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 WorkshopHanover, Maryland

8-10 June 2004

2

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

3

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

4

Multipoint Sensing Classes

Multipoint sensing applications fall into 3 classes:

Each class has associated data collection and processing needs for combining the multiple sensor signals => different traffic models

Pixellation/Voxellation of space

Beamformation

Tomography/Rendering

5

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

6

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

7

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

days 84.1489795.1282453

/10986004.3

10137.637840222/12314

2/33

2/1

2/3

s

sm

maT

mission MC in orbit largest for /sm 10 3.986004 2314eRaGM 40,

Note that storage requirements are substantial, in addition to age of data.

8

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

9

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

10

Technical Approach

0. Obtain potentialtopology G

6. Generate schedule fromtree using Florens

-McEliece algorithm

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

11

Derive Node Locations

Example 16-satellite, 3-ground stations configuration

C

415

3

14

2

11

1

10

9 G

8

7

65

12

13

16

MC

415

3

14

2

11

1

10

9 G

8

7

65

12

13

16

M

12

Grow Branches

2

3

4

5

12

14

15 16

1 9 10

13

6 7 8

11

111

11

2

4

10

13

111

4

21

1

Lbranch(1) = 1 Lbranch(2) = 13 Lbranch(3) = 2

13

Load-Balancing Among Branches

2

3

4

5

12

14

15 16

1 9 10

13

6 7 8

11

111

11

2

4

10

13

111

4

21

1

Lbranch(1) = 1 Lbranch(2) = 13 Lbranch(3) = 2

14

Load-Balancing Among Branches (cont)

2

3

4

514

15 16

1 9 10

13 11 12

6 7 8

9 61

Lbranch(1) = 1 Lbranch(2) = 9 Lbranch(3) = 6

111 111

44 11

26 5

15

Attach to Ground Stations

2

3

4

5

15 16

1 9 10

13 12

6 7 8

7 81

111 11

64 1

6 7

14

11

1

21

Canberra GoldstoneNo improvements can be mad by load balancing among the ground stations (step 5)

16

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

17

Example Schedule Table

Schedule for 16-satellite example:

→ 15 time slots to deliver all 16 packets

1615

121615

1216152

2121615

14

12

10

8

21216156

1216154

16C152

G15

GC513

GC514311

GC539

GC537

GC535

GC353

GC1

16151413121110987654321

1615

121615

1216152

2121615

14

12

10

8

21216156

1216154

16C152

G15

GC513

GC514311

GC539

GC537

GC535

GC353

GC1

16151413121110987654321

Tim

e

Nodes

18

Mitigation 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

Directionality of path flows permits schedule to be adjusted to remove

effects of propagation delays

19

Propagation Delays (Half Duplex)

15C 4 3 14 2 11 1 10 9O

ne

Cyc

le o

f S

ched

ule Canberra

20

Propagation Delays (Full Duplex)

C 4 15 3 14 2

11 1 10 9

On

e C

ycle

of

Sch

edu

le Canberra

21

Simulation

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

A simulation was developed for performance characterization

22

Simulation Results

59.2%

159.16

100.

1 ground station

44.5%46.8%55.4%54.7%Percent length increase

47.9259.7377.34113.52

33.1740.6849.7673.38

8 ground stations

6 ground stations

4 ground stations

2 ground stations

59.2%

159.16

100.

1 ground station

44.5%46.8%55.4%54.7%Percent length increase

47.9259.7377.34113.52

33.1740.6849.7673.38Schedule length using

optimized tree algorithm

8 ground stations

6 ground stations

4 ground stations

2 ground stations

Performance Improvement using Optimized Tree Algorithm

Schedule length without optimized tree

algorithm

Schedule Length versus Number of Ground Stations

23

Simulation Results (continued)

2 Ground Stations

1 Ground Station

47.6%52.6%56.3%59.2%Percent length increase

70.68 70.5471.9673.38Arbitrary algorithm schedule

length

100.75104.31107.66113.52Arbitrary algorithm schedule

length

54.7%

159.16

100.

20 nodes

42.5%47.9%49.6%Percent length increase

147.62152.58156.28Arbitrary algorithm schedule

length

100.100.100.Proposed algorithm schedule

length

80 nodes60 nodes40 nodes

2 Ground Stations

1 Ground Station

47.6%52.6%56.3%59.2%Percent length increase

70.68 70.5471.9673.38Arbitrary algorithm schedule

length

100.75104.31107.66113.52Arbitrary algorithm schedule

length

54.7%

159.16

100.

20 nodes

42.5%47.9%49.6%Percent length increase

147.62152.58156.28Arbitrary algorithm schedule

length

100.100.100.Proposed algorithm schedule

length

80 nodes60 nodes40 nodes

Performance Improvement using Optimized Tree Algorithm

Schedule Length versus Network Size

Sch

edu

le L

en

gth

ver

sus

Nu

mb

er o

f G

rou

nd

Sta

tio

ns

24

Simulation Results (continued)

Schedule Length Distribution

(20 nodes)

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250

Time Slots

2 GS

4 GS

6 GS

8 GS

Schedule Length versus Number of Ground Stations

25

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