



Internet Engineering Task Force (IETF)                   E. Birrane, III
Request for Comments: 9657                                       JHU/APL
Category: Informational                                          N. Kuhn
ISSN: 2070-1721                                      Thales Alenia Space
                                                                   Y. Qu
                                                  Futurewei Technologies
                                                               R. Taylor
                                                    Aalyria Technologies
                                                                L. Zhang
                                                                  Huawei
                                                            October 2024


                  Time-Variant Routing (TVR) Use Cases

Abstract

   This document introduces use cases where Time-Variant Routing (TVR)
   computations (i.e., routing computations that take into consideration
   time-based or scheduled changes to a network) could improve routing
   protocol convergence and/or network performance.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This document is a product of the Internet Engineering Task Force
   (IETF).  It represents the consensus of the IETF community.  It has
   received public review and has been approved for publication by the
   Internet Engineering Steering Group (IESG).  Not all documents
   approved by the IESG are candidates for any level of Internet
   Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at
   https://www.rfc-editor.org/info/rfc9657.

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Table of Contents

   1.  Introduction
   2.  Resource Preservation
     2.1.  Assumptions
     2.2.  Routing Impacts
     2.3.  Example
   3.  Operating Efficiency
     3.1.  Assumptions
     3.2.  Routing Impacts
     3.3.  Example: Cellular Network
     3.4.  Another Example: Tidal Network
   4.  Dynamic Reachability
     4.1.  Assumptions
     4.2.  Routing Impacts
     4.3.  Example: Mobile Satellites
     4.4.  Another Example: Predictable Moving Vessels
   5.  Security Considerations
   6.  IANA Considerations
   7.  Informative References
   Acknowledgments
   Authors' Addresses

1.  Introduction

   There is a growing number of use cases where changes to the routing
   topology are an expected part of network operations.  In these use
   cases, the pre-planned loss and restoration of an adjacency, or
   formation of an alternate adjacency, should be seen as a
   nondisruptive event.

   Expected changes to topologies can occur for a variety of reasons.
   In networks with mobile nodes, such as unmanned aerial vehicles and
   some orbiting spacecraft constellations, links are lost and re-
   established as a function of the mobility of the platforms.  In
   networks without reliable access to power, such as networks
   harvesting energy from wind and solar, link activity might be
   restricted to certain times of day.  Similarly, in networks
   prioritizing green computing and energy efficiency over data rate,
   network traffic might be planned around energy costs or expected user
   data volumes.

   This document defines three categories of use cases where a route
   computation might beneficially consider time information.  Each of
   these use cases are included as follows:

   1.  An overview of the use case describing how route computations
       might select different paths (or subpaths) as a function of time.

   2.  A set of assumptions made by the use case as to the nature of the
       network and data exchange.

   3.  Specific discussion on the routing impacts of the use case.

   4.  Example networks conformant to the use case.

   The use cases that are considered in this document are as follows:

   1.  Resource Preservation (described in Section 2), where there is
       information about link availability over time at the client
       level.  Time-Variant Routing (TVR) can utilize the predictability
       of the link availability to optimize network connectivity by
       taking into account endpoint resource preservation.

   2.  Operating Efficiency (described in Section 3), where there is a
       server cost or a path cost usage varying over time.  TVR can
       exploit the predictability of the path cost to optimize the cost
       of the system exploitation.  The notion of a path cost is
       extended to be a time-dependent function instead of a constant.

   3.  Dynamic Reachability (described in Section 4), where there is
       information about link availability variation between nodes in
       the end-to-end path.  TVR can exploit the predictability of the
       link availability to optimize in-network routing.

   The document does not intend to represent the full set of cases where
   TVR computations could beneficially impact network performance -- new
   use cases are expected to be generated over time.  Similarly, the
   concrete examples within each use case are meant to provide an
   existence proof of the use case and not to present any exhaustive
   enumeration of potential examples.  It is likely that multiple
   example networks exist that could be claimed as instances of any
   given use case.

   The document focuses on deterministic scenarios.  Non-deterministic
   scenarios, such as vehicle-to-vehicle communication, are out of the
   scope of the document.

