1. Cross-layer Packet Size Optimization for Wireless Terrestrial, Underwater, and Underground Sensor Networks IEEE INFOCOM 2008 Mehmet C. Vuran and Ian F. Akyildiz Database Lab. Soo Hyung Kim

2. Contents  Introduction  Related Work  Factors Affecting the Packet Size  Packet Size Optimization Framework  Optimization Results  Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Conclusion 2Database Laboratory

3. Introduction  Traditional approach Point-to-point link  Successful and efficient transmission Cannot be captured multi-hop, broadcast nature 3Database Laboratory Node serial cable, phone line

4. Introduction  Multi-hop WSN Routes established Existence of neighbor nodes Wireless channel and error control technique  Nature of WSN Terrestrial areas Underwater (UW-ASN) Underground (WUSN) Database Laboratory4

5. Introduction  Cross-layer solution for packet size optimization The effects of multi-hop routing The broadcast nature of the physical wireless channel The effects of error control techniques  Three objective functions Packet throughput Energy consumption Resource utilization Database Laboratory5

6. Related Work  Voice Packet Size between UMTS-to-PSTN [1] Single hop communication  Improving Wireless Link [2] Variable packet size  Properties of the wireless channel  Energy efficiency [3] Most relevant work Effects of error correction on energy efficiency Energy channel model is based on single hop Database Laboratory6

7. Factors Affecting the Packet Size  Factors(focus on energy consumption) Transmit a packet and Reliability of the network  Small packet size  increase reliability  inefficient transmission  Longer packet size  provide error resiliency  increased energy consumption Collision  Longer packet size  increase the collision rate Database Laboratory7

8. Factors Affecting the Packet Size  Carrier sense mechanism Successful carrier sense No collision transmission  Formulation(from [4]) Database Laboratory8 Probability of successful carrier sense Probability of sensing the channel free Probability of no collision Overall traffic rate

9. Factors Affecting the Packet Size  Total generated packet rate (pkts/s)  b : average sampling rate  Ld : packet payload  i : node  M : number of nodes in the transmission rage  MAC Failure rate Database Laboratory9

10. Packet Size Optimization Framework  Three objective function Packet throughput Energy per useful bit Resource utilization Database Laboratory10  Ld : payload length  PER : end-to-end packet error rate  T : end-to-end latency  E : end-to-end energy consumption

11. Packet Size Optimization Framework  Channel-aware algorithm Determine next hop using SNR  SNR ( ) Signal to noise ratio  Medium access RTS-CTS-DATA exchange  Error correction ACK and ARQ FEC code  (n,k,t) - n:block length, k:payload length, t:error correcting capability in bits Database Laboratory11

12. Packet Size Optimization Framework  Channel model Log-normal channel model [5]  Database Laboratory12

13. Packet Size Optimization Framework  End-to-End energy consumption [6] Database Laboratory13

14. Packet Size Optimization Framework   Etx for ARQ and FEC  Similar approach for,,,,, Database Laboratory14

15. Optimization Results  Energy consumption Packet size SNR threshold  Packet size optimization is affected by the routing decisions. Database Laboratory15

16. Optimization Results Database Laboratory16

17. Optimization Results  Using MATLAB Database Laboratory17

18. Optimization Results  Very long packet sizes have problem [7] Database Laboratory18

19. Optimization Results  Certain WSN application End-to-End latency Reliability constraints Database Laboratory19

20. Optimization Results Database Laboratory20

21. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Underwater Channel Model Urick path loss formula [8]  Signal level  SNR of channel  Bit error rate  where Database Laboratory21

22. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Underwater Channel Model 2-path Rayleigh model  Direct path signal  Surface reflected path signal Bit error rate  Combination of these signals 2-path Rayleigh model  Not closed form expression for SNR distribution  Performed simulation to find these values Database Laboratory22

23. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Underground Channel Model [9] 2-path location-based Rayleigh fading channel model VWC(volumetric water content) of the soil Total path loss  Bit error rate  SNR  Database Laboratory23

24. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Results Three different optimization problems ,, Underwater  Deep water network  Two-ray underwater channel model  Shallow water network  Reflections from the sea surface Underground  Channel model presented in previous page  Effects of volumetric water content(VWC) Database Laboratory24

25. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underwater Sensor Networks Deep Water Environment Database Laboratory25

26. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underwater Sensor Networks Shallow Water Environment Database Laboratory26

27. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks Database Laboratory27  Wireless Underwater Sensor Networks Optimum Energy Consumption

28. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks Database Laboratory28  Wireless Underwater Sensor Networks Optimum Packet Throughput

29. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks Database Laboratory29  Wireless Underwater Sensor Networks Optimum Resource Utilization

30. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks Database Laboratory30  Wireless Underwater Sensor Networks Optimum Packet Size for

31. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks Database Laboratory31  Wireless Underwater Sensor Networks Optimum Energy Consumption

32. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underground Sensor Networks Optimum Packet Size Database Laboratory32

33. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underground Sensor Networks Optimum Energy Consumption Database Laboratory33

34. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underground Sensor Networks Optimum Packet Size for Database Laboratory34

35. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underground Sensor Networks Optimum Energy Consumption Database Laboratory35

36. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks  Wireless Underground Sensor Networks Optimum Packet Throughput Database Laboratory36

37. Conclusion  Packet size optimization for wireless terrestrial, underwater, and underground sensor networks  Framework Medium access collisions Routing decisions  Performance metrics Throughput Energy consumption Packet error rate Database Laboratory37 Environment Terrestrial15225025 Underwater(Deep)66843916 Underwater(Shallow)12321003236 Underground(vwc=5%)86450223

38. Thank you!! Database Laboratory38

39. Reference  [1] F. Poppe, D. De Vleeschauwer, G. H. Petit, “Choosing the UMTS airinterface parameters, the voice packet size and the dejitteringdelay for a voice-over-IP call between a UMTS and a PSTN party,”in Proc. IEEE INFOCOM 2001, vol. 2, pp. 805 -814, April2001.  [2] P. Lettieri, M. B. Srivastava, “Adaptive frame length control for improving wireless link throughput, range, and energy efficiency,” in Proc. IEEE INFOCOM 1998, vol. 2, pp. 564 -571, April 1998.  [3] Y. Sankarasubramaniam, I. F. Akyildiz, S. W. McLaughlin, “Energy efficiency based packet size optimization in wireless sensor networks,” in Proc. IEEE Internal Workshop on Sensor Network Protocols and Applications, pp. 1 - 8, 2003.  [4] K. Schwieger, A. Kumar, G. Fettweis, “On the Impact of the Physical Layer on Energy Consumption in Sensor Networks,” in Proc. EWSN ’05, pp. 13 - 24, Feb. 2005.  [5] M. Zuniga, B. Krishnamachari, “Analyzing the Transitional Region in Low Power Wireless Links,” in Proc. IEEE SECON ’04, pp. 517 – 526, Oct. 2004.  [6] M. C. Vuran and I. F. Akyildiz, “Cross Layer Analysis of Error Control in Wireless Sensor Networks,” in Proc. IEEE SECON ’06, Reston, VA, September 2006.  [7] IEEE 802.15.4, “Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low- Rate Wireless Personal Area Networks (LR-WPANs),” October 2003.  [8] I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater Acoustic Sensor Networks: Research Challenges,” Ad Hoc Networks Journal (Elsevier), vol. 3, no. 3, pp. 257-279, March 2005.  [9] L. Li, M. C. Vuran, and I. F. Akyildiz, “Characteristics of Underground Channel for Wireless Underground Sensor Networks,” in Proc. Med-Hoc- Net ’07, Corfu, Greece, June 2007. Database Laboratory39