Inter-network Coexistence in the Internet of Things

Versions: 00 01 02 03                                                   
Network Working Group                                          L. Feeney
Internet-Draft                                        Uppsala University
Intended status: Informational                                  V. Fodor
Expires: November 26, 2018                                           KTH
                                                            May 25, 2018

          Inter-network Coexistence in the Internet of Things


   The breadth of IoT applications implies that there will be many
   diverse, administratively independent networks operating in the same
   physical location.  In many cases, these networks will use unlicensed
   spectrum, due to its low cost and ease of deployment.  However, this
   spectrum is becoming increasingly crowded.  IoT networks will
   therefore be subject to wireless interference, both from similar
   networks and from networks that use the wireless channel in very
   different ways.

   High-density, heterogeneous wireless environments present formidable
   challenges for network coexistence.  The PHY and MAC layers are
   primarily responsible for managing how radios use the channel.  But
   higher layer protocols are also a key factor in inter-network
   interaction.  To date, there have been few performance studies of
   coexistence in future IoT operating environments, particularly with
   respect to protocol behavior and network-scale interactions.

   This document describes key challenges for coexistence and highlights
   some recent research results that demonstrate the impact of protocol
   level interactions on network performance.  It identifies both
   concrete and speculative opportunities for the IRTF T2TRG community.
   The former include developing and documenting best practices for
   performance evaluation and contributing IoT-related protocols being
   developed within IETF.  The latter include speculative research into
   the design of high-layer protocols that allow networks to actively
   coordinate their access to the shared channel.

Status of This Memo

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   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  IoT interaction challenges  . . . . . . . . . . . . . . . . .   5
     2.1.  Scale . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     2.2.  Independence  . . . . . . . . . . . . . . . . . . . . . .   5
     2.3.  Resource limitations  . . . . . . . . . . . . . . . . . .   5
     2.4.  Diversity . . . . . . . . . . . . . . . . . . . . . . . .   6
       2.4.1.  Radio and PHY . . . . . . . . . . . . . . . . . . . .   6
       2.4.2.  Network structures  . . . . . . . . . . . . . . . . .   7
       2.4.3.  Protocols . . . . . . . . . . . . . . . . . . . . . .   7
   3.  Interaction behaviors . . . . . . . . . . . . . . . . . . . .   9
     3.1.  WiFi  . . . . . . . . . . . . . . . . . . . . . . . . . .  10
     3.2.  IEEE 802.15.4 . . . . . . . . . . . . . . . . . . . . . .  10
     3.3.  Recent results in IoT networks  . . . . . . . . . . . . .  10
     3.4.  Higher layer protocols  . . . . . . . . . . . . . . . . .  11
   4.  Network coexistence in the IRTF/IETF context  . . . . . . . .  11
     4.1.  Performance evaluation and protocol design  . . . . . . .  12
     4.2.  Adaptive mitigation strategies  . . . . . . . . . . . . .  13
     4.3.  Active mitigation strategies  . . . . . . . . . . . . . .  14
     4.4.  Role of Spectrum Regulation . . . . . . . . . . . . . . .  15
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  16
   6.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  17
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  18
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  20
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  20

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1.  Introduction

   An IoT application is a set of wireless devices that act together to
   perform some sensing and control function.  Most applications also
   have some connectivity to external resources, such as a mobile app or
   cloud-based service.  In general, each application is deployed
   independently of any other applications that may be operating in the
   area and is a physically and administratively separate network.

   An enormous range of IoT applications are expected to become
   pervasive in daily life.  Networks will be installed in public
   spaces, businesses, and residences by a wide range of individual,
   commercial, and government actors.  As a result, there will be many
   diverse, administratively independent networks operating in the same
   physical location.  For example, a future home environment may
   include IoT applications for security, heating and cooling, elder
   care, air quality monitoring, personal health and fitness, smart home
   appliances, structural monitoring, lighting, utilities, and

   Many of these networks will use unlicensed spectrum due to low cost
   and simplicity of deployment for both the user and developer.  In
   unlicensed spectrum, there is no authority that has a management
   relationship with (or even knows about) all of the potentially
   interfering networks that can be present in some location.  This
   means that there is no entity that can coordinate networks' use of
   the shared wireless channel.  Networks will therefore experience
   interference caused by transmissions from devices belonging to other

   The PHY and MAC layers have primary responsibility for ensuring that
   devices share the channel efficiently, while spectrum regulations
   limit devices' output power and overall channel utilization.  But the
   MAC protocol can only explicitly coordinate devices within a single
   network.  It provides only limited protection from other networks,
   some of which may have very different transmission footprints over
   time, spectrum or physical space.

   Network coexistence is mainly evaluated in terms of PHY layer and
   radio hardware resilience to interference.  This is generally based
   on analytic modeling of the probability of successful packet
   reception for varying SNIR conditions or on carefully controlled
   measurements of interacting RF waveforms.  (See e.g.  [NIST] for a
   discussion of relevant issues and [SKH11] for an example of such an
   analysis for IEEE 802.15.4g.)