2.  Resource Preservation

   Some nodes in a network might operate in resource-constrained
   environments or otherwise with limited internal resources.
   Constraints, such as available power, thermal ranges, and on-board
   storage, can all impact the instantaneous operation of a node.  In
   particular, resource management on such a node can require that
   certain functionality be powered on (or off) to extend the ability of
   the node to participate in the network.

   When power on a node is running low, noncritical functions on the
   node might be turned off in favor of extending node life.
   Alternatively, certain functions on a node may be turned off to allow
   the node to use available power to respond to an event, such as data
   collection.  When a node is in danger of violating a thermal
   constraint, normal processing might be paused in favor of a
   transition to a thermal safe mode until a regular operating condition
   is reestablished.  When local storage resources run low, a node might
   choose to expend power resources to compress, delete, or transmit
   data off the node to free up space for future data collection.  There
   might also be cases where a node experiences a planned offline state
   to save and accumulate power.

   In addition to power, thermal, and storage, other resource
   constraints may exist on a node such that the preservation of
   resources is necessary to preserve the existence (and proper
   function) of the node in the network.  Nodes operating in these
   conditions might benefit from TVR computations as the connectivity of
   the node changes over time as part of node preservation.

2.1.  Assumptions

   To effectively manage on-board functionality based on available
   resources, a node must comprehend specific aspects concerning the
   utilization and replenishment of resources.  It is expected that
   patterns of the environment, device construction, and operational
   configuration exist with enough regularity and stability to allow
   meaningful planning.  The following assumptions are made with this
   use case:

   1.  Known resource expenditures.  It is assumed that there exists
       some determinable relationship between the resources available on
       a node and the resources needed to participate in a network.  A
       node would need to understand when it has met some condition for
       participating in, or dropping out of, a network.  This is
       somewhat similar to predicting the amount of battery life left on
       a laptop as a function of likely future usage.

   2.  Predictable resource accumulation.  It is assumed that the
       accumulation of resources on a node are predictable such that a
       node might expect (and be able to communicate) when it is likely
       to next rejoin a network.  This is similar to predicting the time
       at which a battery on a laptop will be fully charged.

   3.  Consistent cost functions.  It is assumed that resource
       management on a node is deterministic such that the management of
       a node as a function of resource expenditure and accumulation is
       consistent enough for link planning.

2.2.  Routing Impacts

   Resource management in these scenarios might involve turning off
   elements of the node as part of on-board resource management.  These
   activities can affect data routing in a variety of ways.

   1.  Power Savings.  On-board radios may be turned off to allow other
       node processing.  This may happen on power-constrained devices to
       extend the battery life of the node or to allow a node to perform
       some other power-intensive task.

   2.  Thermal Savings.  On-board radios may be turned off if there are
       thermal considerations on the node, such as an increase in a
       node's operating temperature.

   3.  Storage Savings.  On-board radios may be turned on with the
       purpose of transmitting data off the node to free local storage
       space to collect new data.

   Whenever a communications device on a node changes its powered state
   there is the possibility (if the node is within range of other nodes
   in a network) that the topology of the network is changed, which
   impacts route calculations through the network.  Additionally,
   whenever a node joins a network there may be a delay between the
   joining of the node to the network and any discovery that may take
   place relating to the status of the node's functional neighborhood.
   During these times, forwarding to and from the node might be delayed
   pending some synchronization.

2.3.  Example

   An illustrative example of a network necessitating resource
   preservation is an energy-harvesting wireless sensor network.  In
   such a network, nodes rely exclusively on environmental sources for
   power, such as solar panels.  On-board power levels may fluctuate
   based on various factors including sensor activity, processing
   demands, and the node's position and orientation relative to its
   energy source.

   Consider a simple three-node network where each node accumulates
   power through solar panels.  Power available for radio frequency (RF)
   transmission is shown in Figure 1.  In this figure, each of the three
   nodes (Node 1, Node 2, and Node 3) has a different plot of available
   power over time.  This example assumes that a node will not power its
   radio until available power is over some threshold, which is shown by
   the horizontal line on each plot.

