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15-829B/18-849B/95-811A/19-729A Internet-Scale Sensor Systems: Design and Policy

15-829B/18-849B/95-811A/19-729A Internet-Scale Sensor Systems: Design and Policy. Lecture 3 – Code Overview & Project Ideas. Outline. Code Overview Project Ideas. Code Availability. http://www.intel-iris.net Code accessible via CVS What is CVS  version control system

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15-829B/18-849B/95-811A/19-729A Internet-Scale Sensor Systems: Design and Policy

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  1. 15-829B/18-849B/95-811A/19-729AInternet-Scale Sensor Systems: Design and Policy Lecture 3 – Code Overview & Project Ideas

  2. Outline • Code Overview • Project Ideas Lecture 3

  3. Code Availability • http://www.intel-iris.net • Code accessible via CVS • What is CVS  version control system • Useful for group access to source code • Multiple people can edit the same file but conflicts may occur • Anonymous CVS  can’t check in code Lecture 3

  4. Mailing-lists • Class members • irisnet-course@intel-iris.net • Iriscode developers • irisdev@intel-iris.net • Managed by majordomo@intel-iris.net Lecture 3

  5. Using Laptop • Laptop setup • irisnet and root account  passwords = irisnet • Feel free to customize account but don’t change password • IrisNet source code checked out in ~irisnet/IRISNET • Redhat 8.0 • Boot with camera attached • Say configure at all the configuration prompts • Log in as irisnet • Start X server • Network configuration • System settings  network • Add…  wireless  lucent  SSID = CMU • Activate • Start browser with outside CMU URL  register wireless Lecture 3

  6. Building IrisNet • cd IRISNET • export CVS_RSH=ssh • cvs login • password = anonymous • cvs up -d -P • ./install • Should build without error • Try test suite • cd ~/IRISNET/TestSuite; ./testall.sh Lecture 3

  7. Components • SA • C-based • Base SA code • OpenCV • CamStream webcam viewer • OA • Java-based • Base OA code • Apache Xindice XML database • Logger • NAM network simulation animator • Applications Lecture 3

  8. SA Architecture Sensor Feed Senselet Senselet Shared Memory Network Senselet SA Daemon Lecture 3

  9. SA – Sensor Feed • Read from sensor and place info in shared memory • One process per sensor • Sensor readings are stored in circular buffer • Provides access to historical measurements as space permits Lecture 3

  10. SA – Starting the Sensor Feed • cd SA/IrisWebcam • ./runwebcam.sh to use real camera • ./loadImage to load one image • ./loadImage images/pX.jpg 75 • X = number of full spots = 0..6 • 75 = shared memory key for IrisNet • ./loadImages to cycle through files in images subdir • ./showImage shows the current image Lecture 3

  11. SA – CamStream • CamStream provides an interface to webcams and a bunch of useful tools • E.g., webcam capture/record • Needs gcc 2.95 (not installed on laptops) to compile completely • Gcc 3.2 only compiles the few libraries critical to IrisNet Lecture 3

  12. SA – Senselet • Senselet • One senselet per sensor service • Each senselet runs as a separate process • Senselet typically does: 1) take sensor data from shared memory 2) processes data to produce result 3) Transmits result to configured OA 4) Results may be stored back in shared memory Lecture 3

  13. SA – Running Senselet • ./SAControl 127.0.0.1 3456 “S 127.0.0.1 6789 parking” executes parking detection senselet • Need to first run “Util/Portsink/portsink 6789” to discard data • Can load new images to see changes in result • Can use similar technique to test your code for mini-project Lecture 3

  14. SA – Example Senselet • cd Applications/parking/SA • parking.cc contains parking space senselet • Main function called Start(FilterParameter param) • Typical structure of main function • Open shared memory • cvCreateImageHeader and cvSetImageData calls retrieve video image • openCV calls to process image data • Send data to the OA Lecture 3

  15. SA – SA Daemon • Manages the execution of senselets • Accepts commands from OAs to start/stop execution • Text-based command protocol (port #3456) • L <OA_IP> <OA_PORT> <senselet filename> <senselet name> <senselet code> • uploads the code for a senselet and places it in filesystem • S <OA_IP> <OA_PORT> <senselet name> • starts execution of a senselet and transmits output to specified OA • U <OA_IP> <OA_PORT> <senselet name> • stop execution of a senselet Lecture 3

  16. SA – SA Daemon • Starting • cd SA/IrisSA/src • ./runsa • Controlling • Normally controlled by OA’s • cd Util/SAControl • ./SAControl to manually send commands Lecture 3

  17. OA - Architecture Xindice OA Daemon Network Lecture 3

  18. OA – Xindice • What does it do? • Local XML database engine • Stores XML documents, handles Xpath 1.0 (mostly select and update) • Interfaces • Command line  enter Xpath • Java API • What doesn’t it do [well] • Xquery, XSLT • XSLT processing  done in separate Java library • Image storage • How do we use it • Each service has a single XML document that stores sensor readings and static metadata Lecture 3

  19. OA – Starting Xindice • cd packages/xml-xindice-1.0 • ./xindice.server start • Make sure that config/xindice.pid does not exist • export XINDICE_HOME=`pwd` • cd packages/xml-xindice-1.0/bin • Run ./xinidiceadmin to control database Lecture 3

  20. OA – OA Daemon • Listens on port 6789 • Handles processing/forwarding of queries • Uses XSLT to query Xindice database • Manages partitioning of database across nodes • DNS entries • Reaction to load Lecture 3

