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Efficient Geographic Routing in Multihop Wireless Networks

This paper presents a novel approach to geographic routing in multihop wireless networks through the introduction of the Normalized Advance (NADV) link metric. Unlike traditional methods that select the closest neighbor to the destination, NADV allows for selecting neighbors based on a balanced trade-off between link quality and distance to optimize packet delivery. The paper discusses techniques for effective link cost estimation, including packet error rate and power consumption assessments, and demonstrates the performance benefits of NADV through simulation results in a controlled environment. Future work aims to refine the link cost model for better adaptability.

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Efficient Geographic Routing in Multihop Wireless Networks

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  1. Efficient Geographic Routing in Multihop Wireless Networks Seungjoon Lee*, Bobby Bhattacharjee*, and Suman Banerjee** *Department of Computer Science University of Maryland **Department of Computer Sciences University of Wisconsin-Madison Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing (MobiHoc '05) Chien-Ku Lai

  2. Outline • Introduction • New Link Metric for Geographic Routing • Link Cost Types and Estimation • Simulation • Conclusions and Future Work

  3. Introduction- Geographic Routing (Position-based Routing) • This kind of routings uses location information for packet delivery • Neighbors locally exchange location information • Neither route establishment nor per-destination state is required

  4. Introduction- Normalized Advance (NADV) • Instead of the neighbor closest to the destination, NADV lets users select the neighbor with the best trade-off between link cost and proximity

  5. Introduction- about this paper • For the effective use of NADV, this work presents techniques for efficient and adaptive link cost estimation • Providing multiple techniques thus enabling nodes to choose the best scheme for the current network and system setting

  6. New Link Metric for Geographic Routing Background Normalized Advance

  7. Background • Link cost • the power consumption required for a packet transmission over the link • Link metric • “degree of preference” in path selection

  8. Background (cont.) • In many geographic routing protocols • The current node S greedily selects the neighbor that is closest to destination T

  9. Background- Goal • To gain as large advance as possible for fast and efficient packet delivery • To balance the trade-off, so that we can select a neighbor with both large advance and good link quality

  10. Normalized Advance (NADV)

  11. Link Cost Types and Estimation Packet Error Rate (PER) Delay Power Consumption

  12. Packet Error Rate (PER)

  13. Packet Error Rate (PER) • Using Probe Messages for PER Estimation • Using Signal-to-Noise Ratio for PER Estimation • Neighborhood Monitoring for PER Estimation • Self Monitoring for PER Estimation

  14. Packet Error Rate (PER)- Using Probe Messages for PER Estimation

  15. ： the received power ： the transmission bit rate ： the channel bandwidth ： the complementaryerror function ： thenoise power Packet Error Rate (PER)- Using Signal-to-Noise Ratio for PER Estimation • Assuming an AWGN (Additive White Gaussian Noise) channel, in the case of BPSK (Binary Phase Shift Keying), the bit error rate is given by

  16. Packet Error Rate (PER)- Neighborhood Monitoring for PER Estimation • In IEEE 802.11 networks • using the MAC sequence number A can count how many frames from neighbor B it has missed • The quality of two directional links may differ

  17. Packet Error Rate (PER)- Self Monitoring for PER Estimation • Aging • multiply PERs of unused links by 0.9 every 30 seconds

  18. Delay • Two types of link delay • medium time • total delay – future work

  19. Power Consumption

  20. Simulation Model Results

  21. Simulation Model • Simulator： ns-2 • Deployment： uniform • Region： 1000m x 1000m • Nodes： 100 • Maximum transmission range： 250m

  22. Simulation Results- Number of transmissions/delivery

  23. Simulation Results- Average Path Length

  24. Simulation Results- Latency

  25. Simulation Results- Using Delay as Link Cost

  26. Simulation Results- Power Consumption

  27. Conclusions and Future Work

  28. Conclusions • This work has introduced NADV as link metric for geographic routing • Geographic routing with NADV provides an adaptive routing strategy • is general • can be used for various link cost types • This work presented techniques for link cost estimation • NADV also finds paths whose cost is close to the optimum

  29. Future Work • To design a link cost model that balances multiple cost criteria • To implement the NADV framework on real testbeds and evaluate the performance in practice

  30. Question? Thank you.

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