            Node 1                   Node 2                   Node 3
 P |                      P |   -------            P |          --
 o |  ----       --       o |  /       \           o |         /  \
 w |~/~~~~\~~~~~/~~\~~    w |~/~~~~~~~~~\~~~~~~    w |~~~~~~~~/~~~~\~~~~
 e |/      \   /    \     e |/           \         e |       /      \
 r |        ---      -    r |             -----    r |-------        ---
   +---++----++----++-      +---++----++----++-      +---++----++----++-
       t1    t2    t3           t1    t2    t3           t1    t2    t3
            Time                     Time                     Time

                     Figure 1: Node Power over Time

   The connectivity of this three-node network changes over time in ways
   that may be predictable and are likely able to be communicated to
   other nodes in this small sensor network.  Examples of connectivity
   are shown in Figure 2.  This figure shows a sample of network
   connectivity at three times: t1, t2, and t3.

   *  At time t1, Node 1 and Node 2 have their radios powered on and are
      expected to communicate.

   *  At time t2, it is expected that Node 1 has its radio off but that
      Node 2 and Node 3 can communicate.

   *  Finally, at time t3, it is expected that Node 1 may be turning its
      radio off, that Node 2 and Node 3 are not powering their radios,
      and there is no expectation of connectivity.

              +----------+        +----------+        +----------+
         t1   |  Node 1  |--------|  Node 2  |        |  Node 3  |
              +----------+        +----------+        +----------+

              +----------+        +----------+        +----------+
         t2   |  Node 1  |        |  Node 2  |--------|  Node 3  |
              +----------+        +----------+        +----------+

              +----------+        +----------+        +----------+
         t3   |  Node 1  |        |  Node 2  |        |  Node 3  |
              +----------+        +----------+        +----------+

                        Figure 2: Topology over Time

3.  Operating Efficiency

   Some nodes in a network might alter their networking behavior to
   optimize metrics associated with the cost of a node's operation.
   While the resource preservation use case described in Section 2
   addresses node survival, this use case discusses non-survival
   efficiencies such as the financial cost to operate the node and the
   environmental impact (cost) of using that node.

   When a node operates using some preexisting infrastructure, there is
   typically some cost associated with the use of that infrastructure.
   Sample costs are included as follows:

   1.  Nodes that use existing wireless communications, such as a
       cellular infrastructure, must pay to communicate to and through
       that infrastructure.

   2.  Nodes supplied with electricity from an energy provider pay for
       the power they use.

   3.  Nodes that cluster computation and activities might increase the
       temperature of the node and incur additional costs associated
       with cooling the node (or collection of nodes).

   4.  Beyond financial costs, assessing the environmental impact of
       operating a node may also be modeled as a cost associated with
       node operation, to include achieving carbon credits or other
       incentives for green computing.

   When the cost of using a node's resources changes over time, a node
   can benefit from predicting when data transmissions might optimize
   costs, environmental impacts, or other metrics associated with
   operation.

3.1.  Assumptions

   The ability to predict the impact of a node's resource utilization
   over time presumes that the node exists within a defined environment
   (or infrastructure).  Some characteristics of these environments are
   listed as follows:

   1.  Cost Measurability.  The impacts of operating a node within its
       environment can be measured in a deterministic way.  For example,
       the cost-per-bit of data over a cellular network or the cost-per-
       kilowatt of energy used are known.

   2.  Cost Predictability.  Changes to the impacts of resource
       utilization are known in advance.  For example, if the cost of
       energy is less expensive in the evening than during the day,
       there exists some way of communicating this change to a node.

   3.  Cost Persistent.  Changes to the cost of operating in the
       environment persist for a sufficient amount of time such that
       behavior can be adjusted in response to changing costs.  If costs
       change too rapidly, it is likely not possible to meaningfully
       react to their change.

   4.  Cost Magnitude.  The magnitude of cost changes is such that a
       node experiences a minimum threshold cost reduction through
       optimization.  A specified time period is designated for
       measuring the cost reduction.

3.2.  Routing Impacts

   Optimizing resource utilization can affect route computation in ways
   similar to those experienced with resource preservation.  The route
   computation may not change the available path, but the topology as
   seen by an endpoint would be different.  Cost optimization can impact
   route calculation in a variety of ways, some of which are described
   as follows:

   1.  Link Filtering.  Data might be accumulated on a node waiting for
       a cost-effective time for data transmission.  Individual link
       costs might be annotated with cost information such that
       adjacencies with a too high cost might not be used for
       forwarding.  This effectively filters which adjacencies are used
       (possibly as a function of the type of data being routed).

