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. Range-based Solutions - MMSE Ideally, e i should be 0

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. z

15. 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 Ref[Loc_2], Section 2 The more inconsistent a set of location references is, the greater the corresponding mean square error should be

21. Impact of Malicious Beacons

23. Minimum Mean Square Estimation τ is 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?