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Adaptive Network Management for Countering Selective Capture in Wireless Sensor Networks

This paper presents a solution to the critical issue of selective capture near base stations in wireless sensor networks. By implementing dynamic radio adjustment, multisource multipath, and voting-based intrusion detection, the proposed adaptive network management enhances system robustness and energy efficiency while countering intrusions. The study explores various design parameters to optimize system performance, including maximizing Mean Time to Failure (MTTF) and evaluating the impact of attack strength on detection rates. The research demonstrates the effectiveness of utilizing multiple paths and sources to mitigate selective capture, highlighting the balance between energy consumption and network resilience for prolonged WSN lifespan.

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Adaptive Network Management for Countering Selective Capture in Wireless Sensor Networks

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  1. Adaptive Network Management for Countering Selective Capture in Wireless Sensor Networks • Authors: Hamid Al-Hamadi and Ing-Ray Chen Paper Presentation By : Gaurav Dixit (gdixit@vt.edu)

  2. Critical node (selective) capture near base station The Problem

  3. The Problem

  4. The Problem

  5. The Problem No access to base station

  6. Dynamic radio adjustment • Multisource multipath • Voting-based intrusion detection The Solution Proposed

  7. Dynamic radio adjustment

  8. Dynamic radio adjustment

  9. Dynamic radio adjustment • SNs increases radio range dynamically to connect to n0 1-hop neighbors

  10. Multisource multipath Event Occurred

  11. Multisource multipath Event Occurred

  12. It would consume more energy • It would be more robust • What is best redundancy level? Multisource multipath

  13. Evict compromised nodes • “host IDS” run on SN to conserve energy • Each node monitors neighbors only. • False positives, false negatives. Voting-based intrusion detection

  14. ms - source redundancy • mp - path redundancy • m – number of voters • TIDS – the intrusion detection interval Design parameters

  15. Output: MTTF • Maximize MTTF by tuning design parameters. • Rq (tQ,j ) – probability of successful response to query j

  16. Max queries, Nq before system dies • First term says system fails on query i+1 Output…

  17. (if capture time is exponentially distributed) increasing bad nodes..

  18. number of good and bad nodes number of forwarding neighbors (f =1/4, geographical routing) is

  19. more than half of voting nodes bad (Voting) false positives.. selecting m neighbor nodes Good nodes give bad decisions due to host false positive probability

  20. Re-adjusting good/bad node densities Thus, possibility of a bad at dist x from BS is

  21. The Success Probability This is success probability of a path from SNj to BS.

  22. Failure probability with multipath : Failure probability Failure probability with multiple source and multipath :

  23. ‘Black ring’ consumes more energy • Multipath, multisource , frequent IDS consumes energy Energy!

  24. No. of IDS cycle before SN energy exhaustion Energy! Transmit nb bits : Receive nb bits : Energy consumed by SN at x for processing ith query:

  25. Number of SNs within range of SNs at distance x : Energy! Energy spent by a SN located at x in i-th IDS cycle.

  26. On TD timer event: • BS determines design parameters, and notifies SNs of new TIDSand m. • SNs update new settings. Adaptive network

  27. -- once in 4 weeks --once in 12 hrs to 3 days n0 = 7 Performance Optimal (mp ,ms ) values to counter ‘selective capturing’ (m=3):

  28. High detection in High Attack strength

  29. Low detection in High Attack strength

  30. Effect of varying TIDS

  31. MTTF comparison with Attack strength

  32. Density of Good SNs at distance X vs. Time

  33. MTTF comparison with Attack strength

  34. Three ways studied to counter selective capture of sensor nodes. • Proved experimentally that there exist an optimum value for multiple paths and multiple sources. • Studied the trade-off between energy expense and robustness of WSN, thus extending usable lifetime of WSN. Conclusion

  35. Thank you!

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