   Analytic modeling of network interactions at the MAC layer is much
   harder, because each network adapts its transmission parameters and

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   timing in response to the others.  The presence of interfering
   networks is usually modeled as increasing the intensity of some
   statistical process representing noise or loss.  Testbed tools, such
   as JamLab, have also been used to generate controlled interference.
   Other studies are based on simulation or testbed measurements of
   simple scenarios.  The research literature contains a number of such
   studies, especially for IEEE 802.11 and IEEE 802.15.4.  (See [SURVEY]
   and [SURVEY2] for an overview.)

   In practice, currently deployed networks mostly rely on low IoT
   traffic loads and careful channel selection to achieve adequate
   performance.  This may not be sustainable as rapid growth in IoT (and
   mobile data offloading) lead to increasing pressure on unlicensed
   spectrum.  There are very few studies that evaluate complex,
   heterogeneous IoT interference scenarios, particularly with regard to
   protocol behavior and network-scale interactions.  But as recent work
   [WETZ17] demonstrates, real world instances of IoT interference do
   occur and require considerable effort to diagnose.

   This document explores key challenges for network coexistence in
   future IoT environments and highlights some recent research results
   ([FF16], [F3G15], [YTB17]).  These suggest that protocol level
   interactions can significantly affect network performance, even in
   simple scenarios where the channel is not heavily loaded.  Higher
   layer protocols will need to be aware of the potential impact of
   inter-network interference and avoid contributing to adverse

   The community does not yet have a solid understanding of the
   reliability and effectiveness of IoT protocols in the presence of
   inter-network interference.  In part, this is because the tools and
   techniques for performance evaluation of network coexistence
   scenarios are still immature.  This document considers some of the
   challenges and requirements for both simulation and testbed

   We also identify both concrete and speculative areas where T2TRG is
   well-positioned to contribute: The former includes the development of
   best practices for performance evaluation and informing the ongoing
   development of IoT-related protocols being developed within the IETF.
   The latter includes speculative research into the development of
   protocols that allow independent networks to actively coordinate
   their use of the shared wireless channel.

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2.  IoT interaction challenges

   Widespread deployment of diverse IoT applications presents four main
   challenges: 1) scale 2) lack of a trust relationship between
   independently deployed networks 3) resource limitations, especially
   battery capacity 4) diversity of application requirements and channel
   utilization behavior.

2.1.  Scale

   As IoT becomes pervasive, there will be many independent networks
   operating in any given location.  Devices will experience high levels
   of both homogeneous and heterogeneous radio interference.

   Since networks use different kinds of radios and have different
   wireless coverage areas, their topologies will overlap with each
   other in complex ways.  Interference will therefore involve not just
   individual wireless links, but also larger regions in the network and
   protocols operating at network scale.

   Interaction scenarios will also be highly dynamic, with mobility and
   user activity leading to frequent changes in the set of interfering

2.2.  Independence

   In unlicensed spectrum, there is no obvious basis for an
   administrative relationship between networks.  Networks with
   overlapping wireless coverage may well have been deployed by at
   different times by unrelated actors.  Nor is there any common
   authority that has an administrative relationship with all of the
   potentially interfering networks in any given location.

   This means that there is no external entity that networks can trust
   to coordinate access to the shared channel.  Devices within any one
   network will be able to authenticate themselves to each other and
   their own administrator (usually a non-expert user).  But there is no
   way for them to authenticate themselves to each other - an IoT
   network may not have any meaningful external identity.  Even if two
   networks can exchange this information, there is no obvious way for
   each to determine whether the other will participate appropriately
   with respect to some coexistence mechanism.

2.3.  Resource limitations

   IoT networks are severely resource constrained in many respects,
   including channel capacity, energy, hardware capabilities and cost.

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   The large number of devices sharing the wireless channel naturally
   limits the capacity available to each device.  In addition, many
   devices use low bit-rate radios, which further reduces the
   communication capacity.  (Note, however, that adverse interactions
   between networks can occur even in cases where the channel is only
   lightly used.)

   For energy-harvesting and battery-powered devices, maximizing
   lifetime is essential.  Protocol design is dominated by the need to
   minimize device activity and especially by the need to keep the
   energy-hungry radio turned off as much as possible, while still
   maintaining necessary connectivity.  Even sensing the channel
   conditions is an extremely expensive operation.  This limits
   networks' ability to observe and adapt to the behavior of their

   Finally, for many IoT applications, devices must be low cost and
   easily deployed and managed by non-expert users.  They often have
   very limited memory and CPU resources.  These factors constrain the
   design space and limit the complexity of proposed solutions.

2.4.  Diversity

   Even networks that use the same radio hardware and protocols will
   interfere with each other.  But the diversity of IoT radios,
   protocols and applications creates additional challenges.  Even
   characterizing the space of possible interactions may be challenging:
   Protocols can be anything from freely available, to consortia-driven
   standards such as ZigBee or WirelessHART, to completely proprietary.

