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# Wireless Sensor Networks Routing

Wireless Sensor Networks Routing. Professor Jack Stankovic University of Virginia. Sensor Net Routing. End-to-end Real-time Collisions Congestion Power Security Mobility Link Quality. Last Mile. Destination. Source. Base Station. Assumption: Nodes know location (localization).

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## Wireless Sensor Networks Routing

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1. Wireless Sensor NetworksRouting Professor Jack Stankovic University of Virginia

2. Sensor Net Routing • End-to-end • Real-time • Collisions • Congestion • Power • Security • Mobility • Link Quality Last Mile Destination Source Base Station Assumption: Nodes know location (localization)

3. Last Mile Semantics • At least 1 - Any • At most 1 • All • Unicast – exactly which node by ID

4. Ad Hoc DeploymentNeighbor Discovery Data Structure ID Location 1 x,y 2 a,b 3 c,d 1 3 2

5. Question • Suppose probability of a packet getting to next hop is 95% • What is the probability of a packet making it across 10 hops? (.95) ~= 60% 10

6. Most WSN • Multi-hop • Ad hoc deployment • Need “more interesting” routing protocols • Find routes on-demand • Energy issues • Irregular communication range • Interferences • Congestion • Highly dynamic link quality

7. Many Routing Algorithms • GF (SGF; GPSR) • DSR(supports mobility) (MANET) • AODV (supports mobility) (MANET) • Directed Diffusion • SPEED (RT) • IGF (supports mobility, stateless)

8. Other Routing Algorithms • Perimeter face routing • Trajectory based routing • Cluster head routing • Minimum spanning trees • GEAR • GF plus consider energy • Rumor Routing • RAP

9. GF always chooses a node that is closest to the destination. Every node knows its location. Geographic Forwarding (GF) s d

10. GF – Information Required • Node i (maintains routing table) • My location • List of neighbors and their locations • Destination location • Find neighbor closest to destination • How? D S

11. Sensor Network Routing • Current routing solutions • Many classical solutions need routing tables the size of the network • Most use single path to destination (DSR, AODV,…) • Many use path finding beacons (DD) - bad for real-time • SPEED • local (neighbor) tables only • utilize multiple paths • no path set-up beacons needed • Real-time addressed/control theory

12. SPEED Protocol (7 Aspects) • API (and last mile processing) • Neighbor Beacon Exchange • Delay Estimation Scheme • Neighborhood Feedback Loop (NFL) • Semi-Stateless Non-deterministic Geographic Forwarding (SNGF) • Back-pressure Rerouting • Void Avoidance

13. SPEED Architecture RT/Control

14. API (Last Mile Processing) • AreaMulticast • AreaAnyCast • Unicast Destination Possible INTERFERENCE Source

15. SPEED – Velocity Idea USE VELOCITY

16. ID Position RP Compute Speed ID Position Delay 2 (7,8) 100% 9 ( 1,6) 5 7 (3,4) 3 3 (4,7) 3 2 (7,8) 1 Nondeterministic Forwarding Example 1: RP: Relay probability Destination 7 9 2 s 3

17. Compute Speed ID Position Delay 9 ( 1,6) 2 7 (3,4) 6 3 (4,7) 1 2 (7,8) 3 ID Position RP 9 ( 1,6) 50% 2 (7,8) 50% Nondeterministic Forwarding Example 2: RP: Relay probability 7 9 Destination s 2 3

18. Compute Speed ID Position Delay 9 ( 1,6) 1 7 (3,4) 4 3 (4,7) 5 2 (7,8) 2 ID Position RP 7 (3,4) 45% 3 (4,7) 40% Drop 15% Nondeterministic Forwarding Example 3: Example: Overload situation Drop ratio is computed according to the Neighborhood feedback control loop

19. ID Position Delay 9 ( 1,6) 1 7 (3,4) 2 3 (4,7) 2 2 (7,8) 2 M Back-pressure re-routing • When all available forwarding nodes are congested, the sending node will drop packets, which will be perceived by previous nodes. Route changes. 3 Congestion Area 7 DROP 9 2

20. Neighborhood FC Periodically every node broadcast its delay to its neighbors. Receiving nodes update their neighborhood table.

21. System Model • Use system identification methodology from control theory • Create a model that relates delay and drop ratio • Use of a P controller

22. Void Avoidance • Uses congestion control scheme (no need for a separate technique) Void

23. Evaluation 8-9 hops • 6 CBR flows on one side of terrain send to one base station on the other side of terrain • Average number of hops (8-9) • 90% CI (within 2-10% of mean) • Miss ratio results – not shown here but much better for SPEED • Under heavy congestion • added flows in center of terrain • Transient performance

24. Control Theory • Transient metrics (to address uncertainty) • Settling time • Overshoot • Stability • Identify requirements and design controller to meet them!

25. Control Theory - Performance Specifications Controlled variable Overshoot Steady state error % Reference Transient State Steady State Time Settling time

26. Evaluation (Added Congestion) E2E Delay Energy Consumption

27. Performance Figure A. E2E delay profile of DSR Figure B. E2E delay profile of AODV

28. Performance Figure. D E2E delay profile of SPEED Figure C. E2E delay profile of GF

29. ID Position Delay 9 ( 1,6) 2 7 (3,4) 6 3 (4,7) 1 2 (7,8) 3 Extensions: Physical Properties • Add power (P) decision, i.e., choose next hop based on “most power” remaining • Add reliable link decision, i.e., compute link quality(LQ) and use it for choosing next hop LQ P 1.7 3.0 2.5 3.1

30. Summary – WSN Routing • Routing with global tables not appropriate • Geographic (and ID) Based • Sleeping nodes • Real-time • Voids • Special Communication Patterns • Dynamic Link Qualities • Minimize control overhead • Uncertainty high – use of control theory

31. Summary – Physical Properties • Obstacles • Interferences • Properties of EM waves (fading, diffraction, reflection) • Power • Quality and types of devices • Voids • Location • Result: varying link qualities

32. Summary – Control Theory • System ID • Model • Set transient and steady state performance goals • Design controllers to meet requirements • Can adapt, can handle uncertainty

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