Networked Embedded Systems

Kamin WhitehouseCS 696 09/05/07

Technology Trends Sensors/actuators entering human habitats

32 million homes have security sensors 5 million homes have X10 devices Estimated 20 million ZigBee devices by EOY

We will need: To program them To debug them Privacy preservation Data stream processing

Macro-programming

for i = 1:100val = node[i].light.read();date = node[i].time();if val < 50

printf(“Error!”);else

hashtable.put(i,[val,date]);endpda.display(val);

end

RPC (marionette):

node

pda

node/server

pda/server

node

4

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val < 50

printf(“Error!”);else

hashtable.put(i,[val,date]);endpda.display(val);

end

node

pda

node/server

pda/server

node

Automatic Decomposition:

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val < 50

printf(“Error!”);else

hashtable.put(i,[val,date]);endpda.display(val);

end

node

pda

server

server

node

Automatic Decomposition:

6

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val < 50

printf(“Error!”);else

hashtable.put(i,[val,date]);endpda.display(val);

end

node

pda

server

server

node

QoS Satisfaction: L < 350:

7

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val > 50

printf(“Error!”);else

hashtable.put(i,[val,date]);endpda.display(val);

end

node

pda

server

server

node

QoS Satisfaction: L < 350:

8

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val > 50

hashtable.put(i,[val,date]); else

printf(“Error!”);endpda.display(val);

end

node

pda

server

server

node

QoS Satisfaction: L < 350:

9

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val > 50

hashtable.put(i,[val,date]); else

printf(“Error!”);endpda.display(val);

end

node

pda

pda

node

node

QoS Satisfaction: L < 350:

10

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val > 50

hashtable.put(i,[val,date]); else

printf(“Error!”);endpda.display(val);

end

node

pda

pda

node

node

QoS Satisfaction: L < 350:

Macroprogramming

for i = 1:100val = node[i].light.read();date = node[i].time();if val < 50

printf(“Error!”);else

hashtable.put(i,[val,date]);endpda.display(val);

end

node

pda

server

server

node

QoS Satisfaction: L < 350:

Outline check: Programming Debugging Privacy Data Processing

Debugging

Debugging

Program

MCU

Debugger

Add a “trap”

Debugging

Debugging Break Step Watch Backtrace Etc

30KB of program memory 1KB of RAM

Outline check: Programming Debugging Privacy Data Processing

Privacy Preservation

Home or away Awake or asleep Bathroom usage Kitchen usage Showering, toileting,

washing Cooking hot food or

preparing cold food

FATS Attack

Bath

room

Kit

ch

en

Liv

ing

Rm

FATS success across 4 homes

Preserving Privacy

),()|,(

),(

)()|,(),|(

)(),|(

FTPDFTP

FTP

DpDFTPFTDp

DPFTDP

Complete Privacy:

Bayes’ Rule:

Requirement For Privacy:

Counter Attacks Periodic Transmissions

Assumes tolerable latency bound L

Does not work with real-time or high bandwidth requirements

Consumes bandwidth Consumes power

Random Delays Exploit L with lower power

& bandwidth requirements Still assumes L

Counter Attacks Mask fingerprints in

hardware by varying features for each transmission Arms race scenario,

unable to predict features used by an adversary

Not supportable by current hardware

Does not affect inference of sleep and home occupancy variables

Counter Attacks Increasing Packet loss

ratio by: Reducing transmission

power Introducing RF

attenuators

Outline check: Programming Debugging Privacy Data Processing

Data Stream Processing

Can the user infer desired info without making strong assumptions?

Data labeling

Can we detect patterns without assumptions? Major time saver

Data Labeling

Results

Results

Data Sharing Personal sensors are prevalent

Homes Cars Phones Shoes, etc

Goal: create infrastructure for sharing data & creating value

Shopkeepers publish data #people in front of store #people coming into store Credit card purchase info,

etc Entrepreneurs provide

service Effect of weather, concerts,

etc on business Effect of advertising How to increase conversion

rates, etc Plus…

Overall activity downtown Value of commercial real

estate Effect of vehicular traffic on

businesses, etc.

Search Search is key to data sharing PageRank

StreamRank mines the WWSW and creates links between data streams Correlation Ownership Browsing, etc

Thanks

Kamin WhitehouseComputer Science Department

whitehouse@cs.virginia.edu