pysense: wireless sensor computing in python?

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Davide Carboni,

ICSOFT 2010, Athens 22-24 April

PySensePython Decorators for Wireless Sensor Macroprogramming

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Who, where...

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geoweb.crs4.ittwitter.com/__dadaista__

locative sensor web ubicomp web of things mobile

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wireless sensor networking today

communication

communicationcomputation

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wireless sensor computing

communication--

communication--

computation++

computation++

computation--

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Decentralization

• In today cloud computing “computation costs less then storage” → “move the computation to data”

• In today wireless networks “computation costs less then communication” → move the computation to data (sensors)

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Decentralization

• Wireless Sensor Computing– not only simple sensors connected to

a central computer, but rather elements capable of computation in a distributed system

• Computation Vs Communication– One byte sent demands 100 times

the energy of an integer instruction

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Programming models

• Node-level programming– program for each node type (error

prone, difficult, only for experts)

• Network as DB– Good but limited to queries (TinyDB)

• Macroprogramming– Program the net as a whole, the

middleware partition the code on the nodes automatically

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Use case

• given an energy consumption model E and an application code C, there exists a partitioning of code C={c1,c2,...,cn} and a set Tx of transmissions Tx={tx1,tx2,...,txk} which is optimal for E

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PySense

• What is PySense– A programming model for Python– A middleware based on Python

decorators and API – It runs on:

• Base Runtime Environment (based on Python 2.6)

• Remote Runtime Environment (based on Python-on-a-chip)

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What is not PySense

• Not ready to use solution• Not bound to a specific network

topology (mesh, clustering, ...) or network protocol stack (zigbee,tcp,...)

• Not efficient in terms of memory used by nodes

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Motivation

• Python: – easy to use, – easy to learn, – rapid prototyping

And because we like to go beyond

the limits

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What is a python decorator

Syntactic sugar@foodef f(x):<implementation>

Equivalent to callfoo(f) where foo returns a function

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What is a python decorator

def foo(func):def my_f(*args):

do_something_pre()func(*args)do_something_post()

return my_f

@foodef f(x):

return x*x

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Elements of PySense

• Region• Group• @mote• @onboard• @onbase• @auto

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PySense Regions

Region(“/(0,0,100,100)”) | Region(“/foo/3/312”)

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@mote

@moteclass M: def getX(self):pass def setY(self,y):pass

m=M()m.getX()

Finds a mote m with X,Y

Read X from m, translated in a network message from the invoker to m

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@moteclass M:

def getX(self):pass

@onboarddef f(self,args):<code to move on node>

@onbasedef g(self,args):<code to run on base>

@autodef h(self,args):<some code>

Translated into a network message:

TO <addr> CALL f x,y,...

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Simple program

r=Region(“/somewhere”) #somewhere

@mote

class CO2Sense:

def getConc(self):

pass

values=

[c.getConc() for c in r.items(CO2Sense)]

avg = sum(values)/len(values)

A lot of long range messages exchanged between base

and remotes

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A little better

@mote

class CO2Sense:

def getConc(self):pass

class CO2Group(Group):

@onboard

def avg(self):

return sum([m.getConc() for m in self.motes]) / len(self.motes)

avg=CO2Group(r.items(CO2Sense)).avg()

The avg () will be runIn one remote elected

head of group

Short range messages

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Where we are

• API and decorators implemented and tested on PC processes running python-on-a-chip interpreter and pipes for I/O

• Todo:– @auto– Deployment on real boards … we

want a real test bed not simulation

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A lot to do

• Approach #1– running a VM and interpreter on the

mote– Memory demanding

• Approach #2– Compiling @onboard python on native

code – Need OTA reconf

Hey … this is a position paper

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VM on board

MBED board64KB RAM

Clock 100MHzUSB, ETH, ...

Running pythonand HTTP

is too demanding

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Questions & Answers