   2.  Burst Planning.  In cases where there is a cost savings
       associated with fewer longer transmissions (versus many smaller
       transmissions), nodes might refuse to forward data until a
       sufficient data volume exists to justify a transmission.

   3.  Environmental Measurement.  Nodes that measure the quality of
       individual links can compute the overall cost of using a link as
       a function of the signal strength of the link.  If link quality
       is insufficient due to environmental conditions (such as clouds
       on a free-space optical link or long distance RF transmission in
       a storm) the cost required to communicate over the link may be
       too much, even if access to infrastructure is otherwise in a less
       expensive time of day.

   In each of these cases, some consideration of the efficiency of
   transmission is prioritized over achieving a particular data rate.
   Waiting until data rate costs are lower takes advantage of platforms
   using time-of-use rate plans -- both for pay-as-you-go data and
   associated energy costs.  Accumulating data volumes and choosing more
   opportune times to transmit can also result in less energy
   consumption by radios and, thus, less operating cost for platforms.

3.3.  Example: Cellular Network

   One example of a network where nodes might seek to optimize operating
   cost is a set of nodes operating over cellular connections that
   charge both peak and off-peak data rates.  In this case, individual
   nodes may be allocated a fixed set of "peak" minutes such that
   exceeding that amount of time results in expensive overage charges.
   Generally, the concept of peak and off-peak minutes exists to deter
   the use of a given network at times when the cellular network is
   likely to encounter heavy call volumes (such as during the workday).

   Just as pricing information can act as a deterrent (or incentive) for
   a human cellular user, this pricing information can be codified in
   ways that also allow machine-to-machine (M2M) connections to
   prioritize off-peak communications for certain types of data
   exchange.  Many M2M traffic exchanges involve schedulable activities,
   such as nightly bulk file transfers, pushing software updates,
   synchronizing datastores, and sending noncritical events and logs.
   These activities are usually already scheduled to minimize impact on
   businesses and customers but can also be scheduled to minimize
   overall cost.

   Consider a simple three-node network, similar to the one pictured in
   Figure 1, except that in this case the resource that varies over time
   is the cost of the data exchange.  This case is illustrated below in
   Figure 3.  In this figure, a series of three plots are given, one for
   each of the three nodes (Node 1, Node 2, and Node 3).  Each of these
   nodes exists in a different cellular service area that has different
   peak and off-peak data rate times.  This is shown in each figure by
   times when the cost is low (off-peak) and when the cost is high
   (peak).

   Node 1                 Node 2                  Node 3

 C |       +---------   C |--+                  C |-------------+
 o |       |            o |  |                  o |             |
 s |       |            s |  |                  s |             |
 t |-------+            t |  +----------------  t |             +-------
   |                      |                       |
   +---++----++----++--   +----++----++----++--   +----++----++-----++--
       t1    t2    t3          t1    t2    t3          t1    t2     t3
            Time                    Time                    Time

                     Figure 3: Data Cost over Time

   Given the presumption that peak times are known in advance, the cost
   of data exchange from Node 1 through Node 2 to Node 3 can be
   calculated.  Examples of these data exchanges are shown in Figure 4.
   From this figure, both times t1 and t3 result in a smaller cost of
   data exchange than choosing to communicate data at time t2.

          +-----------+          +-----------+          +-----------+
     t1   |  Node N1  |---LOW----|  Node N2  |---HIGH---|  Node N3  |
          +-----------+          +-----------+          +-----------+

          +-----------+          +-----------+          +-----------+
     t2   |  Node N1  |---HIGH---|  Node N2  |---HIGH---|  Node N3  |
          +-----------+          +-----------+          +-----------+

          +-----------+          +-----------+          +-----------+
     t3   |  Node N1  |---HIGH---|  Node N2  |----LOW---|  Node N3  |
          +-----------+          +-----------+          +-----------+

                   Figure 4: Data Exchange Cost over Time

   While not possible in every circumstance, a highly optimized plan
   could be to communicate from Node 1 to Node 2 at time t1 and then
   queue data at Node 2 until time t3 for delivery to Node 3.  This case
   is shown in Figure 5.