   This diversity is driven by the diversity of IoT applications.
   Applications will differ significantly in their devices'
   communication range and the overall network coverage area.  They will
   vary in the number of devices and traffic load.  They will have
   different requirements for latency and reliability.  And they will
   use different energy sources and have different requirements on
   energy efficiency and lifetime.  To meet these requirements,
   applications will use a wide variety of radios, protocols and network

2.4.1.  Radio and PHY

   Different radio technologies divide the spectrum into channels
   differently: In the 2.4GHz unlicensed band, for example, IEEE 802.11
   has up to 14 overlapping channels, while IEEE 802.15.4 and
   BluetoothLE have 16 and 40 non-overlapping ones.  Radios also use a
   variety of modulation techniques at the PHY layer to define how data
   is encoded on the channel as RF energy.

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   This means that there are many different ways that RF energy is
   distributed over time and spectrum.  As a result, it may not be
   possible for channel sensing mechanisms to reliably detect the
   presence of potentially interfering transmissions or identify the
   source of interference and packet loss.

   Each PHY layer makes different tradeoffs between transmit power,
   communication range, bit-rate, bandwidth, energy consumption and
   resilience.  In sub-GHz spectrum, for example, IEEE 802.15.4g/Wi-SUN
   (smart utility network) provides 50-200 kbps bit-rates with ranges of
   > 1000m.  Very low power EnOcean devices provide similar bit-rates,
   but ranges of < 100m.  By contrast, LoRa provides bit-rates of at
   most a few kbps, but can obtain 10km of range.  Differences in bit-
   rate and frame size mean that packet transmit times can range from <
   10 ms to > 200 ms.  Radios operating in 2.4GHz, such as IEEE
   802.15.4, IEEE 802.11 and Bluetooth, show similar diversity.

2.4.2.  Network structures

   Along with different kinds of radios, different kinds of network
   structures can be used to meet application requirements for density
   and coverage area.  The most common structure is the star topology,
   where all devices communicate directly with a controller.  Networks
   can also cover larger areas or achieve higher reliability by using
   multi-hop forwarding over various topologies, such as directed
   acyclic graphs, cluster trees, and meshes.

   These structures affect how transmissions within a network are
   correlated with each other in time and space, such as forwarding a
   frame across a mesh.  It can also affect interactions between
   networks, particularly networks whose radios have very different
   coverage areas.  For example, a long-range device belonging to one
   network may be located in the midst of a mesh of short-range devices
   belonging to another network.

2.4.3.  Protocols

   The MAC layer defines how senders coordinate their transmissions
   within a network.  Like the PHY layer, different MAC layers create
   different distributions of RF energy in time and (for channel hopping
   protocols) spectrum.

   CSMA-based (channel sensing and backoff) protocols can provide some
   protection from external transmissions, since they defer to any
   ongoing transmission that they detect.  Conflicts due to hidden
   terminals can occur even within a single network, but differences
   between radio technologies and network structures may exacerbate the
   problem.  In addition, MAC timing parameters, such as backoff times,

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   are generally proportional to bit-rate and frame transmit times.
   Timing incompatibilities between interfering senders can reduce the
   effectiveness of backoff and retransmissions in heterogeneous

   TDMA-based (transmission schedule) protocols can be more efficient in
   their use of the channel and energy than CSMA protocols.  But because
   the networks define their slot structure and transmission schedules
   independently, they may allocate transmission slots that conflict
   with each other.  Since senders rely on their assigned schedule, such
   conflicts can be costly.

   Minimizing energy consumption is often the absolute priority for IoT
   design.  It is necessary to keep radio turned off as much as
   possible, while still ensuring connectivity.  As with MAC protocols
   (with which they are sometimes integrated), there are a variety of
   approaches.  With synchronous methods, devices wake up according to a
   schedule that ensures that senders and receivers are awake at the
   same time (as in IEEE 802.15.4 beacon-enabled PANs or TSCH).
   Asynchronous methods allow devices to coordinate their wake up
   schedules on-demand (as in ContikiMAC).

   Coordinating the duty cycles of a sender and receiver imposes strict
   timing constraints on radio operations.  As with the PHY and MAC,
   each power save protocol creates its own distribution of RF energy
   over time.  Depending on application requirements and tradeoffs for
   latency and battery lifetime, duty cycles could be on timescale of <
   1s to > 1000s.

   Many IoT networks use IP(v6), but there is also considerable
   diversity in higher layer protocols, both open and proprietary.
   Routing protocols make different tradeoffs between latency,
   reliability, energy efficiency and overhead, depending on the
   application requirements.  The operation of the routing protocol also
   affects the distribution of RF energy in physical space, as frames
   are forwarded toward a root or across a mesh.  The routing protocol
   may also react to the presence of interference by attempting to re-
   route its traffic.

