Maté: A Tiny Virtual Machine for Sensor

NetworksPhilip Levis and David Culler

Presented by: Michele Romano

Outline

• Sensor Networks• Virtual Machines• Maté Details• Evaluation• Conclusion

Sensor Networks

• Composed of 1000’s of tiny devices (Motes) with limited resources

Berkeley Mote Specifications

TinyOS

• OS designed for sensor networks• Split-phase non-blocking execution• Not suited well to non-expert

programmers

Reprogramming Motes

Reprogramming is desirable as:• Environmental conditions change• Analysis techniques evolve

Examples: • Great Duck Island• Building instrumentation

Reprogramming Motes

To change the behaviour of a TinyOS program, either:

1. Hardcode a state transitionOR2. Modify source code, recompile a

TinyOS image and place image on mote

Sensor Networks Challenges• Energy

– Recharging is difficult or impossible– Deterministic network lifetime desirable

• Communication– Lossy wireless networks– Bandwidth conservation

• Programming– Motes unreachable in deployed networks– Difficult for a non-programmer to program

TinyOS

System Requirements

• Small• Expressive• Concise• Resilient• Efficient• Tailorable• Simple

Virtual Machine

• Easier programming• Short VM programs• A VM can provide a safe program

execution environment

Maté VM Overview

• Bytecode interpreter that runs on TinyOS

• Single TinyOS component that sits on top of several system components

• Code fits in capsules of 24 instructions• Built-in ad-hoc routing algorithm AND

mechanisms for writing new ones

Code Capsules

• There are four types of capsules– Message send capsules– Message receive capsules– Timer capsules– Subroutine capsules

Maté Architecture

Instruction Set

There are three classes of Maté instructions:

8 instructions reserved for users to define

basic 00iiiiii i = instruction

s-class 01iiixxx i = instruction, x=argument

x-class 1ixxxxxx i = instruction, x=argument

Code Execution

• Execution of code begins in response to an event

• These three contexts can run concurrently

• Each instruction is executed as a TinyOS task

Code Security

• Bound checks prevent overrun and underrun

• Heap addressing is not a problem because there is only a single shared variable

• Unrecognized instructions result in no-ops

Code Infection

Reprogramming is easy:• Each capsule contains a type and

version number• When a capsule with a more recent

version is received, it is installed• forw or forwo is used to broadcast

the capsule for network neighbours to install

Maté Evaluation

Ad-hoc routing algorithm was implemented to measure:

1. Rate of instruction2. CPU overhead3. Network infection rates

1. Rate of InstructionTest

Maté Bytecode vs. Native Code

Operation Maté Clock Cycles

Native Clock Cycles

Cost

Simple: and 469 14 33.5:1

Downcall: rand 435 45 9.5:1

Quick Split: sense

1342 396 3.4:1

Long Split: sendr

685+~20000 ~20000 1.03:1

2. CPU Overhead

• Given the energy cost of an execution and the energy cost of installation:– Mate is preferable for a small number

of executions– For large number of executions,

Native code is preferable

3. Network Infection

Percentage of Motes Running New Program Over Time

Case Study

Great Duck Island Application• Spends most of its time in deep

sleep mode – draws 50 μA• Reads several sensors and sends a

packet• Maté proves to save energy if only

run for 5 days or less

Conclusion

• Maté met all of the defined requirements

• Maté can conserve energy in domains of frequent reprogramming

• VM can provide user-land guarantees

References

• http://www.cs.berkeley.edu/~pal/research/mate.html

• http://www.cs.berkeley.edu/~pal/pubs/brown-7-02.pdf

• http://www.cs.virginia.edu/~qc9b/fall03cs851/mate_damon_jo.ppt