1 / 18

Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection

This paper explores efficient traffic allocation strategies in wireless networks to mitigate jamming effects. By utilizing multiple-path routing, the study introduces a novel portfolio selection framework to maximize overall throughput. It assesses different jamming behaviors, such as constant, random, and mobile jammers, and integrates traffic allocation with path probabilities to avoid congestion. The solution employs quadratic programming and Lagrangian dual decomposition to enable distributed and scalable traffic management, ensuring robust communication even in adversarial conditions.

fiona-england
Download Presentation

Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection Authors: P. Tague et al. IEEE/ACM transactions on Networking Presented by: Ying Xuan

  2. Jamming Behaviors • disturb wireless communications • proactive / reactive • constant, random, repeat, deceive • single bit/packet • outsider / insider • static / mobile • nondeterministic and dynamic http://khongtenmien.com/forum/showthread.php?p=8462

  3. Multiple-Path routing • Anti-jamming techniques = diversity • Multiple frequency bands • Different MAC channels • Multiple Routing paths • Multi-Path Routing • Each source node chooses multiple paths • Each path is allocated with different traffic amount (how to avoid congestion?) • Each path has different probabilities to be jammed (how to measure this?) http://w3.antd.nist.gov/adhoc/Project_drouting.shtml Goal: Efficiently allocate the traffic to maximize the overall throughput.

  4. Roadmap • Use PDR to approximate the overall throughput • What is PDR? How to get PDR? • Use a quadratic program based on Portfolio Selection Theory to give the optimal solution • Objective Function? Constraints? • Use Lagrangian dual decomposition to get a distributed solution • Efficiency: accuracy, convergence, scalability.

  5. Represent the throughput - IEstimate local packet success rates (LPSR) • Each node updates (LPSR), Update period T << Ts update relay period • Estimated value by Packet Delivery Rate (PDR) • Variance by the variance of PDR

  6. Represent the throughput - IIEnd-to-End Packet Success Rate • Mean • Variance/covariance • Given that there are Ls paths at source node s, what does these two above mean?

  7. Represent the throughput – III • Given that the traffic allocation vector at node s is • Expected throughput: • Variance:

  8. Formulate Optimal Solution – IPortfolio Selection • Analogy of concepts http://proteger.com.my/articles/investment/modernportfolio.html

  9. Formulate Optimal Solution – IPortfolio Selection (cont’) • risk-aversion factor ks • ks = 0 means the throughput is maximized regardless of any risks • Ks>0 (0.005 in the simulation)

  10. Formulate Optimal Solution – IICongestion Avoidance • Delivery rate from s to node i is • The aggregate traffic going through link (i,j) is

  11. Formulate Optimal Solution - III

  12. Iterative and Distributed Solution • Many allocation quadratic program on large-scale networks can be solved efficiently through decomposition techniques • Daniel P. Palomar and MungChiang, A Tutorial on Decomposition Methods for Network Utility Maximization. • Lagrangian dual decomposition

  13. Lagrangian Duality

  14. Decomposition Topology

  15. Decomposition Flow lagrangian decouple(c) dual function • the dual function could be solved using a gradient method

  16. Apply to this formulation Local update at step 3 requires mutual information exchanges at all the sources……

  17. What can we learn • Way to formulate throughput • Way to solve quadratic program distributedly for large-scale networks

  18. Q&A • Thanks!

More Related