   Higher layer protocols largely abstract away from the behavior of
   individual wireless links.  They use a variety of mechanisms to
   maintain communication performance under conditions of loss and
   delay, including retransmissions, multi-path communication, and
   application-specific adaptations.

   Finally, the variety of transport, transfer and application protocols
   used in IoT networks reflects the diversity of use cases: The RESTful
   model is central for IoT applications based on web services

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   [I-D.keranen-t2trg-rest-iot].  Wireless sensing applications often
   use in-network data processing and aggregation to reduce their
   communication load.  Industrial IoT applications emphasize low
   latency and reliability.  Wide-area IoT/SUN networks collect small
   amounts of data from a very large number of devices.  As a result,
   applications may have very different priorities with respect to
   packet loss, delay, and energy consumption.

3.  Interaction behaviors

   All elements of network functionality - MAC, power saving, topology
   and routing, congestion control, data transfer, application -
   contribute patterns of channel utilization over time, frequency, and
   physical space.  At the same time, protocols adapt their behavior in
   response to channel conditions; relying on channel sensing and frame
   errors at low layers and on loss and delay at higher layers.  Inter-
   network interaction therefore occurs on multiple time- and spatial-
   scales and involves all layers of the protocol stack.

   Motivating scenarios include:

   o  How will sub-GHz LPWAN networks such as LoRa and SigFox, whose
      base stations cover wide areas, interact with multiple shorter-
      range networks using IEEE 802.15.4g/WiSUN, Z-Wave, or EnOcean

   o  What happens if two or more independent networks using
      6LoWPAN+RPL+CoAP are operating in the same room?  Or two 6TiSCH
      networks, each using a different scheduling function?  What if an
      a beacon-enabled PAN interacts with a ZigBee- or ContikiMAC- or
      Thread-based network?  What if people wearing BluetoothLE-based
      body-area networks are also moving around in the area?  Especially
      in a WiFi heavy environment, the value of channel hopping for
      interference mitigation may be limited.

   o  More generally, can networks using protocols optimized for
      different metrics (e.g. latency vs battery lifetime) operate
      effectively in the same location?

   To date, there have been very few studies that examine network
   performance under realistic - dense, heterogeneous, dynamic -
   interference scenarios.  Some existing observations and results are
   noted here.

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3.1.  WiFi

   Interference between WiFi networks is a long-standing problem,
   particularly in dense residential and urban areas, where there are
   many independently deployed networks and large amounts of traffic.

   To some extent, this has been mitigated by expansion into 5GHz
   unlicensed spectrum and by major improvements in WiFi, including
   higher bit-rates and directional transmission (beamforming).  The
   WiFi environment also has some properties that are helpful for
   coexistence: WiFi networks are largely homogeneous, consisting of an
   AP and associated devices that communicate directly with their AP.
   WiFi also uses a CSMA-based MAC, which means that senders inherently
   defer to any ongoing WiFi transmission, regardless of its source.
   And the dominant application is media streaming, which is supported
   by adaptive mechanisms everywhere from the server to the user

   However, WiFi performance may come under increasing pressure, due not
   only to the increasing number of IoT networks, but also to the
   forthcoming deployment of LTE traffic into 5GHz unlicensed spectrum.

3.2.  IEEE 802.15.4

   A common scenario in 2.4GHz spectrum will involve high-power, high-
   traffic WiFi networks impacting networks based on low power, low bit-
   rate radios, such as IEEE 802.15.4.

   Practical existing solutions are mostly based on IEEE 802.15.4
   devices identifying and using the least interfered channels, either
   statically or by channel hopping.  But in areas where there is a lot
   of WiFi traffic, there may be very few such channels.  WiFi
   conventionally uses non-overlapping WiFi channels 1, 6, and 11,
   leaving just three minimally interfered IEEE 802.15.4 channels.  As a
   result, low power IoT networks operating in these areas may be
   crowded into a small number of "good" channels.  These may come under
   increasing pressure as IoT deployment increases.

3.3.  Recent results in IoT networks

   Recent research suggests that protocol level interactions can lead to
   severe performance degradation, even when the channel is not heavily
   loaded.  While these studies focus on various IEEE 802.15.4 MAC
   layers, the results suggest broader implications for protocol design.

   [F3G15] and [FF16] show that IEEE 802.15.4 beacon-enabled PANs can
   experience episodes of severe disruption due to protocol level
   interactions.  This includes behaviors such as short-term

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   oscillations in throughput and extended periods of disconnectivity -
   even when the channel itself is only lightly loaded.  Similarly,
   [YTB17] shows that interfering IEEE 802.15.4 6TiSCH-based networks
   experience packet loss and so-called blackout periods, as well as
   increased energy consumption.

