1. Wireless Sensor Networks

2. Puzzle You are blindfolded There is a square table in front of you
Four bottles places – one at each corner
Bottles can either be in UP or DOWN orientations
You can “feel” any two of the bottles at a time, switch their orientation however you want to – you win if all bottles are oriented alike
The table will be rotated arbitrary number of ¼ turns after each of your moves
Can you guarantee that you will win?

3. Puzzle 5 moves (minimum required to guarantee)
We will denote positions of bottles as "U", "D" or "?", for "up",
"down" or "unknown", respectively. We list the four positions
clockwise, up to unknown rotation. Initially they are "? ? ? ?". First move: Take two opposite bottles and turn them "up". Now their setup is "? U ? U". Second move: Take two adjacent bottles and turn them "up". Now the setup is "? U U U". Assuming the referee does not stop the game, we know in fact that the setup is "D U U U". Third move: Take two opposite bottles. If one is "down", turn it "up", so that we win ("U U U U"). Otherwise they are both "up"; turn one "down", so that we are left with "D D U U". Fourth move: Take two adjacent bottles and turn them over. If they were both "up", we win with "D D D D". If they were both "down", we win with "U U U U". Otherwise our setup becomes "D U D U". Fifth move: Take two opposite bottles and turn them over, winning.

4. Project Report Due … on April 27th, by 11.59pm EST
Send electronic copy only to both TA Loren and me
please pick up PA2 from TA
PA3 will be given back on Monday
Preliminary report can be picked up from my office Friday 12noon-2pm, or after class on Monday

5. Wireless Sensor Networks
Used for sensing!
Large number of sensor nodes densely deployed either inside the phenomenon of interest or close to it
Random deployment feasible due to low cost nature of sensors
Sensor: sensing, processing, communication

6. WSN Organization Sensors sense, process and give information to sink
Backbone
Sensing Field
User/
Decision maker
Sink
Sensors sense, process and give information to sink
Sink propagates information back to the user/decision maker

7. WSN Applications
Sensors – temperature, humidity, motion, light, pressure, soil make-up, noise levels, stress, etc.
Military – surveillance, targeting, damage assessment, chemical/biological agent detection
Environment – forest fires, biocomplexity mapping, flood detection, precision agriculture
Health – telemonitoring, tracking, drug administration
Home – automation, smart environment

8. WSN vs Ad-hoc Networks Larger number of nodes Density of nodes
Failure proneness
Communication patterns
Limited Node capabilities
Lack of Global Identifiers

9. Key Factors in WSNs Fault tolerance (survivability) Scalability
Production costs
Hardware constraints
Topology management

10. WSN Protocol Stack Layers Planes Application Transport Network
Data link
Physical
Planes
Power management
Mobility management
Task management

11. Application Layer Time synchronization
Controlling ON/OFF decisions of sensors
Querying & controlling sensor network configuration
Security
Task assignment
Data collection

12. Transport Layer
Conventional transport layer protocols (TCP, UDP, NORM, SRM, etc.) cannot be uses in a sensor network
Point-to-point vs point-to-multipoint (downstream) & multipoint-to-point (upstream)
Upstream: Information reliability vs data reliability
Downstream: Dimensions of reliability

13. Dimensions of Reliability
All sensors in the network
Sensors in a sub-region (attributes based definition)
Contiguous
Non-contiguous
Sensors to cover sensing field (tackle redundancy)
Partial reliability

14. Network Layer Data centric routing and flooding
How many pedestrians do you observe in region X?
Let me know if the temperature in your local neighborhood is greater than 100F
Potential data processing on paths!

15. Directed Diffusion
A node requests data by sending interests for named data
The request “How many pedestrians do you observe in region X” is broadcasted to region X
Data matching the interest is then “drawn” down towards the node
When a node in region X receives the request, it activates its sensors, and returns sensed information along reverse path of interest propagation
Intermediate nodes can cache, or transform data
Combine reports from multiple sensors to more accurately pinpoint pedestrian’s location

16. Elements of Directed Diffusion
Interests
Query of what the user wants
Data messages
Collected or processed information of a physical phenomenon
Gradients
Direction state created in each node that receives the interest
Reinforcements
Of one or a small number of the available paths
Example

17. Naming Attribute-value pairs Example: Vehicle detection task (query)
(Type=wheeled, interval=20ms, duration=10seconds, rect=[-100,100,200,400])
VDT (response)
(type=wheeled,instance=truck,location=[125,220],intensity=0.6,confidence=0.85,timestamp=01:20:40)

18. Interests Can be initiated by the sink
Exploratory interest with a large interval, followed by reinforcements
e.g. to detect any wheeled vehicles
Soft-state refreshing of interests
reliability & overheads
Each node maintains one entry per interest in an interest-cache

19. Interests (contd.)
Each interest entry contains a gradient (neighbor, report rate, and lifetime)
Interest entry possibly created upon receipt of interest
Interest possibly forwarded to a sub-set of neighbors
e.g. based on cached data

20. Gradient Establishment
A generic notion
Can be implemented in several ways: binary values, probabilistic forwarding, load balancing
Gradients might be set-up differently for different tasks

21. Data Propagation Nodes in “rect” sense data
Propagates data according to the gradients to the corresponding interest entry
If an intermediate node receives data, but finds on interest entry, it drops the data
Gradients can change as data is being forwarded
e.g. down-sampling : 100 events/second to 50 events/second

22. Reinforcement Exploratory gradients vs. data gradients
Sink reinforces one (or a subset) of the neighbors reporting back exploratory events
Data gradients can have a higher reporting rate – positive reinforcement
Allows sink to reinforce selective paths and reduce multi-path routing for the real heavy data

23. Other Issues MAC Topology control (with sensing reliability)
Sensor placement
Reliable transport
Congestion control

24. Puzzle Singly linked list Need to find if the list has a loop
Constraint1: only read operations on the nodes of the linked-list
Constraint2: constant order memory usage