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Maté: A Tiny Virtual Machine for Sensor Networks Philip Levis and David Culler Presented by: Michele Romano
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Outline Sensor Networks Virtual Machines Maté Details Evaluation Conclusion
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Sensor Networks Composed of 1000’s of tiny devices (Motes) with limited resources
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Berkeley Mote Specifications
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TinyOS OS designed for sensor networks Split-phase non-blocking execution Not suited well to non-expert programmers
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Reprogramming Motes Reprogramming is desirable as: Environmental conditions change Analysis techniques evolve Examples: Great Duck Island Building instrumentation
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Reprogramming Motes To change the behaviour of a TinyOS program, either: 1. Hardcode a state transition OR 2. Modify source code, recompile a TinyOS image and place image on mote
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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
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System Requirements Small Expressive Concise Resilient Efficient Tailorable Simple
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Virtual Machine Easier programming Short VM programs A VM can provide a safe program execution environment
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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
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Code Capsules There are four types of capsules –Message send capsules –Message receive capsules –Timer capsules –Subroutine capsules
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Maté Architecture
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Instruction Set There are three classes of Maté instructions: 8 instructions reserved for users to define basic00iiiiiii = instruction s-class01iiixxxi = instruction, x=argument x-class1ixxxxxxi = instruction, x=argument
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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
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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
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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
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Maté Evaluation Ad-hoc routing algorithm was implemented to measure: 1.Rate of instruction 2.CPU overhead 3.Network infection rates
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1. Rate of InstructionTest Maté Bytecode vs. Native Code OperationMaté Clock CyclesNative Clock CyclesCost Simple: and4691433.5:1 Downcall: rand435459.5:1 Quick Split: sense13423963.4:1 Long Split: sendr685+~20000~200001.03:1
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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
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3. Network Infection Percentage of Motes Running New Program Over Time
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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
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Conclusion Maté met all of the defined requirements Maté can conserve energy in domains of frequent reprogramming VM can provide user-land guarantees
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References http://www.cs.berkeley.edu/~pal/resea rch/mate.htmlhttp://www.cs.berkeley.edu/~pal/resea rch/mate.html http://www.cs.berkeley.edu/~pal/pubs/ brown-7-02.pdfhttp://www.cs.berkeley.edu/~pal/pubs/ brown-7-02.pdf http://www.cs.virginia.edu/~qc9b/fall03 cs851/mate_damon_jo.ppthttp://www.cs.virginia.edu/~qc9b/fall03 cs851/mate_damon_jo.ppt
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