Comm Architecture for Space-Based Networks

1
An Efficient Layer 2 Mesh Communications Protocol
for Space Sensor Networks
Loren Clare, Jay Gao, Esther Jennings, and
Clayton Okino Jet Propulsion
Laboratory, California Institute of
Technology Presented at Space Internet
Workshop Hanover, Maryland 8-10 June 2004
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Outline

• The need for multi-spacecraft sensing
• Distributed spacecraft mission types
• Why network?
• Networking solution approach, described through
  an example
• Extension to Demand-Driven traffic scheduling
• Conclusions

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Multi-Spacecraft Sensing Missions

• Many phenomena can only be measured using
  multipoint sensing
• multiple sensors that are
• spread over a spatial regime of interest and
• simultaneously measure the target phenomena
• The need for multipoint (multi-spacecraft)
  sensing has long been recognized
• Space Science Board of the NAS in 1974 for
  large-scale geospace phenomena (space
  weather)
• Interplanetary Monitoring Platform (IMP-7 and
  IMP-8) s/c launched in early 70s
• International Sun-Earth Explorer (ISEE) 3
  spacecraft late 70s
• able to break the space-time ambiguity
  inevitably associated with measurements by a
  single spacecraft on thin boundaries which may be
  in motion, such as the bow shock and the
  magnetopause.
• Dynamics Explorer (DE) 2 spacecraft launched
  1981
• Many subsequent missions (GEOTAIL, WIND,
  INTERBALL, SOHO, POLAR, Cluster,)
• Space Studies Board (NRC) decadal strategy August
  2002 7 of 9 recommended moderate-class programs
  are multi-spacecraft
• 2003 SSE Strategy Constellation technology must
  be developed to permit collecting data
  efficiently and simultaneously at dispersed
  locations
• Sensor Web concept is critical component of
  Earth Science strategic plan

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Multipoint Sensing Classes

• Multipoint sensing applications fall into 3
  classes

Pixellation/Voxellation of space
Beamformation
Tomography/Rendering
Each class has associated data collection and
processing needs for combining the multiple
sensor signals gt different traffic models
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Additional Reasons for Distributed Sensing

• Coverage of large (possibly sculpted) area via
  union of many spatially dispersed sensors
• Incremental sizing (evolution/extension,
  replenishment)
• In situ sensing mitigates sensor range
  limitations and overcome ambient environmental
  noise
• Speed through parallel actions
• Fault tolerance
• Mix multiple sensor modalities at appropriate
  densities

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Why Use a Communications Network?

• Why not just store data and dump at perigee?
• Incorporating intersatellite links and networking
  enables
• Access to any/all spacecraft in the
  multi-spacecraft mission is continuously provided
  via single ground contact with any spacecraft
• Increases ground operations efficiency
• Enables automated operation of the whole act
  as a single mission spacecraft for coordinated
  observations
• Real-time coordinated observations and processing
• Alert/cue ground-based assets (e.g., gamma ray
  bursts)
• E.g., on March 29, 2003 the High-Energy Transient
  Explorer (HETE) detected a gamma burst and cued
  the European Southern Observatory's Very Large
  Telescope, which confirmed a correlated supernova
  explosion (http//www.gsfc.nasa.gov/topstory/2003/
  0618rosettaburst.html) Gamma Ray Burst
  Coordinate Distribution Network 10-20 second
  latency
• Event-based interactions among distributed sensor
  spacecraft
• cueing, data aggregation (compression), fusion
  (improves resource use)
• Autonomous cooperative processes among
  distributed spacecraft
• precision navigation constellation control and
  reconfiguration
• network time synchronization for precise
  time-stamping of sensor data

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What If No Crosslinks?
Suppose there are no crosslinks. Data is stored
onboard and each s/c dumps its data to Earth when
it is near perigee. Data delivery latency is
therefore approximately equal to the orbital
period of the spacecraft. For example, for the
MagCon mission, worst case is
Note that storage requirements are substantial,
in addition to age of data.
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Uniqueness of Space-Based Sensor Networks

