IEEE INFOCOM 2008 Mehmet C. Vuran and Ian F. Akyildiz Database Lab. Soo Hyung Kim

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. SooHyung 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 Database Laboratory

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

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 Laboratory

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 Laboratory

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 Laboratory

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 Laboratory

8. Factors Affecting the Packet Size • Carrier sense mechanism • Successful carrier sense • No collision transmission • Formulation(from [4]) Database Laboratory

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 thetransmission rage • MAC Failure rate Database Laboratory

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

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 Laboratory

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

13. Packet Size Optimization Framework • End-to-End energy consumption [6] Database Laboratory

14. Packet Size Optimization Framework • Etx for ARQ and FEC • Similar approach for , , , , , Database Laboratory

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

16. Optimization Results Database Laboratory

17. Optimization Results • Using MATLAB Database Laboratory

18. Optimization Results • Very long packet sizes have problem [7] Database Laboratory

19. Optimization Results • Certain WSN application • End-to-End latency • Reliability constraints Database Laboratory

20. Optimization Results Database Laboratory

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 Laboratory

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 Laboratory

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 Laboratory

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 Laboratory

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

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

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

28. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks • Wireless Underwater Sensor Networks • Optimum Packet Throughput Database Laboratory

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

30. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks • Wireless Underwater Sensor Networks • Optimum Packet Size for Database Laboratory

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

32. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks • Wireless Underground Sensor Networks • Optimum Packet Size Database Laboratory

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

34. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks • Wireless Underground Sensor Networks • Optimum Packet Size for Database Laboratory

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

36. Packet Size Optimization in Wireless Underwater and Underground Sensor Networks • Wireless Underground Sensor Networks • Optimum Packet Throughput Database Laboratory

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 Laboratory

38. Thank you!! Database Laboratory

39. Reference • [1] F. Poppe, D. De Vleeschauwer, G. H. Petit, “Choosing the UMTS airinterfaceparameters, the voice packet size and the dejitteringdelay for a voice-over-IP call between a UMTS and a PSTN party,”inProc. 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 Laboratory