1. 1 SenMetrics’05, San Diego, 07/21/2005 SOSBRA: A MAC-Layer Retransmission Algorithm Designed for the Physical-Layer Characteristics of Clustered Sensor Networks Qingjiang Tian and Edward J. Coyle Center for Wireless Systems and Applications (CWSA) School of Electrical and Computer Engineering Purdue University {tianq,coyle}@ecn.purdue.edu

2. 2 SenMetrics’05, San Diego, 07/21/2005Outline  Background  SOSBRA Approach for Clustered Sensor Networks  Numerical Results  Optimal Contention Window  Conclusions

3. 3 SenMetrics’05, San Diego, 07/21/2005 Introduction  Design for Energy Efficiency Through All Layers of the Protocol Stack  Cross-Layer Design to Improve Performance Need to avoid fragility  My Work: Physical-MAC Layer Interface Small Propagation delay in sensor net applications Opportunity to redesign Retransmission algorithms Physical Energy Efficiency MAC Network Application

4. 4 SenMetrics’05, San Diego, 07/21/2005 Background  General – 802.11 MAC Layer CSMA/CA Collision Avoidance Binary Exponential Backoff Homogeneous peer-to-peer Designed for hidden nodes (RTS-CTS Handshake)  V. Bharghavan, “MACAW: A Media Access Protocol for Wireless LANS” All nodes can hear each other  Y. Kwon,etc, “A Novel MAC Protocol with Fast Collision Resolution for Wireless LANs” multiplicative-increase, linear-decrease  C. Wang,etc, “A new collision resolution mechanism to enhance the performance of IEEE 802.11 DCF,” contention window size is halved after c consecutive successful transmission

5. 5 SenMetrics’05, San Diego, 07/21/2005 Motivation for My Work  IEEE 802.11 Distributed Coordination Function (DCF) Called WiFi Homogeneous, peer-to-peer Communications Binary exponential backoff & cross-stage collisions

6. 6 SenMetrics’05, San Diego, 07/21/2005 Motivation for My Work  Clustering in Sensor Networks Clusterhead: central control, broadcasting, synchronization of other nodes Energy efficiency is a goal Increase throughput on the channel »Minimize collisions and idle time Very Small Propagation Delay fd SENSOR fd SENSOR fd SENSOR fd SENSOR fd SENSOR fd SENSOR fd SENSOR 100m

7. 7 SenMetrics’05, San Diego, 07/21/2005 SOSBRA: Synchronized, One-Stage-Backoff Retransmission Algorithm  Assumptions One-hop cluster considered Traffic model: collect one packet from each node within the cluster We ignore the small propagation delay between sensor nodes and CH All nodes within one cluster can be synchronized to within 1 microsecond Synchronization beam – similar to ZigBee – starts “rounds” or retransmissions on the channel Nodes can sense each other’s activity

8. 8 SenMetrics’05, San Diego, 07/21/2005 SOSBRA Approach 1.Each node that needs to either transmit or retransmit at the beginning of a round will chose a slot at random in a contention window of size W for its retransmission. 2.Nodes that transmit without collision are done. 3.Nodes in collisions in the current round will reschedule transmissions in the next round of W slots. 4.W is the same for every round.

9. 9 SenMetrics’05, San Diego, 07/21/2005 SOSBRA vs 802.11 DCF A A AC Standard 802.11 DCF 1 2………………… W 1 2 ……………. W New Round A B SOSBRA-based 802.11 DCF Window 1 Window 2 B C B ABC

10. 10 SenMetrics’05, San Diego, 07/21/2005 PerformanceAnalysis Performance Analysis N: Total non-CH nodes within the cluster W: fixed one stage contention window :Total time required to collect one packet from each node : The duration of a RTS collision : The duration of a data packet transmission

11. 11 SenMetrics’05, San Diego, 07/21/2005 PerformanceAnalysis Performance Analysis  No collisions (1) (2)

12. 12 SenMetrics’05, San Diego, 07/21/2005 PerformanceAnalysis Performance Analysis  N1 nodes succeed in the first round and all of remaining N2 nodes succeed in the second round,C1 collisions in the first round (3) (4)

13. 13 SenMetrics’05, San Diego, 07/21/2005 PerformanceAnalysis Performance Analysis (5)(5) (6) General Case

14. 14 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig.1 Numerical results for the probability mass function of, the total time to empty the cluster, for the SOSBRA-based 802.11 protocol. Here, N =50 nodes and W =120.