   These behaviors appear to be due to a combination of timing
   rigidities in the MAC protocol, periodicity in the radio duty cycle,
   and clock drift between networks.  Battery constraints force devices
   to spend most of their time with their radios turned off.  Senders
   and receivers therefore need some way to coordinate their radio wake
   up times so that they can exchange packets.  These mechanisms often
   depend heavily on careful timing of radio operations, instead of (or
   in addition to) explicit control traffic.  This timing dependence can
   make networks more sensitive to disruption than might be expected
   from just considering overall channel utilization and collision
   probabilities.  Periodicity can exacerbate these effects.  In
   addition, clock drift results in networks' synchronizing and
   desynchronizing with each other.  This can result in interaction
   effects at timescales on the orders of minutes or even days.
   Generalizing these observations suggests that it will be necessary to
   reconsider tradeoffs between energy consumption and resilience.

3.4.  Higher layer protocols

   To date, there have been few studies that address the performance of
   high layer protocols, such as routing or data transfer, in network
   coexistence.  Certainly, extended outages at the link layer will
   affect their operation and there is a risk that higher layer
   protocols' reaction will exacerbate the impact of interference.
   Conversely, it is possible that higher layer protocols may act to
   mitigate the impact of interference, e.g. through congestion

4.  Network coexistence in the IRTF/IETF context

   The research literature contains a variety of proposals for improving
   protocol performance in the presence of interference (see [SURVEY],
   [SURVEY2] for an overview).  In many cases, they assume rather
   narrowly defined interaction scenarios and none seem to have been
   deployed in practice.

   Network coexistence in realistic IoT environments remains an open
   issue, particularly with respect to protocol and network-scale
   interactions.  T2TRG is well-positioned to contribute to addressing
   it by:

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   o  Developing and advocating best practices for performance
      evaluation, focusing on realistic future wireless environments.

   o  Contributing to the ongoing development of IoT-related IETF
      protocols, so that they are as resilient as possible to inter-
      network interference.

   o  Supporting speculative research into the possibility of higher
      layer protocols for active coordination between networks sharing
      unlicensed spectrum.

4.1.  Performance evaluation and protocol design

   Performance evaluation of IoT protocols should take into account
   their behavior in the presence of many diverse, administratively
   independent networks operating in the same spectrum.  To date, there
   have been few studies that fully reflect this aspect of the future
   IoT operating environment.  This suggests that the community does not
   yet have a complete understanding of effectiveness and reliability of
   IoT protocols.

   Given the community's limited experience with such evaluation, it is
   unsurprising that there are not yet clear principles for designing
   experiments that can provide meaningful results.  Experiments must
   reflect a realistic interference environment and capture behaviors
   caused by interactions within the protocol stack, within a network,
   and between networks - while still being both manageable and
   informative for the the user.  Best practices for designing such
   experiments have not been established and existing simulation and
   testbed tools have significant limitations.

   Protocol-oriented network simulators (e.g. ns-2/3, OMNeT++, OPNET)
   enable performance evaluation at scale: It is straightforward to
   simulate an extremely large number of scenarios behavior over a long
   period.  Simulation also provides complete control and visibility
   into the operation of the simulated system.  However, these
   advantages come at the cost of reduced fidelity, especially for
   wireless propagation and reception.  Modeling of interference between
   different kinds of radios is particularly lacking.

   By contrast, testbeds provide ground-truth about network performance
   in a specific scenario.  There are a number of open WSN/IoT testbeds
   (e.g.  [FINTEROP], [FITIOT]) that provide access to various
   collections of hardware.  However, the community has had little
   experience using them for evaluating coexistence scenarios.

   There are three main challenges: One is the logistics of deploying
   long-running experiments involving multiple applications and many

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   devices.  Another practical challenge is instrumenting and collecting
   data from the entire protocol stack and correlating the results
   across networks, especially with resource-constrained devices.  This
   functionality is essential for obtaining data that allows users to
   reason about the observed performance.  Finally, there is a deeper
   challenge in defining experiments that allow the user to
   systematically explore the space of possible interactions, despite
   the complexity and variability of the inter-network interference

   In this context, T2TRG can contribute to the development of and
   advocacy for best practices for performance evaluation.  The results
   of such studies can inform ongoing protocol development.  This
   includes protocols being developed in the IETF 6lo, 6TiSCH
   (especially 6top), LPWAN, LWIG, ROLL and CoRe Working Groups.  (It
   is, of course, also necessary to take into account interactions with
   protocols from other open and proprietary sources.)

4.2.  Adaptive mitigation strategies

   Network coexistence is likely to rely heavily on improving resilience
   to interference in the MAC layer, which is ultimately responsible for
   determining when a sender transmits.

   But a MAC protocol cannot explicitly coordinate with devices in other
   networks; it may not even be able to identify what kinds of networks
   are sharing the channel, much less exchange (authenticated) control
   traffic.  The MAC layer must instead adapt to the presence of other
   networks based on channel sensing and frame loss.  This is a
   significant challenge in complex interference environments,
   especially for battery-powered devices, which must avoid the high
   energy cost of listening to the channel as much as possible.  While
   the MAC layer and power saving protocols are themselves largely
   outside IETF scope, these topics are relevant to the work of IETF
   WG's such as 6lo, 6TiSCH, LPWAN and LWIG.

