1. A Preliminary Proposal
A Method of Forming Self-Healing Patterns on Amorphous Computing Agents
A Preliminary Proposal

2. The Amorphous Computing Group
Many identical “agents”
Mass produced – Not reliable and no global clocks or beacons. “Throw at a problem.”
Connected in changing, unpredictable ways.
Goal: Coherent, robust behavior to achieve jobs

3. Motivation Researchers’ inspiration from living things
Examples of life from engineering standpoint:
Bees build a hive
Wolves hunt in packs
People build civilizations
Most impressive:
Cells, roughly identically programmed, create us

4. Why Now? Abundance of Microelectronic devices and MEMS
Cilia clean rooms
Smart paint
Sound cancellation

5. Why Now? Advances in biotechnology
We are able to “reprogram” genetic information of cells
Can grow quickly and cheaply
Don’t have concepts to allow us to engineer how life does
Yet good processors, mobile, sensing.

6. Research at MIT
Biology: Morphogenesis and self assembly as inspiration.
- Use insights to understand biological phenomena
Sensor Networks: Amorphous computers. More from a CS perspective.

7. Specific Project Area
Radhika Nagpal, Daniel Coore: Complex Pattern formation.
Fixed agents
Made of lines and endpoints.
Importance
Differentiating function
Determine distance and orientation

8. Healing Lines Lauren Clement – Self-healing lines
Agents in line die – reform line
Nagpal deals only with points and lines

9. Ex. Of How to Build a Line Fixed, randomly placed agents.
Build a line connecting two given points
Idea of gradient to tell distance
Constant local communciation:
- Random ID
- Successor ID
- Line?
- Gradient value

10. Ex. How to Build a Line Endpoint A initiates gradient
Each agent determines max gradient value, stores “successor ID”
Gradient reaches Endpoint B, line back propagates.
Successor
Grad = 41
Grad = 35

11. Self-Healing Method 1 Endpoint A never stops outputting gradient
Gradients and successor ID’s expire
Line redrawn over and over
Advantages: Can handle multiple gaps, good for large gaps
Disadvantages: Redraw whole line when gap

12. Self-Healing Method 2 Only send gradient value once and then forget
When break detected, successor listens, predecessor initiates gradient.
Advantages: Good for small holes
Disadvantages: Cannot handle multiple breaks with one gradient

13. My Project Extend Clement’s algorithms to deal with multiple lines.
Requires sending, receiving, storing multiple gradients
Way to choose method
- Gradient to tell size of gap.
Integrate into Nagpal’s compiler.

14. And if possible Enable Nagpal’s compiler to output to MICA Motes
4MHz Processor, wireless radio communication, sensors, batteries, TinyOS

15. Technical Risks Able to get number of motes (>= 50)
- $, production ability
Able to drive LED’s on motes.
Able to get Nagpal’s algorithm to program motes (too much work?)

16. Milestones
Find out detailed specifications for MICA Motes. March 2003.
Understand algorithms and existing code for Nagpal’s compiler and Clement’s self-healing methods. April 2003.
Full design with a design document. July 2003.
Obtain motes. August 2003.
Have first draft of code ready for testing. October 2003.
Have final version of code working: December 2003.
Have the motes tested running the programs produced by the compiler: February 2003.
Have thesis written and finished: April 2003.

17. Resources Required Motes, LED’s
Access to code and the people who wrote it
HLSIM simulator