Localization Learning Objectives Understand why WSNs need localization
Localization
Learning Objectives • Understand why WSNs need localization protocols • Understand localization protocols in WSNs • Understand secure localization protocols
Prerequisites • Basic mathematics knowledge • Basic concepts in network protocols
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
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
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
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) • To. A (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
Localization Algorithms in WSNs
Range-based Solutions - MMSE • MMSE: – Minimum Mean Square Estimation
Range-based Solutions - MMSE • Ideally, ei should be 0
Range-based Solutions - MMSE • Rearrange the previous equations, we have • We have N equations
Range-based Solutions - MMSE • Eliminate equations • Hx = z , we get the following N-1
Range-based Solutions - MMSE • H
Range-based Solutions - MMSE • z
Range-based Solutions - MMSE • x • Solution
Range-free Approach - Centroid • Ref[Loc_1], Section 2. 1
Security Concerns in WSNs • Secure Localization Problem • Secure Localization Solutions
Secure Localization • Attack-resistant Minimum Mean Square Estimation • Ref[Loc_2]
Attack-resistant Minimum Mean Square Estimation
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
Impact of Malicious Beacons
Impact of Malicious Beacons
Minimum Mean Square Estimation • τ is important: Depend on many factors
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
How to decide τ? • Measurement error model • How to obtain? – Study the distribution of the mean square error when there are no malicious attacks
Voting-based Location Estimation – Basic Ideas
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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