   Like the MAC layer, higher layer protocols also adapt their behavior,
   using packet loss and delay.  But complex interactions such as those
   described above can lead to disruptions that are difficult for higher
   layer protocols to predict or adapt to in an effective way.  It is
   therefore important to ensure that protocol behaviors, such as route
   selection, congestion control or keep-alive mechanisms, contribute to
   (or at least do not hurt) resilience to inter-network interference.
   These topics are particularly relevant to IETF protocols such as RPL
   and CoAP.

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4.3.  Active mitigation strategies

   More speculatively, there may be opportunities for higher layer
   protocols to actively participate in interference mitigation, by
   sharing information about their operation and even by explicit
   coordination between networks.

   When two networks use the same PHY layer, it is possible for frames
   transmitted by devices in one network to be successfully received by
   devices in other networks.  These frames are usually discarded
   immediately, since they fail a MAC layer authentication check.  But
   if they are not discarded (and are not encrypted), the networks can
   observe each others' control traffic or even explicitly exchange
   information.  Such a mechanism could allow them to announce their
   expected channel utilization patterns, for example.  MAC layer or
   even IPv6 frames could be used for this purpose.

   Alternatively, many IoT applications have some administrative
   component that is connected to the Internet infrastructure, such as
   mobile app-based user interface or cloud-based data collection.  Even
   limited connectivity opens possibilities for making use of a rich
   array of resources.  For example, this may be a way to provide access
   to additional computing power or to allow networks make use of
   external services with which they have an administrative
   relationship.  This might enable a coordination mechanism based on
   negotiation via some trusted cloud-based service.

   The inspiration here is from several different approaches: Cognitive
   radio solutions where secondary users obtain information about
   activity of primary users from trusted sources; Citizens Broadband
   Radio Service (CBRS) and its spectrum allocation service; and
   research into distributed coordination services e.g.  [SEMCK14].
   However, all of these approaches rely on either strict spectrum
   regulation or a strong assumption of compatibility and cooperative
   behavior among networks.

   Even more speculatively, a secure distributed ledger could be used to
   allow networks to announce themselves in a location, to provide
   information about their channel utilization, and to obtain
   information about co-located networks.  Such a ledger could further
   act as a reputation management system or as a resource broker.  This
   is potentially related to distributed infrastructure work in the IRTF

   However, these are very much an open research area and there are
   substantial challenges in developing such mechanisms:

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   1) There is an enormous diversity of radios, channel access methods
   and utilization patterns that might need to be described.  It is not
   clear what information should be signaled or what actions a receiver
   should take in response.

   2) Battery lifetime, channel capacity, and device CPU and memory
   resources continue to be significant limitations.  In particular, the
   radio duty cycle is highly constrained, limiting both sensing and

   3) Any cooperative mechanism must operate effectively in the absence
   of any administrative or trust relationship between networks.
   Alternatively, there must be some way to establish an appropriate
   level of trust.  This presents a significant challenge to the
   practical implementation of cooperative mechanisms proposed in the
   literature.  (See Security Considerations below.)

   4) The privacy implications of networks sharing information about
   their activity must be carefully considered.  (See Security
   Considerations below.)

   Despite the challenges, this topic seems particularly amenable to
   standards and interoperability-oriented approaches enabled by IRTF
   T2TRG.  There may be synergy with IRTF T2TRG work in IoT semantic
   interoperability: Can IoT networks describe not only the 'things'
   they connect, but also themselves?  In addition, the IRTF DIN
   research group is active in the area of secure distributed Internet

4.4.  Role of Spectrum Regulation

   Network coexistence is ultimately a problem of spectrum regulation.
   Regulation of unlicensed spectrum has historically focused on output
   power and overall spectrum utilization.  For example, in 868 MHz
   spectrum, LoRa relies on transmit duty cycle (DC) limits (which range
   from 0.1% to 1%, depending on sub-band) to ensure efficient channel

   In some cases, listen-before-talk (LBT) has been mandated for
   unlicensed bands, including (optionally) 868MHz.  This results in a
   more complex regulatory structures, due to the need to specify
   detection thresholds, listening intervals, and backoff behaviors.
   The regulations specify minimum requirements, rather than a mechanism
   that is common to all networks.  This can lead to networks with
   different backoff behaviors sharing a channel.  Issues of
   compatibility and fairness between various LBT strategies are an
   active topic of study, notably with regard to WiFi and LTE
   coexistence in 5GHz spectrum (e.g.  [KYK16]).

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   The IETF community has a strong interest in ensuring that spectrum
   regulation not only enables efficient use of unlicensed spectrum for
   IoT applications, but also avoids overly prescriptive mandates that
   constrain diversity and innovation.

