with file
417 transfer, because by the time the packet lost has been identified it
418 is too late to proceed with packet re-transmission. Buffering might
419 be employed to provide a certain delay which will allow for one or
420 more re-transmissions, however such approach is not efficient in
421 application where delays are not acceptable.
423 4.2.2. Synchronized Stream Playback
425 In the context of ProAV, latency is the time between the transmitted
426 signal over a stream and its reception. Thus, for sound to remain
427 synchronized to the movement in the video, the latency of both the
428 audio and video streams must be bounded and consistent.
430 4.3. The Need for Wireless
432 The devices need the wireless communication to support video
433 streaming via 802.11 wireless LAN for instance.
435 During the public address, the deployed announcement speakers, for
436 instance along the platforms of the train stations, need the wireless
437 communication to forward the audio traffic in real time.
439 4.4. Requirements for RAW
441 The network infrastructure needs to support heterogeneous types of
442 traffic (including QoS).
444 Content delivery with bounded (lowest possible) latency.
446 The deployed network topology should allow for multipath. This will
447 enable for multiple streams to have different (and multiple) paths
448 through the network to support redundancy.
450 5. Gaming
452 5.1. Use Case Description
454 The gaming industry includes [IEEE80211RTA]:
456 o Real-time Mobile Gaming: Different from traditional games, real
457 time mobile gaming is very sensitive to network latency and
458 stability. The mobile game can connect multiple players together
459 in a single game session and exchange data messages between game
460 server and connected players. Real-time means the feedback should
461 present on screen as users operate in game. For good game
462 experience, the end to end latency plus game servers processing
463 time should not be noticed by users as they play the game.
465 o Wireless Console Gaming: Playing online on a console has 2 types
466 of internet connectivity, which is either wired or Wi-Fi. Most of
467 the gaming consoles today support Wi-Fi 5. But Wi-Fi has an
468 especially bad reputation among the gaming community. The main
469 reasons are high latency, lag spikes and jitter.
471 o Cloud Gaming: The cloud gaming requires low latency capability as
472 the user commands in a game session need to be sent back to the
473 cloud server, the cloud server would update game context depending
474 on the received commands, and the cloud server would render the
475 picture/video to be displayed at user devices and stream the
476 picture/video content to the user devices. User devices might
477 very likely be connected wirelessly.
479 5.2. Specificities
481 While a lot of details can be found on [IEEE80211RTA], we next
482 summarize the main requirements in terms of latency, jitter and
483 packet loss:
485 o Intra BSS latency: less than 5 ms.
487 o Jitter variance: less than 2 ms.
489 o Packet loss: less than 0.1 percent.
491 5.3. The Need for Wireless
493 It is clear that gaming is evolving towards wireless, as players
494 demand being able to play anywhere. Besides, the industry is
495 changing towards playing from mobile phones, which are inherently
496 connected via wireless technologies.
498 5.4. Requirements for RAW
500 o Time sensitive networking extensions. Extensions, such as time-
501 aware shaping and redundancy (FRE) can be explored to address
502 congestion and reliability problems present in wireless networks.
504 o Priority tagging (Stream identification). One basic requirement
505 to provide better QoS for time-sensitive traffic is the capability
506 to identify and differentiate time-sensitive packets from other
507 (e.g. best-effort) traffic.
509 o Time-aware shaping. This capability (defined in IEEE 802.1Qbv)
510 consists of gates to control the opening/closing of queues that
511 share a common egress port within an Ethernet switch. A scheduler
512 defines the times when each queue opens or close, therefore
513 eliminating congestion and ensuring that frames are delivered
514 within the expected latency bounds.
516 o Dual/multiple link. Due to the competitions and interference are
517 common and hardly in control under wireless network, in order to
518 improve the latency stability, dual/multiple link proposal is
519 brought up to address this issue. Two modes are defined:
520 duplicate and joint.
522 o Admission Control. Congestion is a major cause of high/variable
523 latency and it is well known that if the traffic load exceeds the
524 capability of the link, QoS will be degraded. QoS degradation
525 maybe acceptable for many applications today, however emerging
526 time-sensitive applications are highly susceptible to increased
527 latency and jitter. In order to better control QoS, it is
528 important to control access to the network resources.
