Internet Scale Sensor Systems

1. 15-829B/18-849B/95-811A/19-729A
Internet-Scale Sensor Systems:
Design and Policy
Lecture 1 – Class Overview

2. Lecture 1 2
01-14-03
Outline
 Introduction to sensor systems
 Administrivia
 Whirlwind tour

3. Lecture 1 3
01-14-03
Objectives
 A multi-disciplinary introduction to the topic
area of sensor systems
 Databases, networking, security, policy
 Related topics
 Ad-hoc networking, XML databases, etc.
 What are sensor systems?

4. Lecture 1 4
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Sensor System Types – Smart-Dust/Motes
 First introduced in late 90’s by groups at
UCB/UCLA/USC
 Published at Mobicom/SOSP conferences
 Small, resource limited devices
 CPU, disk, power, bandwidth, etc.
 Simple scalar sensors – temperature, motion
 Single domain of deployment (e.g. farm, battlefield,
etc.) for a targeted task (find the tanks)
 Ad-hoc wireless network

5. Lecture 1 5
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Sensor System Types – Smart-Dust/Motes
 Hardware
 UCB motes
 Programming
 TinyOS
 Query processing
 TinyDB
 Directed diffusion
 Geographic hash tables
 Power management
 MAC protocols
 Adaptive topologies

6. Lecture 1 6
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Berkeley Motes
 Devices that incorporate
communications, processing,
sensors, and batteries into a
small package
 Atmel microcontroller with
sensors and a communication
unit
 RF transceiver, laser module, or
a corner cube reflector
 Temperature, light, humidity,
pressure, 3 axis
magnetometers, 3 axis
accelerometers

7. Lecture 1 7
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Berkeley Motes (Levis & Culler, ASPLOS 02)

8. Lecture 1 8
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Sensor System Types – Brilliant Rocks
 Largely unexplored
 Larger PC/PDA class devices
 Multimedia sensors
 Sensors shared by many applications
 Parking space finder
 Person locator
 Bus locator
 Directly connected to Internet

9. Lecture 1 9
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Example Application: Parking Space Finder
 A distributed database
maintains
 Spot availability data
 Address of parking spot
 Meter description
 Historical availability data
 Query: Where is the
cheapest empty parking spot
near Wean Hall?
 Returns list of spaces,
details on their meters

10. Lecture 1 10
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IrisNet
 Local CMU/Intel Research Pittsburgh project
 Webcams & other smart sensors are everywhere,
collecting vast amounts of data
 But, no effective tools for querying this data and extracting
useful information
 Goal: Build a scalable infrastructure with
 Efficient query processing
 Filtering raw video feeds
 Dynamic reconfigurability
 Ease of service creation

11. Lecture 1 11
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IrisNet Architecture
• Organizing Agents (OA):
• PC-class or server-class
processor, GBs of storage,
Linux
• Sensing Agents (SA):
• PDA/PC-class processor,
MBs–GBs storage, Linux
User
Internet
Sensing Agent Sensing Agent
Sensing Agent
Organizing Agents

12. Lecture 1 12
01-14-03
Outline
 Introduction to sensor systems
 Administrivia
 Whirlwind tour

13. Lecture 1 13
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Who’s Who?
 Dave Farber (Public Policy)
 http://www.cis.upenn.edu/~farber
 dave@farber.net
 Phil Gibbons (Databases)
 http://info.pittsburgh.intel-research.net/People/gibbons
 phillip.b.gibbons@intel.com
 Brad Karp (Networking/Distributed Systems)
 http://www.cs.cmu.edu/~bkarp
 bkarp@cs.cmu.edu

14. Lecture 1 14
01-14-03
Who’s Who?
 Adrian Perrig (Security)
 http://www.ece.cmu.edu/~adrian
 perrig@cmu.edu
 Srinivasan Seshan (Networking/Distributed
Systems)
 http://www.cs.cmu.edu/~srini
 srini@cmu.edu
 TA: Suman Nath
 http://www.cs.cmu.edu/~sknath
 sknath@cs.cmu.edu

15. Lecture 1 15
01-14-03
Class Web Page:
http://www.cs.cmu.edu/~srini/15-829A
 Check regularly  announcements!!
 Course schedule
 Reading list
 Lecture notes
 Assignments
 Project ideas
 Exams
 Student list

16. Lecture 1 16
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Office Hours
 Will be posted on web page
 Current office hours
 Srini: Tuesday 12:30 – 1:30
 Suman: ?
 Brad: Wednesday 10:00 – 11:00
 Dave: ? (out till Jan 23rd)
 Phil: ?
 Adrian: Tuesday 3:00 – 4:00

17. Lecture 1 17
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Grading
 Homework assignments
 Mini-project (15%)
 Hand-ins for readings (10%)
 Class participation (10%)
 Project (40%)
 Final exam (25%)

18. Lecture 1 18
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Course Materials
 Lack of a good reference text
 Too recent a topic
 Research papers
 Links to ps or pdf on Web page
 ~40 papers total (1-2 papers per lecture)
 Optional readings

19. Lecture 1 19
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Assumed Background
 Undergrad familiarity with databases,
networking, security topic areas
 Should have familiarity in at least 2 of the 3
 E.g., relational database, routing, transport,
public/private key crypto, etc.
 For missing background, we will
 Provide pointers to catch up on these topics
 Keep undergrad texts on reserve in library
 Cover background quickly in lectures

20. Lecture 1 20
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Projects
 3-person groups
 IrisNet-based
 IrisNet – locally developed research prototype
 An infrastructure for developing wide-area sensor services
 Mini-project provides intro to IrisNet
 Expectations
 10 page report + presentation
 Workshop quality results
 Project ideas
 Will be posted on Web page and discussed in lecture #3

21. Lecture 1 21
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Example Projects – New Applications
 Person tracker – where is Srini?
 Image processing
 How do you identify Srini?
 Can you simplify the problem by using tracking?
 Query processing
 What type of queries are possible – where is Srini
now?, where has Srini been?, where is he usually?
 What type of data schema – are people organized by
location, name, organization?
 Privacy issues
 Who can access this information?
 How can user and infrastructure control/check
privacy?

