1. Wireless Sensor Networks

2. 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

3. 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

4. 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

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

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

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

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

9. 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

10. 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!

11. 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

12. 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

13. 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)

14. 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

15. 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

16. 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

17. 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 no 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

18. 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

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

20. Puzzle Three bulbs inside a room Three switches outside the room
Room initially locked
You can initially play with the switches
You then need to enter the room and be able to match switches to their respective bulbs
Devise a strategy