1. Topology Control Presenter: Ajit Warrier With Dr. Sangjoon Park (ETRI, South Korea), Jeongki Min and Dr. Injong Rhee (advisor) North Carolina State University Networking Lab http://netsrv.csc.ncsu.edu

2. Introduction: Topology Control

3. Topology Control/Clustering ■ Reduce structural complexity in a network. ■ Delegate complex/energy consuming activities to a subset of nodes in the network.

4. Topology Control Approaches Power Control Most often used in wireless ad-hoc networks. Reduce routing complexity. Reduce wireless interference. Preserve network capacity ? Connectivity ?

5. Topology Control Approaches Connected Backbone A B Most often used in wireless ad-hoc networks. Reduce routing complexity. Reduce wireless interference. Preserve network capacity ?

6. Topology Control Approaches Clustering/Hierarchy Most often used in wireless sensor networks. Reducing complexity not the issue, radio power consumption is ! Reduce radio transmissions/energy consumption. Do not care (as much) about capacity.

7. Topology Control – Pros/Cons Pros ■ Energy Efficient – Radio draws order of magnitude more energy than the sensing board. ■ Less radio interference. ■ Less routing complexity. Cons ■ Loss of routing selectivity. ■ Topology maintenance overhead.

8. Motivation Lots of theory/simulation – very few experimental results. ■Complicated algorithms. ■Assumptions in the algorithm difficult to realize in practice: ■Wireless links usually vary in quality over time. ■Wireless links not binary in nature. ■Wireless links may be asymmetric. ■Sensor nodes have low speed CPUs, may not be possible to run complex algorithms.

9. barrier Mica2 nodes Mica2Dot nodes observer G3 G2 G1 HEED experimental testbedFLOC experimental testbed

10. Algorithm and Analysis

11. Our Topology Control Algorithm - Overview ■ Divide the sensor network into approximately equal regions called clusters. ■ Cluster Members  Every node belongs to one cluster.  Perform sensing, if an event occurs, transmit event to cluster head. ■ Cluster Head  Within radio range of all nodes of a cluster.  Responsible for two activities:  Collect sensing reports from members.  Route/forward sensing reports toward the sink. ■ Gateways  Member nodes acting as connecting link between two clusters.

12. Algorithm - Overview

13. Cluster Head Election Algorithm Time-line of a node, in rounds

14. Cluster Head Election Algorithm Flip coin with probability p 0 Time-line of a node, in rounds

15. Cluster Head Election Algorithm Flip coin with probability p 0 Time-line of a node, in rounds Lose

16. Cluster Head Election Algorithm Flip coin with probability p 0 Flip coin with probability kp 0 Time-line of a node, in rounds Lose

17. Cluster Head Election Algorithm Flip coin with probability p 0 Flip coin with probability kp 0 Time-line of a node, in rounds Lose

18. Cluster Head Election Algorithm Flip coin with probability p 0 Flip coin with probability kp 0 Flip coin with probability k 2 p 0 Time-line of a node, in rounds Lose

19. Cluster Head Election Algorithm Flip coin with probability p 0 Flip coin with probability kp 0 Flip coin with probability k 2 p 0 Time-line of a node, in rounds Lose Win – Become Cluster Head Transmit Cluster Head Announcement (CHA)‏

20. Cluster Head Election Algorithm Flip coin with probability p 0 Time-line of a node, in rounds Lose Receive CHA – Become Member Node

21. Cluster Head Selection

22. Gateway Selection

23. Routing Phase

24. Data Transmission – Differential Duty Cycling Cluster heads, gateways responsible for routing/data forwarding => set radio to high duty cycle. Member nodes only responsible for sensing => set radio to low duty cycle (ideally to 0%). Ratio of duty cycle of member nodes to that of cluster heads/gateway nodes decides energy efficiency of network.

25. Analysis Result – Energy Saving Ratio  Ratio  Ratio  Ratio  Ratio 

26. Topology Control Operations

27. Experimental Results

28. Experimental Platform  Platform: Motes (UC Berkeley)‏ 8-bit CPU at 4MHz 128KB flash, 4KB RAM 916MHz radio TinyOS event-driven The algorithm has been implemented on Mica2 sensor nodes running the TinyOS event-driven operating system.

29. Experimental Testbed ■42 Mica2 sensor motes in Withers Lab. ■Wall-powered and connected to the Internet via Ethernet ports. ■Programs uploaded via the Internet, all mote interaction via wireless. ■Links vary in quality, some have loss rates up to 30-40%. ■Asymmetric links also present.

30. Experimental Testbed – Connectivity

31. Experimental Testbed – Snapshot

32. Implementation Details ■ MAC Layer – B-MAC  CSMA-based.  Duty Cycled. ■ Routing Layer – Mint  DSDV-like table driven, proactive  Uses link level measurements to select routing parents. ■ Member nodes switch off their radio. (δ = 0)‏ ■ Cluster heads tested with varying duty cycles (X = 2% - 45%)‏ ■ Radio is 19.2 Kbps, packet payload of 36 bytes.

33. Experimental Method ■ Every node transmits packets with probability α% per second. ■ α varied for two types of scenarios  Low Data Rate Experiment  Nodes idle most of the time, brief periods of activity, e.g. Earthquake detection.  α = 0.1 – 1  High Data Rate Experiment  Application scenarios with more periodicity, e.g. Temperature monitoring.  α = 10 – 100

34. Algorithm Overhead ■ Total energy of 5 J is 0.03% of the total battery capacity. ■ Half the time overhead is because of routing. ■ Given time synch period of 10s, it is feasible to use a reclustering period of 17 hours.

35. Energy Efficiency – Low Data Rate Topology ControlB-MAC 2% Duty Cycle5% Duty Cycle10% Duty Cycle

36. Energy Efficiency – High Data Rate Topology ControlB-MAC 2% Duty Cycle5% Duty Cycle10% Duty Cycle

37. Throughput B-MAC Topology Control B-MAC

38. Conclusion and Future Work ■ As a thumb rule, topology control can extend network lifetime by the network density divided by 4-8. ■ Topology control is not necessarily capacity conserving, may result in up to 50% loss in throughput. This is due to reduced routing selectivity. ■ Given the mathematical analysis, one may attempt to optimize the algorithm for some system performance metric, for instance throughput. ■ Need to develop robust algorithms for node failure resolution.