530 6. UAV platooning and control
532 6.1. Use Case Description
534 Unmanned Aerial Vehicles (UAVs) are becoming very popular for many
535 different applications, including military and civil use cases. The
536 term drone is commonly used to refer to a UAV.
538 UAVs can be used to perform aerial surveillance activities, traffic
539 monitoring (e.g., Spanish traffic control has recently introduced a
540 fleet of drones for quicker reactions upon traffic congestion related
541 events), support of emergency situations, and even transportation of
542 small goods.
544 UAVs typically have various forms of wireless connectivity:
546 o cellular: for communication with the control center, for remote
547 manuevering as well as monitoring of the drone;
549 o IEEE 802.11: for inter-drone communications (e.g., platooning) and
550 providing connectivity to other devices (e.g., acting as Access
551 Point).
553 6.2. Specificities
555 Some of the use cases/tasks involving drones require coordination
556 among drones. Others involve complex compute tasks that might not be
557 performed using the limited computing resources that a drone
558 typically has. These two aspects require continuous connectivity
559 with the control center and among drones.
561 Remote manouvering of a drone might be performed over a cellular
562 network in some cased, however, there are situations that need very
563 low latencies and deterministic behaviour of the connectivity.
564 Examples involve platooning of drones or share of computing resources
565 among drones (e.g., a drone offload some function to a neighbouring
566 drone).
568 6.3. The Need for Wireless
570 UAVs cannot be connected through any type of wired media, so it is
571 obvious that wireless is needed.
573 6.4. Requirements for RAW
575 The network infrastructure is actually composed by the UAVs
576 themselves, requiring self-configuration capabilities.
578 Heterogeneous types of traffic need to be supported, from extremely
579 critical ones requiring ultra low latency and high resiliency, to
580 traffic requiring low-medium latency.
582 When a given service is decomposed into functions -- hosted at
583 different drones -- chained, each link connecting two given functions
584 would have a well-defined set of requirements (latency, bandwith and
585 jitter) that have to be met.
587 7. Edge Robotics control
589 7.1. Use Case Description
591 The Edge Robotics scenario consists of several robots, deployed in a
592 given area (for example a shopping mall), inter-connected via an
593 access network to a network's edge device or a data center. The
594 robots are connected to the edge so complex computational activities
595 are not executed locally at the robots, but offloaded to the edge.
596 This brings additional flexibility in the type of tasks that the
597 robots do, as well as reducing the costs of robot manufacturing (due
598 to their lower complexity), and enabling complex tasks involving
599 coordination among robots (that can be more easily performed if
600 robots are centrally controlled).
602 A simple example of the use of multiples robots is cleaning,
603 delivering of goods from warehouses to shops or video surveillance.
604 Multiple robots are simultaneously instructed to perform individual
605 tasks by moving the robotic intelligence from the robots to the
606 network's edge (e.g., data center). That enables easy
607 synchronization, scalable solution and on-demand option to create
608 flexible fleet of robots.
610 Robots would have various forms of wireless connectivity:
612 o IEEE 802.11: for connection to the edge and also inter-robot
613 communications (e.g., for coordinated actions);
615 o cellular: as an additional communication link to the edge, though
616 primarily as backup, since ultra low latencies are needed.
618 7.2. Specificities
620 Some of the use cases/tasks involving robots might benefit from
621 decomposition of a service in small functions that are distributed
622 and chained among robots and the edge. These require continuous
623 connectivity with the control center and among drones.
625 Robot control is an activity requiring very low latencies between the
626 robot and the location where the control intelligence resides (which
627 might be the edge or another robot).
629 7.3. The Need for Wireless
631 Deploying robots in scenarios such as shopping malls for the
632 aforementioned applications cannot be done via wired connectivity.
634 7.4. Requirements for RAW
636 The network infrastructure needs to support heterogeneous types of
637 traffic, from robot control to video streaming.
639 When a given service is decomposed into functions -- hosted at
640 different robots -- chained, each link connecting two given functions
641 would have a well-defined set of requirements (latency, bandwith and
642 jitter) that have to be met.