5.  Security Considerations

   An overview of security challenges in IoT environments is given in
   [I-D.irtf-t2trg-iot-seccons].  The current document focuses on
   coexistence between independently administrated networks operating in
   the same location.  The biggest security challenge for managing
   network interactions is that such networks do not necessarily have
   any basis for a trust relationship.

   Regulations concerning unlicensed spectrum only control radio
   behaviors such as transmit power and overall channel utilization.
   Regulations do not mandate the use of any specific protocol.  It is
   therefore not possible to externally enforce that networks
   participate in some specific coexistence protocol (as long as they
   otherwise comply with regulations).

   Most wireless protocols adapt their behavior to channel conditions to
   some extent, such as contention backoff, channel blacklisting, or re-
   routing.  But the more a network changes its behavior in response to
   small amounts of information from an untrusted source, the more
   leverage an attacker has to disrupt it.  Similarly, the more
   information about its future behavior a network provides to an
   untrusted destination, the easier it is for an attacker to disrupt
   it.  The risk is further exacerbated in energy-constrained networks,
   because a device may be forced to spend energy unnecessarily.  In
   addition, the high energy cost of listening to the channel makes it
   expensive to build trust by observing the behavior of other networks.

   Any proposed solution will therefore need to be resilient to the
   possibility of incompatible, oblivious, selfish, or even hostile
   networks when designing a coexistence mechanism.  This is especially
   true for methods in which two networks actively coordinate their use
   of the shared channel.  At a minimum, participating in information
   exchange should not substantially increase vulnerability to
   disruption in the case of a malicious (or merely incompatible) actor.

   In addition, networks that try to be friendly toward each other may
   disclose substantial information about their operation.  There are
   privacy issues associated with IoT networks making such information
   visible, because of their close coupling with human activity.
   Particularly for health-related applications, even being able to
   identify the type of application or its level of activity may reveal
   sensitive data.  Ideally, it should be possible for a network to both

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   obfuscate its communication patterns (if needed) and act

   One maxim that may be useful in designing the set of information that
   a network discloses as a matter of course with the intention of
   facilitating coexistence is that the information disclosed should not
   provide more insight than that information an attacker might have
   gained by simply observing the network for a while.  But note that
   simply disclosing that information in an accessible way still changes
   the economy of surveillance -- the objective is that it also changes
   the economy of coexistence, and these effects need to be carefully
   weighed against each other.

6.  Conclusion

   The future IoT operating environment will contain many diverse,
   administratively independent networks sharing unlicensed spectrum.
   Ensuring network coexistence is essential for avoiding the "tragedy
   of the commons" and enabling practical deployment of IoT solutions.

   The community currently lacks a good understanding of the impact of
   inter-network interactions, particularly with regard to protocol
   behavior and network-scale interactions.  However, recent results for
   both IEEE 802.15.4 PANs and 6TiSCH + RPL networks suggest that inter-
   network interactions can lead to episodes of significant disruption,
   even when the channel itself is not overloaded.  More research is
   needed into both the causes of adverse interactions and ways to
   mitigate them, particularly with regard to the role of higher layer

   Network coexistence is and will continue to be largely driven by
   spectrum regulation and the PHY and MAC layers.  However, this issue
   are also relevant to the work of IETF Working Groups, such as 6lo,
   6TiSCH, LPWAN, ROLL, CoRE, and LWIG.  We identify three areas where
   T2TRG can play a significant role:

   o  Performance evaluation should reflect that the IoT wireless
      environment will contain diverse interfering networks.  Tools and
      techniques for investigating inter-network interaction are still
      immature.  The community could benefit substantially from the
      development and documentation of best practices in this area.

   o  The results of such performance evaluation can assist IETF Working
      Groups in improving the resilience of IoT-related protocols.

   o  There may also be a role for novel network coexistence mechanisms
      based on information sharing or explicit coordination between
      networks.  This is a speculative research topic that seems

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      particularly amenable to standards and interoperability oriented
      approaches.  However, there are substantial challenges.

7.  Informative References

   [F3G15]    Feeney, L., Frey, M., Fodor, V., and M. Gunes, "Modes of
              inter-network interaction in beacon-enabled IEEE 802.15.4
              networks", 2015 14th Annual Mediterranean Ad Hoc
              Networking Workshop (MED-HOC-NET),
              DOI 10.1109/medhocnet.2015.7173294, June 2015.

   [FF16]     Feeney, L. and V. Fodor, "Reliability in co-located
              802.15.4 personal area networks", Proceedings of the 6th
              ACM International Workshop on Pervasive Wireless
              Healthcare - MobiHealth '16, DOI 10.1145/2944921.2944923,

              Kim, E. and S. Ziegler, "Towards an open framework of
              online interoperability and performance tests for the
              Internet of Things", 2017 Global Internet of Things
              Summit (GIoTS), DOI 10.1109/giots.2017.8016248, June 2017.