  21. OA – OA Daemon Messages • Query <srcPort> <id> <fragment #> <root> <Xpath query> • Process the XPath query, id identifies source query made at root, fragment identifies this particular subquery • Reply <id> <fragment #> <replying OA> <response> • Answer to a query • Take ownership <schema> • Start a new OA • Delegate ownership <targetIP> <targetPort> <Xpath query> • Split an OA • Delete database <database name> • Load data from SA <num xupdates> <xupdates> • Used by SAs to add to database • Update DNS <database name> • Use dynamic DNS Lecture 3

  22. OA – OA Daemon • Running • cd OA/IrisOA • Edit oa.cfg if needed • make run • Controlling • make runcl • Type help to see interface • Running a Service • cd Applications/parking/OA • Look at demodataone.xml • Follow handout instructions to load xml document Lecture 3

  23. Logging • What are the OAs/SAs doing? • Log all messages  currently uses a central server • Challenge: how to order and playback log messages • Lamport clocks to ensure causal order • ns-2 network animator (nam) used to playback log • What is nam? Lecture 3

  24. Logging • Running the logger • cd logger/IrisLogger/src • runnam.sh • Running NAM • cd logger/nam-1.0a11a_iris • ./nam ../IrisLogger/src/logfile.6 • Currently logging must be carefully tuned to the application  manual steps involved: • Name and layout of nodes in animation • Conversion of logfile node names to nam nodes identifiers Lecture 3

  25. Outline • Code Overview • Project Ideas Lecture 3

  26. Projects • 2 or 3-person groups • IrisNet-based • Mini-project provides intro to IrisNet • Expectations • 10 page report + presentation • Workshop quality results • Project ideas • Will be posted on Web pages Lecture 3

  27. Projects (cont.) • Generous donation from Intel • 20 laptops + cameras • 10 desktops • Each project group will receive 2 laptops and 3 cameras • Handed out TODAY in CMCL lab 3604 Wean Hall • Only to enrolled students, other students will need to wait • No replacements – take good care of them • No grade until you return them in good condition! • Interesting deadlines • Sensys – Apr 8th • Mobicom workshops – June middle Lecture 3

  28. Applications – Monitoring Interesting Places • At lunch places • At grad lounge foosball table • NSH Atrium • Find me a free conference rooms • Tennis/raquetball/etc. court avail • Desk area/terminal room availability • Car theft monitor • Call/email owner if thief is near car! • Traffic monitoring • Auto traffic (traffic jams) • Throughput of area (cars, people) - speed trap Lecture 3

  29. Applications – Object Tracking • Person tracking • Where did my child/pet go • Advisor/advisee avoidance • Location of my/someone else's car • Inventory control Lecture 3

  30. Other Service Types • Audio sensor-based applications • Identify language spoken • Identify individual based on voice • Triggered sensor applications • Record picture when car horn is heard • Network monitoring as an IrisNet application • Intrusion detection system Lecture 3

  31. Other Service Types – Environment Modeling • Smart room support • 3d positioning based on multiple sensors • Virtual reality tour • Take the sensor input to create parts of a virtual reality • Weather monitoring Lecture 3

  32. Applications – General Challenges • Image processing • How do you identify objects of interest? • Person/object counting techniques • Motion detection based • Identifying the same object in different views • Handoff of tracking data from one sensor to next Lecture 3

  33. Applications – General Challenges • Privacy issues • Access control to OA database • How does a person opt out of a surveillance • Query processing • What type of data schema for sensor readings? • How are historic queries handled • Data mining • Long term trends – e.g., in parking space availability or waiting lines Lecture 3

  34. Infrastructure • Incorporating new sensor types in IrisNet infrastructure • Sensor motes – how do you modify the SA programming model to accommodate motes? • Others: mobile sensors, public webcams, smart camera phones • Making IrisNet infrastructure robust to failures • Replicating part of database • How to handle dynamic vs. static parts of database • Handling failures in routing of queries – e.g., due to caching? • Distributed monitoring of system and on-demand collection of logs Lecture 3

  35. Infrastructure – Energy Constraints • Power management for local processing, e.g., how accurately can we predict energy consumption? • What is the energy tradeoff between local processing of data vs. sending the data to do remote processing? • Running a CPU, a sensor device, or a communication device in sleep mode can save energy considerably. An approach for saving power is thus to operate our devices mostly in sleep mode, and have them turn on periodically. • How much accuracy does our sensor data loosegiven some duty cycle? • Can we find more intelligent scheduling algorithms than simply uniform duty cycles (on during 100ms each second)? • What about using low-energy sensors to schedule morepower-hungry sensors? Lecture 3

  36. Infrastructure • Location-based services • How could we incorporate location-specific information in our sensors? • How could we establish or verify the location of a sensor? • Using video landmarks • Image Processing • Can we anonymize raw sensor feed – e.g., by blurring people in image Lecture 3

  37. Infrastructure – Policy/Security • How can a user ensure that a video feed has not been “misused”? • E.g., monitor the output XML, perform test queries on system • Assuming an open environment (i.e., no central certificate authority everybody trusts), how can we establish trust between unrelated entities? • How do we ensure that sensor data we receive is trustworthy and originates from the correct sensor? • If anyone can make queries/program sensors, how do we protect the system from a denial-of-service attack? Lecture 3

  38. Infrastructure • DNS • Performance evaluation • DHTs as an alternative • Code safety and resource allocation on SAs/OAs • How are services protected from each other • How are nodes protected from malicious services that consume resources • Storage use on SAs • Support for historic sensor reading retrieval • Impact on result sharing Lecture 3

  39. Infrastructure • Push vs. Pull of data • Impact on monitoring of failed nodes • Impact on caching/response time for queries • Handling aggregate fields in OA database Lecture 3

  40. Next Lecture • Image processing • OpenCV library • Assigned reading: • TBA Lecture 3

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