          +-----------+          +-----------+
     t1   |  Node N1  |---LOW----|  Node N2  |
          +-----------+          +-----------+
                                 +-----------+          +-----------+
     t3                          |  Node N2  |----LOW---|  Node N3  |
                                 +-----------+          +-----------+

                     Figure 5: Data Cost Using Storage

3.4.  Another Example: Tidal Network

   Another example related to operating efficiency is often referred to
   as a "tidal network," in which traffic volume undergoes significant
   fluctuations at different times.  Take, for instance, a campus
   network, where thousands of individuals go to classrooms and
   libraries during the daytime and retire to the dormitories at night.
   This results in a regular oscillation of network traffic across
   various locations within the campus.

   In the context of a tidal network scenario, energy-saving methods may
   include the deactivation of some or all components of network nodes.
   These activities have the potential to alter network topology and
   impact data routing in a variety of ways.  Ports on network nodes can
   be selectively disabled or enabled based on traffic patterns, thereby
   reducing the energy consumption of nodes during periods of low
   network traffic.

   More information on tidal networks can be found in [TIDAL].

4.  Dynamic Reachability

   When a node is placed on a mobile platform, the mobility of the
   platform (and thus the mobility of the node) may cause changes to the
   topology of the network over time.  The impacts on the dynamics of
   the topology can be very important.  To the extent that the relative
   mobility between and among nodes in the network and the impacts of
   the environment on the signal propagation can be predicted, the
   associated loss and establishment of adjacencies can also be planned
   for.

   Mobility can cause the loss of an adjacent link in several ways, such
   as that which follows:

   1.  Node mobility can cause the distance between two nodes to become
       large enough that distance-related attenuation causes the mobile
       node to lose connectivity with one or more other nodes in the
       network.

   2.  Node mobility can also be used to maintain a required distance
       from other mobile nodes in the network.  While moving, external
       characteristics may cause the loss of links through occultation
       or other hazards of traversing a shared environment.

   3.  Node mobility can cause the distance between two nodes to vary
       quickly over time, making it complicated to establish and
       maintain connectivity.

   4.  Nodes equipped with communication terminals capable of adjusting
       their orientation or moving behind and emerging from barriers
       will also establish and lose connectivity with other nodes as a
       function of that motion.

   Mobile nodes, like any node, may encounter issues regarding resource
   preservation and cost efficiency.  In addition, they may face unique
   challenges associated with their mobility.  The intermittent
   availability of links can lead to dynamic neighbor relationships at
   the node level.  This use case aims to examine the routing
   implications of motion-induced changes to network topology.

4.1.  Assumptions

   Predicting the impact of node mobility on route computation requires
   some information relating to the nature of the mobility and the
   nature of the environment being moved through.  Some information
   presumed to exist for planning is listed as follows:

   1.  Path Predictability.  The path of a mobile node through its
       environment is known (or can be predicted) as a function of (at
       least) time.  It is presumed that mobile nodes using TVR
       algorithms would not exhibit purely random motion.

   2.  Environmental Knowledge.  When otherwise well-connected mobile
       nodes pass through certain elements of their environment (such as
       a storm, a tunnel, or the horizon), they may lose connectivity.
       The duration of this connectivity loss is assumed to be
       calculable as a function of node mobility and the environment
       itself.

4.2.  Routing Impacts

   Changing a network topology affects the computation of paths (or
   subpaths) through that topology.  In particular, the following
   features can be implemented in a network with mobile nodes such that
   different paths might be computed over time:

   1.  Adjacent Link Expiration.  A node might be able to predict that
       an adjacency will expire as a function of that node's mobility,
       the other node's mobility, or some characteristic of the
       environment.  Determining that an adjacency has expired allows a
       route computation to plan for that loss rather than default to an
       error recovery mechanism.

   2.  Adjacent Link Resumption.  Just as the loss of an adjacency can
       be predicted, it may be possible to predict when an adjacency
       will resume.

   3.  Data Rate Adjustments.  The achievable data rate over a given
       link is not constant over time and may vary significantly as a
       function of both relative mobility between a transmitter and
       receiver as well as the environment being transmitted through.
       Knowledge of both mobility and environmental state may allow for
       prediction of data rates, which may impact path computation.

