Pysense: wireless sensor computing in Python?
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The document discusses Pysense, a Python-based middleware designed for macroprogramming wireless sensor networks using Python decorators and APIs. It contrasts traditional node-level programming with a more decentralized approach, enabling efficient computation directly at sensor locations to reduce communication energy costs. Key features include the introduction of programming models, decorators for sensor management, and future developments aimed at deploying on real hardware.
![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)
18](https://image.slidesharecdn.com/pysense-icsoft-100726053418-phpapp02/85/Pysense-wireless-sensor-computing-in-Python-18-320.jpg)
![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()
19](https://image.slidesharecdn.com/pysense-icsoft-100726053418-phpapp02/85/Pysense-wireless-sensor-computing-in-Python-19-320.jpg)
Pysense: wireless sensor computing in Python?
- 1. PySense Python Decorators for Wireless Sensor Macroprogramming Davide Carboni, ICSOFT 2010, Athens 22-24 April 1
- 2. Who, where... geoweb.crs4.it twitter.com/__dadaista__ locative sensor web ubicomp web of things mobile 2 2
- 3. wireless sensor networking today communication communication computation 3
- 4. wireless sensor computing computation-- communication-- communication-- computation++ computation++ 4
- 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) 5
- 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 6
- 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 7
- 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 8
- 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) 9
- 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 10
- 11. Motivation • Python: – easy to use, – easy to learn, – rapid prototyping And because we like to go beyond the limits 11
- 12. What is a python decorator Syntactic sugar @foo def f(x):<implementation> Equivalent to call foo(f) where foo returns a function 12
- 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 13
- 14. Elements of PySense • Region • Group • @mote • @onboard • @onbase • @auto 14
- 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 16
- 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> 17
- 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) 18
- 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() 19
- 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 20
- 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 21
- 22. VM on board MBED board 64KB RAM Clock 100MHz USB, ETH, ... Running python and HTTP is too demanding 22
- 23. Questions & Answers 23