1. SenProbe: Path Capacity Estimation in Wireless Sensor Networks Tony Sun, Ling-Jyh Chen, Guang Yang M. Y. Sanadidi, Mario Gerla

2. SenMetrics05 2 Outline Motivation Background  Design Consideration  What do we want to measure?  Effective Capacity  Previous Method Proposed Approach: SenProbe Simulation Results Conclusion

3. SenMetrics05 3 Motivation Mobile computing platforms may interact with ambient sensor environment establishing “Opportunistic wireless networks” Evaluation and measurement of wireless path capacity in sensor network is of realistic interest  (i.e. Capacity planning, protocol design, performance analysis, system deployment, assess applicability of deployment) Need a tool that can monitor and measure opportunistic path capacity in wireless sensor networks

4. SenMetrics05 4 Outline Motivation Background Background  Design Consideration  What does SenProbe actually measure?  Effective Capacity  Previous Method Proposed Approach: SenProbe Simulation Results Conclusion

5. SenMetrics05 5 Design Consideration CSMA-CA and variant schemes still popularly used in sensor network for its simplicity  (IEEE 802.15.4 beaconless mode, Berkeley motes, etc) Basic CSMA-CA doesn’t incorporate RTS/CTS mechanisms  Send packet when an idle channel is detected  Smaller packet overhead if idle channel can be detected quickly  Suffers from hidden terminal problem

6. SenMetrics05 6 What do we actually measure? (1) Narrow Neighborhood capacity  The minimal set of nodes that must be inactive (no tx nor receive) while a transmission takes place.  Equivalently, the region affected by the transmission  Only one packet can be in the neighborhood at a time Practically, N-hood capacity = link speed/# of N-hood hops The N-hood Capacity trivially reverts to link capacity for the wired section of the path.

7. SenMetrics05 7 What do we want to measure? (1) The effective end-to-end rate is defined as the maximum achievable data rate in the absence of any cross traffic connection. It is smaller than the raw data rate at the physical layer due to  Packet Overhead  Interference between multiple packets in the pipeline

8. SenMetrics05 8 What do we want to measure? (2) In fact, path capacity in wireless net also varies with:  MAC protocol and link scheduling  Link interference  S/N ratio;  Tx power  Encoding/modulation scheme  Number of antennas (eg MIMO)  Antenna directionality  etc

9. SenMetrics05 9 Neighborhood Example If D r = D i =250m, nodes {3,4,5} are within the same n-hood, C’=C/3 If D r =250m, D i =500m, nodes {2,3,4,5,6} are in n-hood, C’=C/4 D r = effective receive range from node 4 (solid-line circle) D i = interference range caused by node 4 (dotted-line circle) Distance between nodes: 200m

10. SenMetrics05 10 Effective Capacity of CSMA-CA Enabled Wireless Channel The effective capacity of a one-hop link can be calculated as For the CSMA environment in our study (if ACKs are used)

11. SenMetrics05 11 Previous Work (Morris et al) Dr=250m, Di=500m Use UDP flows to probe the maximum achievable throughput (brute force method)

12. SenMetrics05 12 Outline Motivation Background  Design Consideration  What does SenProbe actually measure?  Effective Capacity  Previous Method Proposed Approach: SenProbe Proposed Approach: SenProbe Simulation Results Conclusion

13. SenMetrics05 13 CapProbe Concept Key insight: a packet pair that gets through with zero queueing delay yields the exact estimate Capacity Capacity

14. SenMetrics05 14 Issues: Compression and Expansion Queueing delay on the first packet => compression Queueing delay on the second packet => expansion

15. SenMetrics05 15 SenProbe Path capacity estimation tool specially designed for the multi-hop CSMA based wireless networks.  One-way estimation technique, based on CapProbe concepts  Aimed to simplify the path capacity estimation process A back-to-back packet train technique designed to overcome the hidden terminal effects in CSMA environment SenProbe measures end-to-end effective capacity in wireless ad hoc networks. SenProbe is simple, fast and less intrusive to comparative techniques.

16. SenMetrics05 16 SenProbe Algorithm(1) Instead of using back-to-back packet pairs, SenProbe relies on back-to-back packet train to overcome the effect of hidden terminal in CSMA-CA The length of this back-to-back packet train depends on the interference range and the transmission range of the specific radio technology under question

17. SenMetrics05 17 SenProbe Algorithm(2) The receiver measures the OWD of every packet in kth packet train received as the difference between time received and time sent the minimum OWDSUM is kept for the kth packet train. The “good” dispersion sample r (i.e. samples encountering no cross traffic) is the sample with the minimum OWD sum Dispersion of the good sample calculated, and used to estimation capacity

18. SenMetrics05 18 SenProbe-Visualization 1) 2) 3) 4)

19. SenMetrics05 19 Outline Motivation Background  Design Consideration  What does SenProbe actually measure?  Effective Capacity  Previous Method Proposed Approach: SenProbe Simulation Results Simulation Results Conclusion

20. SenMetrics05 20 Simulation Results (1) Path Capacity measured via FTP connection and Packet-Pair technique (one way CapProbe)

21. SenMetrics05 21 Simulation Results (2) Path capacity of adhoc multi-hop forwarding chain in CSMA-CA wireless environment

22. SenMetrics05 22 Simulation Results (3) End-to-end capacity estimation of multi-hop connections within the same collision domain

23. SenMetrics05 23 Simulation Results (4) Capacity estimates along a multi-hop forwarding chain for CSMA-CA with ACK enabled wireless sensor network

24. SenMetrics05 24 Grid Topology Estimate capacity (S1 -> Sink) with different cross traffic rates CSMA-CACSMA-CA with ACK

25. SenMetrics05 25 Conclusion SenProbe uses back-to-back packet trains, and relies on packet dispersion between the packet trains to measure the path capacities in a one-way fashion. SenProbe estimates e2e path capacity in CSMA enabled wireless sensor networks. SenProbe is a simple and non-intrusive technique that can accurately reflects the effective path capacity

26. SenMetrics05 26 Thanks!