1. Localization

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

3. Prerequisites • Basic mathematics knowledge • Basic concepts in network protocols

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