15. 15 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig. 2 Simulations for the SOSBRA-based 802.11 protocol that show during empty the cluster for different contention window sizes. is the number of nodes in the cluster.

16. 16 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig.3 Simulations for the SOSBRA-based 802.11 protocol that show the average channel throughput during the emptying the cluster for different contention window sizes. N is the number of nodes in the cluster.

17. 17 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig. 4 Simulations determining the optimal contention window size for different for the SOSBRA-based 802.11 protocol

18. 18 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig. 5 Simulations determining the minimum, for different cluster sizes for the SOSBRA-based 802.11 protocol.

19. 19 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig. 7. : Simulations comparing the wasted-time before the cluster is emptied for the SOSBRA-based 802.11, Standard 802.11 DCF, and ZigBee with and without GTS.

20. 20 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig. 8. : Simulations comparing total energy consumption to empty the cluster for the SOSBRA-based 802.11, Standard 802.11 DCF, and ZigBee with/without GTS. The energy consumption ratios used was idle:receive:send=1:2:2.5 11

21. 21 SenMetrics’05, San Diego, 07/21/2005 Numerical And Simulation Results Fig. 9. Comparison between SOSBRA and TDMA-based approaches. Here, and a slot time is 10 microsecond in SOSBRA.

22. 22 SenMetrics’05, San Diego, 07/21/2005  Probabilistic Approach Cost Function Cost results from two sources »The first is from the total idle slot W »The other one comes from possible collisions (7) Optimal Contention Window Size

23. 23 SenMetrics’05, San Diego, 07/21/2005 Optimal Contention Window Fig.10. Numerical Results showing Cost Function Vs 1/W

24. 24 SenMetrics’05, San Diego, 07/21/2005 Optimal Contention Window Fig.11. Comparison between simulation and analytical results

25. 25 SenMetrics’05, San Diego, 07/21/2005 Optimal Contention Window Fig. 12. Average Total time obtained with from both simulation and analysis.

26. 26 SenMetrics’05, San Diego, 07/21/2005 Large Number of Nodes if for very large N, We may approximate the total cost to be (11)

27. 27 SenMetrics’05, San Diego, 07/21/2005 Large Number of Nodes Define (12) (13) (14)

28. 28 SenMetrics’05, San Diego, 07/21/2005Conclusions  SOSBRA provides better performance in term of both time and energy compare to 802.11 DCF  Help minimize the multi-access interference (collisions) in design of physical access scheme, especially for CDMA approach  Our future work includes analysis of cross layer designs for wireless sensors with directional transmission capability physical layer improvements, including adaptive modulation schemes synchronization across a sensor network CDMA based optimization of PHY/MAC design

29. 29 SenMetrics’05, San Diego, 07/21/2005 Derivations of Formulas  N: Total non-CH nodes within the cluster  W: fixed one stage contention window  :Total time required to collect one packet from each node  : The duration of a RTS collision  : The duration of a data packet transmission

30. 30 SenMetrics’05, San Diego, 07/21/2005 Derivations of Formulas

31. 31 SenMetrics’05, San Diego, 07/21/2005 Derivations of Formulas (7) (8) (9)

32. 32 SenMetrics’05, San Diego, 07/21/2005 Derivations of Formulas Given N nodes and length W contention window, for each of the W Slots: 1.No nodes choose this slot………………………. 1.Only one node chooses this slot……………...... 1.More than one nodes choose this slot………… Cost Function: Cost results from two sources: total # of empty slots and possible collisions Minimize the Cost Function: (10)

33. 33 SenMetrics’05, San Diego, 07/21/2005 Derivations of Formulas if for very large N, We may approximate the total cost to be (11)

34. 34 SenMetrics’05, San Diego, 07/21/2005 Derivations of Formulas Define (12) (13) (14)