PySense aims at bringing wireless sensor (and "internet of things") macroprogramming to the audience of Python programmers. WSN macroprogramming is an emerging approach where the network is seen as a whole and the programmer focuses only on the application logic. The PySense runtime environment partitions the code and transmits code snippets to the right nodes finding a balance between energy consumption and computing performances.

1. PySense
Python Decorators for Wireless Sensor Macroprogramming
Davide Carboni,
ICSOFT 2010, Athens 22-24 April
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2. Who, where...
geoweb.crs4.it
twitter.com/__dadaista__
locative sensor web ubicomp web of things mobile
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3. wireless sensor
networking today
communication
communication
computation
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4. wireless sensor
computing
computation--
communication--
communication--
computation++
computation++
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5. 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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6. 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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7. 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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8. 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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9. 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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10. 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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11. Motivation
• Python:
– easy to use,
– easy to learn,
– rapid prototyping
And because we
like to go beyond
the limits
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12. What is a python
decorator
Syntactic sugar
@foo
def f(x):<implementation>
Equivalent to call
foo(f)
where foo returns a function
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13. What is a python
decorator
def foo(func):
def my_f(*args):
do_something_pre()
func(*args)
do_something_post()
return my_f
@foo
def f(x):
return x*x
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14. Elements of PySense
• Region
• Group
• @mote
• @onboard
• @onbase
• @auto
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15. PySense Regions
Region(“/(0,0,100,100)”) | Region(“/foo/3/312”)
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16. @mote
@mote
class M:
def getX(self):pass
def setY(self,y):pass
Finds a mote m with X,Y
m=M()
m.getX() Read X from m, translated
in a network message
from the invoker to m
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17. @mote Translated into
class M: a network message:
def getX(self):pass TO <addr> CALL f x,y,...
@onboard
def f(self,args):<code to move on node>
@onbase
def g(self,args):<code to run on base>
@auto
def h(self,args):<some code>
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18. Simple program
r=Region(“/somewhere”) #somewhere
@mote
class CO2Sense:
def getConc(self): A lot of long range messages
pass exchanged between base
and remotes
values=
[c.getConc() for c in r.items(CO2Sense)]
avg = sum(values)/len(values)
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19. A little better
@mote
class CO2Sense:
def getConc(self):pass The avg () will be run
In one remote elected
class CO2Group(Group): head of group
@onboard
def avg(self):
return sum([m.getConc() for m in self.motes])
/ len(self.motes)
Short range
messages
avg=CO2Group(r.items(CO2Sense)).avg()
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20. 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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21. A lot to do
• Approach #1 Hey … this is a position paper
– running a VM and interpreter on the
mote
– Memory demanding
• Approach #2
– Compiling @onboard python on native
code
– Need OTA reconf
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22. VM on board
MBED board
64KB RAM
Clock 100MHz
USB, ETH, ...
Running python
and HTTP
is too demanding
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23. Questions & Answers
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