1 / 28

Localization and Secure Localization

Localization and Secure Localization. Learning Objectives. Understand why WSNs need localization protocols Understand localization protocols in WSNs Understand secure localization protocols. Prerequisites. Module 7 Basic concepts of network security Basic concepts of computer networks.

ina
Download Presentation

Localization and Secure Localization

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. Localization and Secure Localization

  2. Learning Objectives • Understand why WSNs need localization protocols • Understand localization protocols in WSNs • Understand secure localization protocols

  3. Prerequisites • Module 7 • Basic concepts of network security • Basic concepts of computer networks

  4. 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

  5. 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

  6. 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

  7. 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

  8. Localization Algorithms in WSNs

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

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

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

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

  13. Range-based Solutions - MMSE • H

  14. Range-based Solutions - MMSE • z

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

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

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

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

  19. Attack-resistant Minimum Mean Square Estimation

  20. 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

  21. Impact of Malicious Beacons

  22. Impact of Malicious Beacons

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

  24. 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

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

  26. Voting-based Location Estimation – Basic Ideas

  27. 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

  28. Assignment • 1. What is the basic idea of the MMSE-based localization protocols in wireless sensor networks? • 2. What is the basic idea of the MMSE-based secure localization protocols in wireless sensor networks? • 3. What are the differences between range-based and range-free localization algorithms?

More Related