   4.  Adjacent Link Filtering.  Separate from the instantaneous
       presence or absence of an adjacency, a route computation might
       choose to not use an adjacency if that adjacency is likely to
       expire in the near future or if it is likely to experience a
       significant drop in predicted data rate.

4.3.  Example: Mobile Satellites

   A relatively new type of mobile network that has emerged over the
   past several years is the Low Earth Orbit (LEO) networked
   constellation.  There are a number of such constellations being built
   by both private industry and governments.  While this example
   describes LEO satellite systems, the mobility events can be applied
   to satellite systems orbiting at different altitudes (including Very
   LEO (V-LEO) or Medium Earth Orbit (MEO)).

   Many LEO networked constellations have a similar operational concept
   of hundreds to thousands of inexpensive spacecraft that can
   communicate both with their orbital neighbors as well as down to any
   ground station that they happen to be passing over.  A ground station
   is a facility used to communicate with satellites in LEO.  The
   relationship between an individual spacecraft and an individual
   ground station becomes somewhat complex as each spacecraft may only
   be over a single ground station for a few minutes at a time.
   Moreover, as a function of the constellation topology, there are
   scenarios where (1) the inter-satellite links need to be shut down
   for interference avoidance purposes or (2) the network topology
   changes, which modifies the neighbors of a given spacecraft.

   A LEO networked constellation represents a good example of planned
   mobility based on the predictability of spacecraft in orbit.  While
   other mobile vehicles may encounter unpredictable fluctuations in
   velocity, spacecraft operate in an environment with relatively stable
   velocity conditions.  This determinism makes them an excellent
   candidate for TVR computations.  However, inter-satellite link
   failures could still introduce unpredictability in the network
   topology.

   Consider three spacecraft (N1, N2, and N3) following each other
   sequentially in the same orbit.  This is sometimes called a "string
   of pearls" configuration.  Spacecraft N2 always maintains
   connectivity to its two neighbor spacecraft: N1, which is behind in
   the orbit, and N3, which is ahead in the orbit.  This configuration
   is illustrated in Figure 6.  While these spacecraft are all mobile,
   their relative mobility ensures continuous contact with each other
   under normal conditions.

          .--.                     .--.                     .--.
    ####-| N1 |-####  <--->  ####-| N2 |-####  <--->  ####-| N3 |-####
          \__/                     \__/                     \__/

                   Figure 6: Three Sequential Spacecraft

   Flying over a ground station imposes a non-relative motion between
   the ground and the spacecraft -- namely that any given ground station
   will only be in view of the spacecraft for a short period of time.
   The times at which each spacecraft can see the ground station is
   shown in the plots in Figure 7.  In this figure, ground contact is
   shown when the plot is high, and a lack of ground contact is shown
   when the graph is low.  From this, we see that spacecraft N3 can see
   ground at time t1, N2 sees ground at time t2, and spacecraft N1 sees
   ground at time t3.

       Spacecraft N1           Spacecraft N2            Spacecraft N3
G |                     G |                      G |
r |              +--+   r |         +--+         r |   +--+
o |              |  |   o |         |  |         o |   |  |
u |              |  |   u |         |  |         u |   |  |
n |--------------+  +-  n |---------+  +-------  n |---+  +-------------
d |                     d |                      d |
  +---++----++----++--    +----++----++----++--    +----++----++----++--
      t1    t2    t3           t1    t2    t3           t1    t2    t3
           Time                     Time                     Time

            Figure 7: Spacecraft Ground Contacts over Time

   Since the ground station in this example is stationary, each
   spacecraft will pass over it, resulting in a change to the network
   topology.  This topology change is shown in Figure 8.  At time t1,
   any message residing on N3 and destined for the ground could be
   forwarded directly to the ground station.  At time t2, that same
   message would need to, instead, be forwarded to N2 and then forwarded
   to ground.  By time t3, the same message would need to be forwarded
   from N2 to N1 and then down to ground.

