A Method of Forming Self-Healing Patterns on Amorphous Computing

Agents

A Preliminary Proposal

The Amorphous Computing Group

Amorphous Computing 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

Motivation

Researchers’ inspiration from living thingsExamples of life from engineering standpoint: Bees build a hive Wolves hunt in packs People build civilizationsMost impressive: Cells, roughly identically programmed, create

us

Why Now?

Abundance of Microelectronic devices and MEMS

Cilia clean rooms Smart paint Sound cancellation

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.

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.

Specific Project Area

Radhika Nagpal, Daniel Coore: Complex Pattern formation.

Fixed agents Made of lines and endpoints.

Importance Differentiating function Determine distance and orientation

Healing Lines

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

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

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 = 35

Grad = 41

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

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

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.

And if possible

Enable Nagpal’s compiler to output to MICA Motes 4MHz Processor, wireless radio communication,

sensors, batteries, TinyOS

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?)

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.

Resources Required

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