644 8. IANA Considerations
646 N/A.
648 9. Security Considerations
650 N/A.
652 10. Informative References
654 [disney-VIP]
655 Wired, "Disney's $1 Billion Bet on a Magical Wristband",
656 March 2015,
657 .
659 [EIP] http://www.odva.org/, "EtherNet/IP provides users with the
660 network tools to deploy standard Ethernet technology (IEEE
661 802.3 combined with the TCP/IP Suite) for industrial
662 automation applications while enabling Internet and
663 enterprise connectivity data anytime, anywhere.",
664 .
668 [I-D.ietf-6tisch-architecture]
669 Thubert, P., "An Architecture for IPv6 over the TSCH mode
670 of IEEE 802.15.4", draft-ietf-6tisch-architecture-24 (work
671 in progress), July 2019.
673 [I-D.ietf-detnet-architecture]
674 Finn, N., Thubert, P., Varga, B., and J. Farkas,
675 "Deterministic Networking Architecture", draft-ietf-
676 detnet-architecture-13 (work in progress), May 2019.
678 [IEEE802.1TSN]
679 IEEE standard for Information Technology, "IEEE
680 802.1AS-2011 - IEEE Standard for Local and Metropolitan
681 Area Networks - Timing and Synchronization for Time-
682 Sensitive Applications in Bridged Local Area Networks".
684 [ieee80211-rt-tig]
685 IEEE, "IEEE 802.11 Real Time Applications TIG Report",
686 Nov. 2018,
687 .
689 [IEEE80211RTA]
690 IEEE standard for Information Technology, "IEEE 802.11
691 Real Time Applications TIG Report", Nov 2018.
693 [ISA100] ISA/ANSI, "ISA100, Wireless Systems for Automation",
694 .
696 [ODVA] http://www.odva.org/, "The organization that supports
697 network technologies built on the Common Industrial
698 Protocol (CIP) including EtherNet/IP.".
700 [Profinet]
701 http://us.profinet.com/technology/profinet/, "PROFINET is
702 a standard for industrial networking in automation.",
703 .
705 [RFC7554] Watteyne, T., Ed., Palattella, M., and L. Grieco, "Using
706 IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the
707 Internet of Things (IoT): Problem Statement", RFC 7554,
708 DOI 10.17487/RFC7554, May 2015,
709 .
711 [RFC8557] Finn, N. and P. Thubert, "Deterministic Networking Problem
712 Statement", RFC 8557, DOI 10.17487/RFC8557, May 2019,
713 .
715 [RFC8578] Grossman, E., Ed., "Deterministic Networking Use Cases",
716 RFC 8578, DOI 10.17487/RFC8578, May 2019,
717 .
719 [robots] Kober, J., Glisson, M., and M. Mistry, "Playing catch and
720 juggling with a humanoid robot.", 2012,
721 .
723 Authors' Addresses
725 Georgios Z. Papadopoulos
726 IMT Atlantique
727 Office B00 - 114A
728 2 Rue de la Chataigneraie
729 Cesson-Sevigne - Rennes 35510
730 FRANCE
732 Phone: +33 299 12 70 04
733 Email: georgios.papadopoulos@imt-atlantique.fr
735 Pascal Thubert
736 Cisco Systems, Inc
737 Building D
738 45 Allee des Ormes - BP1200
739 MOUGINS - Sophia Antipolis 06254
740 FRANCE
742 Phone: +33 497 23 26 34
743 Email: pthubert@cisco.com
744 Fabrice Theoleyre
745 CNRS
746 ICube Lab, Pole API
747 300 boulevard Sebastien Brant - CS 10413
748 Illkirch 67400
749 FRANCE
751 Phone: +33 368 85 45 33
752 Email: theoleyre@unistra.fr
753 URI: http://www.theoleyre.eu
755 Carlos J. Bernardos
756 Universidad Carlos III de Madrid
757 Av. Universidad, 30
758 Leganes, Madrid 28911
759 Spain
761 Phone: +34 91624 6236
762 Email: cjbc@it.uc3m.es
763 URI: http://www.it.uc3m.es/cjbc/