        +------+          +------+
    t1  |  N2  |----------|  N3  |
        +------+          +---+--+
                              |
                             /|\
                            \___/
                             / \
                           Ground
                           Station
    ------------------------------------------------------------------
        +------+          +------+          +------+
    t2  |  N1  |----------|  N2  |----------|  N3  |
        +------+          +---+--+          +------+
                              |
                             /|\
                            \___/
                             / \
                           Ground
                           Station
    ------------------------------------------------------------------
                          +------+          +------+          +------+
    t3                    |  N1  |----------|  N2  |----------|  N3  |
                          +---+--+          +------+          +------+
                              |
                             /|\
                            \___/
                             / \
                           Ground
                           Station
    ------------------------------------------------------------------

                 Figure 8: Constellation Topology over Time

   This example focuses on the case where the spacecrafts fly over a
   ground station and introduce changes in the network topology.  There
   are also scenarios where the in-constellation network topology varies
   over time following a deterministic time-driven operation from the
   ground system.  More information on in-constellation network topology
   can be found in [SAT-CONSTELLATION] and [SCN].  For this example, and
   in particular for within constellation network topology changes, the
   TVR approach is important to avoid the Interior Gateway Protocol
   (IGP) issues mentioned in [SAT-CONSTELLATION].

4.4.  Another Example: Predictable Moving Vessels

   Another relevant example for this use case involves the movement of
   vessels with predictable trajectories, such as ferries or planes.
   These endpoints often rely on a combination of satellite and
   terrestrial systems for Internet connectivity, capitalizing on their
   predictable journeys.

   This scenario also covers situations where nodes employ dynamic
   pointing solutions to track the mobility of other nodes.  In such
   cases, nodes dynamically adjust their antennas and application
   settings to determine the optimal timing for data transmission along
   the path.

5.  Security Considerations

   While this document does not define a specific mechanism or solution,
   it serves to motivate the use of time-based validation and revocation
   strategies.  Therefore, security considerations are anticipated to be
   addressed elsewhere, such as within a TVR schedule definition or
   through a protocol extension utilizing a TVR schedule.  However, it's
   important to note that time synchronization is critical within a
   network employing a TVR schedule.  Any unauthorized changes to
   network clocks can disrupt network functionality, potentially leading
   to a Denial of Service (DoS) attack.

6.  IANA Considerations

   This document has no IANA actions.

7.  Informative References

   [SAT-CONSTELLATION]
              Han, L., Li, R., Retana, A., Chen, M., Su, L., and T.
              Jiang, "Problems and Requirements of Satellite
              Constellation for Internet", Work in Progress, Internet-
              Draft, draft-lhan-problems-requirements-satellite-net-06,
              4 January 2024, <https://datatracker.ietf.org/doc/html/
              draft-lhan-problems-requirements-satellite-net-06>.

   [SCN]      Wood, L., "Satellite Constellation Networks",
              Internetworking and Computing over Satellite Networks, pp.
              13-34, DOI 10.1007/978-1-4615-0431-3_2, April 2003,
              <https://link.springer.com/
              chapter/10.1007/978-1-4615-0431-3_2>.

   [TIDAL]    Zhang, L., Zhou, T., Dong, J., and N. Nzima, "Use Case of
              Tidal Network", Work in Progress, Internet-Draft, draft-
              zzd-tvr-use-case-tidal-network-02, 28 July 2023,
              <https://datatracker.ietf.org/doc/html/draft-zzd-tvr-use-
              case-tidal-network-02>.

Acknowledgments

   Many thanks to Tony Li, Peter Ashwood-Smith, Abdussalam Baryun,
   Arashmid Akhavain, Dirk Trossen, Brian Sipos, Alexandre Petrescu,
   Haoyu Song, Hou Dongxu, Tianran Zhou, Jie Dong, Nkosinathi Nzima, and
   Vinton Cerf for their useful comments that helped improve the
   document.

Authors' Addresses

   Edward J. Birrane, III
   JHU/APL
   Email: edward.birrane@jhuapl.edu


   Nicolas Kuhn
   Thales Alenia Space
   Email: nicolas.kuhn.ietf@gmail.com


   Yingzhen Qu
   Futurewei Technologies
   Email: yingzhen.ietf@gmail.com


   Rick Taylor
   Aalyria Technologies
   Email: rtaylor@aalyria.com


   Li Zhang
   Huawei
   Email: zhangli344@huawei.com
