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Localization and Secure Localization

Localization and Secure Localization. The Problem. The determination of the geographical locations of sensor nodes Why do we need Localization? Manual configurations of locations is not feasible for large-scale WSNs

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Localization and Secure Localization

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  1. Localization and Secure Localization

  2. The Problem • The determination of the geographical locations of sensor nodes • Why do we need Localization? • Manual configurations of locations is not feasible for large-scale WSNs • Location information is necessary for some applications and services, e.g. geographical routing • Providing each sensor with localization hardware (e.g., GPS) is expensive in terms of cost and energy consumption

  3. Localization • In some applications, it is essential for each node to know its location • Global Positioning System (GPS) is not always possible • GPS cannot work indoors • GPS power consumption is very high

  4. Solutions • Range-based • Use exact measurements (point-to-point distance estimate (range) or angle estimates) • More expensive • Ranging: the process of estimating the distance between the pair of nodes • Range-free • Only need the existences of beacon signals • Cost-effective alternative to range-based solutions

  5. Localization Algorithms in WSNs • Beacon Nodes know their locations • Range-based Algorithms • Sensor nodes need to measure physical distance-related properties • How to measure distance • RSSI (Received Signal Strength Indication) • ToA (Time of Arrival) • TDOA (Time Difference of Arrival) • How to estimate location • MMSE (Minimum Mean Square Estimation) • Range Free Algorithms • Do Not involve distance estimation

  6. Localization Algorithms in WSNs

  7. Range-based Solutions - MMSE • MMSE: • Minimum Mean Square Estimation

  8. Ideally, ei should be 0 Range-based Solutions - MMSE

  9. Range-based Solutions - MMSE • Rearrange the previous equations, we have • We have N equations

  10. Range-based Solutions - MMSE • Eliminate , we get the following N-1 equations • Hx = z

  11. Range-based Solutions - MMSE • H

  12. Range-based Solutions - MMSE • z

  13. Range-based Solutions - MMSE • x • Solution

  14. Range-free Approach - Centroid • Ref[Loc_1], Section 2.1

  15. Security Concerns in WSNs • Secure Localization Problem • Secure Localization Solutions

  16. Secure Localization • Attack-resistant Minimum Mean Square Estimation • Ref[Loc_2]

  17. Attack-resistant Minimum Mean Square Estimation

  18. Minimum Mean Square Estimation • The more inconsistent a set of location references is, the greater the corresponding mean square error should be • Ref[Loc_2], Section 2

  19. Impact of Malicious Beacons

  20. Impact of Malicious Beacons

  21. Minimum Mean Square Estimation • τis important: Depend on many factors

  22. How to Decide the set of Consistent Location References? • Given a set L of n location references and a threshold τ • Optimal solution • Greedy solution

  23. How to decide τ? • Measurement error model • How to obtain? • Study the distribution of the mean square error when there are no malicious attacks

  24. Voting-based Location Estimation – Basic Ideas

  25. Iterative Refinement • The larger the number of cells • More state variables need to be kept • The smaller each cell will be – precision • Iterative Refinement • Initially, the number of cells is chosen based on memory constraints • After the first round, the node may perform the voting process on the smallest rectangle that contains all the cells having the largest vote

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