• Differences from conventional networks
• Nodes are moving, although deterministically
• Unlike typical sensor networks, topology is
  dynamic
• Unlike ad hoc networks, motion (and topology) is
  predictable
• Unlike typical sensor networks, have natural
  load-balancing
• Long ranges between adjacent nodes
• Must use directional transmit and receive
  antennas
• Largely ignored in literature, although some
  recent interest (e.g. for FCS) no known sensor
  network results
• Multihop needed for ground operations efficiency
  and communications energy efficiency

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Assumptions

• Sensor network, with
• traffic originating at satellite nodes and
  destined to multiple ground stations on Earth,
  and
• traffic originating at Earth stations and
  destined to satellites
• Supports half-duplex or full-duplex operation
• Directional antennas are used, so that hidden
  terminal interference does not arise
• Network is synchronized

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Technical Approach
0. Obtain potential topology G
1. Grow branches rooted at satellites that are
1-hop away from any ground station
2. Compute the total load of a subtree rooted at
each node
3. Load-balancing among different branches
4. Attach branches to ground stations (min.
schedule)
5. Load-balancing among ground stations
Cannot balance to improve schedule
6. Generate schedule from tree using
Florens -McEliece algorithm
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Derive Node Locations
Example 16-satellite, 3-ground stations
configuration
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Grow Branches
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Load-Balancing Among Branches
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Load-Balancing Among Branches (cont)
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Attach to Ground Stations
No improvements can be mad by load balancing
among the ground stations (step 5)
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Generate Schedule for Tree
An algorithm for deriving an optimal
(shortest-length) schedule for each tree rooted
at a ground station with half-duplex directional
links has been developed Cedric Florens and
Robert McEliece, Scheduling algorithms for
wireless ad-hoc sensor networks, Proceedings of
IEEE GLOBECOM 2002, Dec. 1-5, 2002 This
algorithm holds for general traffic load
distribution We apply this algorithm to each
tree to obtain the final schedule
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Example Schedule Table
Schedule for 16-satellite example
? 15 time slots to deliver all 16 packets
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Mitigation of Propagation Delays
Directionality of path flows permits schedule to
be adjusted to remove effects of propagation
delays

• Operation
• Pull data from all satellites to Earth
• Push Earth commands/data to satellites
• Propagation losses only occur in transitions
  between these two operational modes
• Can be applied to either Half-Duplex or
  Full-Duplex systems

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Propagation Delays (Half Duplex)
15
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Propagation Delays (Full Duplex)
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Simulation
A simulation was developed for performance
characterization

• Simulation execution
• General topologies derived from random spatial
  distribution and inter-node range constraints
• Traffic load generated from statistical model
• Tree optimization algorithm executed
• Link activation/routing schedule derived
• Measure statistics on schedule length and
  throughput performance

Example Topology
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Simulation Results
Performance Improvement using Optimized Tree
Algorithm
1 ground
8 ground
6 ground
4 ground
2 ground
1 ground
8 ground
6 ground
4 ground
2 ground
station
stations
stations
stations
stations
station
stations
stations
stations
stations
100.
33.17
40.68
49.76
73.38
100.
33.17
40.68
49.76
73.38
Schedule length using optimized tree algorithm
159.16
47.92
59.73
77.34
113.52
159.16
47.92
59.73
77.34
113.52
Schedule length without optimized tree algorithm
59.2
44.5
46.8
55.4
54.7
59.2
44.5
46.8
55.4
54.7
Percent length
Percent length
increase
increase
Schedule Length versus Number of Ground Stations
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Simulation Results (continued)
Performance Improvement using Optimized Tree
Algorithm
Schedule Length versus Number of Ground Stations
Schedule Length versus Network Size
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Simulation Results (continued)
Schedule Length versus Number of Ground Stations
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Summary

• Space-based sensor networks are emerging in order
  to enable new science requiring multipoint
  measurement
• Interspacecraft communications (networking) will
  enable
• Continuous access to any/all spacecraft in the
  multi-spacecraft mission via single ground
  contact with any spacecraft, thereby increasing
  ground operations efficiency and enabling
  automated operation of the whole
• Real-time coordinated observations are made
  possible, such as alerting/cueing ground-based
  assets
• Autonomous operations/processing among
  distributed spacecraft including precision
  navigation and formation control and
  reconfiguration
• Presented a layer 2 mesh link activation/routing
  algorithm that maximizes throughput and minimizes
  latency