22. Lecture 1 22
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Example Projects – Infrastructure
Improvements
 Incorporating new sensor types in IrisNet
infrastructure
 Sensor motes, mobile sensors
 Applying image processing techniques to anonymize
video camera feed
 Using replication techniques to make IrisNet
infrastructure robust to failures
 Interesting deadlines
 Sensys – Apr 8th
 Mobicom workshops – June middle

23. Lecture 1 23
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Projects (cont.)
 Generous donation from Intel
 20 laptops + cameras
 10 desktops
 Each project group will receive 2 laptops and
3 cameras
 Handed out during project proposal
 No replacements – take good care of them
 No grade until you return them in good condition!
 Make project groups by 1/28

24. Lecture 1 24
01-14-03
Outline
 Introduction to sensor systems
 Administrivia
 Whirlwind tour

25. Lecture 1 25
01-14-03
Topic Areas
 Networking/Distributed Systems
 Databases
 Security
 Privacy & Policy

26. Lecture 1 26
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Networking/Distributed Systems
 Sensor Motes
 UCB Mote platform
 Directed diffusion, Geographic Hash Tables (GHT)
 TinyOS
 Wide area naming
 DNS
 INS
 Service discovery
 Distributed Hash Tables (DHTs)
 Chord, CAN, Pastry, Tapestry, etc.
 Databases: Pier
 Applications: CFS, Past

27. Lecture 1 27
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Networking/Distributed Systems
 Adhoc networks/related sensor work
 Adhoc routing (DSR, GPSR)
 Leach/SPIN
 Adhoc network capacity
 Synchronization/localization
 Ad-hoc network clock synchronization
 GPS
 Indoor positioning (Radar, BAT, Cricket)
 Resource allocation/policing
 Class-based scheduling
 Resource protection

28. Lecture 1 28
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Networking/Distributed Systems
 Geographic/network topology discovery
 Global Network Positioning (GNP)
 IDMAPS
 Network Weather Service
 IP2Geo
 Robustness
 Clock synchronization, causality for logging
 Logging/monitoring techniques
 Replication/consistency
 Transactional properties

29. Lecture 1 29
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Databases
 XML introduction
 Data representation/schema
 Query languages (XPath, XQuery, XSLT)
 Distributed query processing in IrisNet
 Sensor network databases
 Streaming queries (Telegraph)
 Tiny DB
 Time-series data & data mining

30. Lecture 1 30
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Databases
 Data models
 Hierarchal
 Relational databases
 Native XML databases
 Advanced topics
 Aggregation
 Consistency
 Queries on images
 Schema changes

31. Lecture 1 31
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Security
 Security intro
 What is security?
 Security properties - authentication / confidentiality /
anonymity
 Attacker models & vulnerability classification
 E.g., Denial-of-service, Identity attacks (Sybil)
 Security vulnerabilities of wireless communication
 Basic cryptographic mechanisms
 Public-key cryptography
 Symmetric-key cryptographic primitives (Hash, MAC, block
cipher)
 One-way chains
 Merkle hash trees (data verification)

32. Lecture 1 32
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Security
 Key distribution, trust bootstrapping
 Duckling (imprinting)
 Public key infrastructure (PKI)
 Small-group key agreement
 Large-group key distribution
 Key infection, secrecy amplification
 Random-key predistribution

33. Lecture 1 33
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Security
 Advanced cryptographic primitives
 Secret sharing
 Group signatures
 Broadcast authentication
 Secure logging
 Code safety
 Sandboxing, VM
 Software fault isolation
 Java security
 Proof carrying code

34. Lecture 1 34
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Privacy & Policy
 Policy introduction
 Mechanisms to encourage proper behavior
 E.g. cameras won’t spy on people
 Rewards & enforcement
 Privacy
 Laws on how private information can be distributed
 Describing privacy requirements (P3P)
 Other countries have privacy laws (info about citizens)
 E.g. European laws about personal info race, religion
 Laws/issues regarding crypto export/import/usage
 Access requirements for law enforcement
 Mechanisms to discover information (traffic analysis, etc.)

35. Lecture 1 35
01-14-03
Privacy & Policy
 Control how data can be used/accessed
 Digital rights management (DRM)
 Trusted systems
 Regulatory requirement (DMCA)
 Palladium
 Resource allocation/policing
 Admission control
 Fair allocation among competing applications
 Enforcement

36. Lecture 1 36
01-14-03
Next Lecture
 IrisNet system overview
 IrisNet demo
 Assigned reading:
 [N+03] IrisNet: An Architecture for Compute-
Intensive Wide-Area Sensor Network Services