The world is the computer and the programmer is you - Presentation Transcript The World is the Computer and the programmer is You Davide Carboni Surfing the 3rd wave, Internet of Things in Sardinia Workshop Pula 14 04 2010 1 Time • 1956:There's Plenty of Room at the Bottom -->MEMS (Feynman) • 1974: TCP/IP (V.Cerf, R Kahn) • 1990: Web (T.Berners Lee) • 1993: Vision of Ubiquitous Computing (Weiser) • 1998: Jini (Sun) • 1999: Internet of things (AutoID labs) • 2000: REST (Roy Fielding) • 2001: Smart Dust (Pister) • 2003: Project JXTA-C: Enabling a Web of Things • 2004: Vision of Spime (B. Sterling) • 2004: Web 2.0 (O'Reilly or DiNucci in 1999) • 2008: (Restful) Web of Things Manifesto (Trifa et al.) 2 Models and tools • to understand Ubiquitous Computing we must make more systematic use of models [...] • how models can form a hierarchy, allowing them to be combined and higher models to explain lower ones – Robin Milner 3 Top down Vs bottom up • Bottom up – Few simple rules, many subjects, many objects – Integrative levels, emergent complexity,evolutionary approach • Top Down – Many complex rules and logic, one subject, many objects – Creational approach 4 Projects • Languages, tools, and models for Web of Things and Wireless Sensor Computing – Hyperpipe (bottom up) – PySense (top down) 5 Top Down 6 wireless sensor networking today 7 PySense • 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 8 Sensing, routing, computing 9 • 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 10 • Node-level programming – program for each node type (error prone, difficult, only for geeks) • Network as DB – Good but limited to queries (TinyDB) • Macroprogramming – Program the net as a whole, the tool partition the code on the nodes automatically 11 PySense • PySense – Language (hosted on decorators) and API – Base Runtime Environment (based on Python 2.6) – Remote Runtime Environment (based on Python-on-a-chip) 12 PySense Regions Region((0,0,100,100)) | Region(“/foo/3/312”) 13 @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 14 @mote class M: def getX(self):pass @onboard def f(self,args):<some> @onbase def g(self,args):<some> @auto def h(self,args):<some> 15 Simple program @mote class CO2Sense: def getConc(self): return self.x values=[c.getConc() for c in region.items(CO2Sense)] 16 A little better @mote class CO2Sense: def getConc(self):return self.conc class CO2Cluster(Cluster) @onboard def average(self): return sum([m.getConc() for m in self.motes]) / len(self.motes) values=CO2Cluster(region.items(CO2Sense)).average () 17 Bottom up Picture released under (CC) attribution share alike by alasis on Flickr 18 Only for geeks ls l | less curl "http://en.wikipedia.org/wiki/Pipeline_(Unix)" | sed 's/[^a zA Z ]/ /g' | tr 'A Z ' 'a­zn' | grep '[a z]' | sort u | comm 23 /usr/share/dict/words 19 Also for bloggers 20 Web 1.0 Pages linked Web 2.0 People, content, social Web 3.0 Structured data and services Web of Things Physical objects,sensors, effectors Wiki of Things, People bend the rules, Physical Mashups new unexpected apps 21 Hyperpipe • Hyperpipes =architecture for the Web of Things • Point-select-connect interaction • Based on pi-calculus / SOA 22 – Every thing is a process – Things have public operations (channels) – Things exchange data through channels – Hyper pipes connect objects' channels – Hyper pipes are processes 23 sensor src() >data effector sink(data) f(data) >data' pro

1. The World is the Computer
and the programmer is You
Davide Carboni
Surfing the 3rd wave, Internet of
Things in Sardinia Workshop
Pula 14 04 2010
1

2. Time
• 1956:There's Plenty of Room at the Bottom -->MEMS
(Feynman)
• 1974: TCP/IP (V.Cerf, R Kahn)
• 1990: Web (T.Berners Lee)
• 1993: Vision of Ubiquitous Computing (Weiser)
• 1998: Jini (Sun)
• 1999: Internet of things (AutoID labs)
• 2000: REST (Roy Fielding)
• 2001: Smart Dust (Pister)
• 2003: Project JXTA-C: Enabling a Web of Things
• 2004: Vision of Spime (B. Sterling)
• 2004: Web 2.0 (O'Reilly or DiNucci in 1999)
• 2008: (Restful) Web of Things Manifesto (Trifa et al.)
2

