Topology Control Presenter: Ajit Warrier

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 B A • 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. HEED experimental testbed FLOC experimental testbed G2 Mica2 nodes G1 barrier G3 observer Mica2Dot nodes

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 p0 Time-line of a node, in rounds

15. Cluster Head Election Algorithm Lose Flip coin with probability p0 Time-line of a node, in rounds

16. Cluster Head Election Algorithm Lose Flip coin with probability p0 Flip coin with probability kp0 Time-line of a node, in rounds

17. Cluster Head Election Algorithm Lose Lose Flip coin with probability p0 Flip coin with probability kp0 Time-line of a node, in rounds

18. Cluster Head Election Algorithm Lose Lose Flip coin with probability p0 Flip coin with probability kp0 Flip coin with probability k2p0 Time-line of a node, in rounds

19. Transmit Cluster Head Announcement (CHA)‏ Cluster Head Election Algorithm Lose Lose Win – Become Cluster Head Flip coin with probability p0 Flip coin with probability kp0 Flip coin with probability k2p0 Time-line of a node, in rounds

20. Cluster Head Election Algorithm Lose Receive CHA – Become Member Node Flip coin with probability p0 Time-line of a node, in rounds

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 d = 0 Ratio d = 0.2 Ratio d = 0.4 Ratio d = 0.6 Ratio d = 1

26. Topology Control Operations

27. Experimental Results

28. 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. Experimental Platform

29. 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. Experimental Testbed

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. Topology Control B-MAC Energy Efficiency – Low Data Rate 2% Duty Cycle 5% Duty Cycle 10% Duty Cycle

36. Topology Control B-MAC Energy Efficiency – High Data Rate 2% Duty Cycle 5% Duty Cycle 10% Duty Cycle

37. Throughput B-MAC B-MAC Topology Control Topology Control

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.