   [FITIOT]   Adjih, C., Baccelli, E., Fleury, E., Harter, G., Mitton,
              N., Noel, T., Pissard-Gibollet, R., Saint-Marcel, F.,
              Schreiner, G., Vandaele, J., and T. Watteyne, "FIT IoT-
              LAB: A large scale open experimental IoT testbed", 2015
              IEEE 2nd World Forum on Internet of Things (WF-IoT), DOI
              10.1109/wf-iot.2015.7389098, December 2015.

              Garcia-Morchon, O., Kumar, S., and M. Sethi, "State-of-
              the-Art and Challenges for the Internet of Things
              Security", draft-irtf-t2trg-iot-seccons-15 (work in
              progress), May 2018.

              Keranen, A., Kovatsch, M., and K. Hartke, "RESTful Design
              for Internet of Things Systems", draft-keranen-t2trg-rest-
              iot-05 (work in progress), September 2017.

   [KYK16]    Kim, C., Yang, C., and C. Kang, "Adaptive Listen-Before-
              Talk (LBT) scheme for LTE and Wi-Fi systems coexisting in
              unlicensed band", 2016 13th IEEE Annual Consumer
              Communications & Networking Conference (CCNC),
              DOI 10.1109/ccnc.2016.7444845, January 2016.

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   [NIST]     Koepke, G., Young, W., Ladbury, J., and J. Coder,
              "Interference and Coexistence of Wireless Systems in
              Critical Infrastructure", National Institute of Standards
              and Technology report, DOI 10.6028/, July

   [SEMCK14]  Skjegstad, M., Ellingsater, B., Maseng, T., Crowcroft, J.,
              and O. Kure, "Large-scale distributed Internet-based
              discovery mechanism for dynamic spectrum allocation", 2014
              IEEE International Symposium on Dynamic Spectrum Access
              Networks (DYSPAN), DOI 10.1109/dyspan.2014.6817824, April

   [SKH11]    Sum, C., Kojima, F., and H. Harada, "Coexistence of
              homogeneous and heterogeneous systems for IEEE 802.15.4g
              smart utility networks", 2011 IEEE International Symposium
              on Dynamic Spectrum Access Networks (DySPAN),
              DOI 10.1109/dyspan.2011.5936241, May 2011.

   [SURVEY]   Han, Y., Ekici, E., Kremo, H., and O. Altintas, "Spectrum
              sharing methods for the coexistence of multiple RF
              systems: A survey", Ad Hoc Networks Vol. 53, pp. 53-78,
              DOI 10.1016/j.adhoc.2016.09.009, December 2016.

   [SURVEY2]  Baccour, N., Puccinelli, D., Voigt, T., Koubaa, A., Noda,
              C., Fotouhi, H., Alves, M., Youssef, H., Zuniga, M.,
              Boano, C., and K. Roemer, "External Radio Interference",
              SpringerBriefs in Electrical and Computer Engineering pp.
              21-63, DOI 10.1007/978-3-319-00774-8_2, 2013.

   [TCG316]   Tinnirello, I., Croce, D., Galioto, N., Garlisi, D., and
              F. Giuliano, "Cross-Technology WiFi/ZigBee Communications:
              Dealing With Channel Insertions and Deletions", IEEE
              Communications Letters Vol. 20, pp. 2300-2303,
              DOI 10.1109/lcomm.2016.2603978, November 2016.

   [WETZ17]   Wetzker, U., Splitt, I., Zimmerling, M., Boano, C., and K.
              Romer, "Troubleshooting Wireless Coexistence Problems in
              the Industrial Internet of Things", 2016 IEEE Intl
              Conference on Computational Science and Engineering (CSE)
              and IEEE Intl Conference on Embedded and Ubiquitous
              Computing (EUC) and 15th Intl Symposium on Distributed
              Computing and Applications for Business
              Engineering (DCABES), DOI 10.1109/cse-euc-dcabes.2016.167,
              August 2016.

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   [YTB17]    Ben Yaala, S., Theoleyre, F., and R. Bouallegue,
              "Cooperative resynchronization to improve the reliability
              of colocated IEEE&#8239;802.15.4 -TSCH networks in dense
              deployments", Ad Hoc Networks Vol. 64, pp. 112-126,
              DOI 10.1016/j.adhoc.2017.07.002, September 2017.


   The authors would like to thank Michael Frey, Charalampos Orfanidis,
   Martin Jacobsson, and Per Gunningberg for their valuable
   collaboration in simulation and measurement studies of inter-network
   interference.  We would also like to thank Carsten Bormann for his
   support and encouragement in preparing this document, particularly
   the discussion of security considerations.  David Oran's detailed
   comments on the text are also much appreciated.

Authors' Addresses

   Laura Marie Feeney
   Uppsala University
   Box 337
   Uppsala  SE-751 05


   Viktoria Fodor
   Osquldas vaeg 10
   Stockholm  SE-100 44


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