3. Models and tools
• to understand Ubiquitous
Computing we must make more
systematic use of models [...]
• how models can form a hierarchy,
allowing them to be combined and
higher models to explain lower
ones
– Robin Milner
3

4. Top down Vs bottom up
• Bottom up
– Few simple rules, many subjects,
many objects
– Integrative levels, emergent
complexity,evolutionary approach
• Top Down
– Many complex rules and logic, one
subject, many objects
– Creational approach
4

5. Projects
• Languages, tools, and models for
Web of Things and Wireless Sensor
Computing
– Hyperpipe (bottom up)
– PySense (top down)
5

6. Top Down
6

7. wireless sensor
networking today
7

8. PySense
• 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
8

9. Sensing,
routing,
computing
9

10. • 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
10

11. • Node-level programming
– program for each node type (error
prone, difficult, only for geeks)
• Network as DB
– Good but limited to queries (TinyDB)
• Macroprogramming
– Program the net as a whole, the tool
partition the code on the nodes
automatically
11

12. PySense
• PySense
– Language (hosted on decorators) and
API
– Base Runtime Environment (based on
Python 2.6)
– Remote Runtime Environment (based
on Python-on-a-chip)
12

13. PySense Regions
Region((0,0,100,100)) | Region(“/foo/3/312”)
13

14. @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
14

15. @mote
class M:
def getX(self):pass
@onboard
def f(self,args):<some code here>
@onbase
def g(self,args):<some code>
@auto
def h(self,args):<some code>
15

16. Simple program
@mote
class CO2Sense:
def getConc(self):
return self.x
values=[c.getConc() for c in
region.items(CO2Sense)]
16

17. A little better
@mote
class CO2Sense:
def getConc(self):return self.conc
class CO2Cluster(Cluster)
@onboard
def average(self):
return sum([m.getConc() for m in self.motes])
/ len(self.motes)
values=CO2Cluster(region.items(CO2Sense)).average
()
17

18. Bottom up
Picture released under (CC) attribution share­alike by alasis on Flickr 18

19. Only for geeks
ls ­l | less
curl
"http://en.wikipedia.org/wiki/Pipeline_(Unix)"
|
sed 's/[^a­zA­Z ]/ /g' |
tr 'A­Z ' 'a­zn' |
grep '[a­z]' |
sort ­u |
comm ­23 ­ /usr/share/dict/words
19

20. Also for bloggers
20

21. Web 1.0 Pages linked
Web 2.0 People, content, social
Web 3.0 Structured data and
services
Web of Things Physical
objects,sensors,
effectors
Wiki of Things, People bend the rules,
Physical Mashups new unexpected apps
21

22. Hyperpipe
• Hyperpipes =architecture for the
Web of Things
• Point-select-connect interaction
• Based on pi-calculus / SOA
22

23. – Every thing is a process
– Things have public operations
(channels)
– Things exchange data through
channels
– Hyper pipes connect objects'
channels
– Hyper pipes are processes
23

24. sensor
src()­>data
effector
sink(data)
f(data)­>data'
processor
24

25. src()­>data sink(data)
25

26. src()­>data sink(data')
f(data)­>data'
26

27. src()­>video sink(video)
27

28. src()­>video sink(video)
Hyper pipe
28

29. 29

30. 30

31. 31

32. 32

33. Selecting two different actions
from two different objects
(or even from the same object)
a pipe can be constructed.
Thanks to
Alessandro Giordano
and Alberto Serra
for the prototype
33

34. stream()­>video sink(video)
snapshot()­>image
faceRecognition()­>event
wsdl
34

35. 35

36. References
• Macroprogramming
http://fiji.eecs.harvard.edu/Macroprogramming
• Enowireless - http://opensource.crs4.it/enowireless/
• Wireless Wires: Let the Users Build the Ubiquitous
ComputerIn Proc. of MUM 2007 - 6th Int.
Conference on Mobile and Ubiquitous Multimedia.
ACM Press.
• http://en.wikipedia.org/wiki/Mark_Weiser
36