Wireless Sensor Network Localization Overview

Motivation “ Location is everything” Military Surveillance (meters) Location-aware services Military Surveillance (meters) Location-aware Services (10 meters) Interactive Gaming– WII (center meters)

Localization Technical related to localization Node Localization Target (source) Localization Location Directory Services person event Base Station vehicle

The need for Localization 1. Location of Data 2. Geo-Routing 3. Object Tracking Sensing Coverage 4. GHT-Base Storage 5. L-based Query 6. Location of Data Source Geo-Routing/Location-Scoped Routing Object Tracking Sensing Coverage GHT:A Geographic Hash Table Location-based query Location-based services (e.g. Virtual touch screen, point&use) Location-based Services (point & use) 7.

Outline Introduction Localization Taxonomy Range-based localization Range-free localization Event-driven Overview of 5 different schemes Range Free localization

Existing Solutions (Outdoor) Outdoor GPS Positioning with Wifi Hotspots Range-Based use absolute distance estimates or angle estimates Expensive, Precise Range-Free use only connectivity and proximity Cheap, Inaccurate

Existing Academia Solutions Range-Based MIT Cricket (Priyantha et at., 2000) UCLA AHLoS (Savvides et al., 2000) UWashington SpotON (Hightower et al., 2000) Range-Free Virginia APIT (He et al., 2003) MIT Amorphous Computing (Nagpal, 2003) Rutgers DV-Hop (Niculescu et al., 2003) USC Centroid (Bulusu et al., 2000) Event-Driven Spotlight, Lighthouse, MSP

Range based localization (1) Fine-grained localizations Uses absolute point to point distance/angle estimates Requires expensive hardware Common examples Time of Arrival (TOA) Time Difference of Arrival (TDOA) Angle of Arrive (AOA) Receive Signal Strength Indicator (RSSI)

Basic idea of Range-based Solutions Relative locations are unique if the graph is rigid To avoid translation, rotation and flipping, d+1 anchors are needed to obtain global positions in d dimension space. Non-rigid rigid rigid

Well-known Range-based solutions GPS Cricket (Priyantha ‘00) AHLoS (Savvides ‘00) Calamari (Whitehouse ’02) Robust Quadrilaterals (Moore ‘04) RIPS (Maroti ’05) TOA TDOA RSSI Noise issue Interferometric Range-Based solutions Robust Ranging + Rigid Theory + Geometry

Range free localization (1) Coarse-grained localization Localization that does not rely on distance measurement Simple hardware/Low cost solution

Anchor based localization Centroid Algorithm (Bulusu ‘00) APIT Centroid (He’03) SeRLoc (Lazos 04) Gradient algorithm (Nagpal ‘03) APS – AdHoc Positioning System (Niculescu ‘03) Five papers!!! Check them out for more details

Anchor based localization Centroid Algorithm APIT SeRLoc Gradient algorithm APS – AdHoc Positioning System Five papers !!!

Centroid Algorithm (1) Anchor nodes (Reference nodes) With known locations Overlapping regions of coverage

Centroid Algorithm (2) Connectivity based approach Anchors broadcast their position periodically Packets with sequence numbers The node Nk selects the closest anchors Nk

Centroid Algorithm (2) Choose the anchors with CM > 90% The node Nk localizes itself to the centroid Nk

Anchor based localization Centroid Algorithm APIT SeRLoc Gradient algorithm APS – AdHoc Positioning System Five papers !!!

APIT Localization An area-based approach: APIT Localization Scheme Approximated Point-In-Triangle Test. Anchor nodes divide sensor field into triangular regions. Given whether a node N locates inside or outside particular triangle Formalize the problem

Perfect P.I.T Theory If there exists a direction in which M is departure from points A, B, and C simultaneously, then M is outside of ∆ABC. Otherwise, M is inside ∆ABC. Require approximation for practical use Nodes can’t move, how to recognize direction of departure Exhaustive test on all directions is impractical

Departure Test Recognize directions of departure via neighbor exchange Receiving Signal Strength Comparison (Monotonic properties) Experimental Result from Berkeley Experiment Result from UVA

A.P.I.T. Test Approximation: Test only directions towards neighbors Error in individual test exists , however is relatively small and can be masked by APIT aggregation. APIT(A,B,C,M) = IN APIT(A,B,C,M) = OUT

APIT Aggregation Aggregation provides a good accuracy, even results by individual tests are coarse and error prone. High Possibility area Grid-Based Aggregation With a density 10 nodes/circle, Average 92% A.P.I.T Test is correct Average 8% A.P.I.T Test is wrong Low possibility area Localization Simulation example

Anchor based localization Centroid Algorithm APIT SeRLoc Gradient algorithm APS – AdHoc Positioning System Five papers !!!

SeRLoc (1) Secure Range Independent Localization Anchor nodes are equipped with directional sectored antenna Normal nodes have Omni-directional antennas Anchors transmit beacons within a sector Each beacon contains anchor location and angles of sectors

SeRLoc (2) Region of Intersection Anchor Sensor θ3,2 A4 θ3,1 A3 A1 s Coordinates Slopes A1 (X1, Y1) [θ1,1, θ1,2] A2 (X2, Y2) [θ2,1, θ2,2] A3 (X3, Y3) [θ3,1, θ3,2] A4 (X4, Y4) [θ4,1, θ4,2] s A3 (0, 0)

SeRLoc (3) A node collects beacons and determines a search area (rectangular) Computes the overlapping region using a majority vote. Finds center of gravity

Sensor places a grid of equally spaced points into the search area. SeRLoc (4) Anchors Sensor Search Area (Xmin+R, Ymax-R) (Xmin+R, Ymin+R) (Xmax-R, Ymin+R) (Xmax-R, Ymax-R) 2R+Xmin - Xmax Sensor places a grid of equally spaced points into the search area. A4 Anchors heard by the sensor (X4, Y4) (X1, Y1) (X2, Y2) (X3, Y3) R R Define: Xmin = min { Xi i  AHs } Ymin = min { Yi i  AHs } Xmax = max { Yi i  AHs } Ymax = max { Yi i  AHs } XA1 yA2 A3 A1 XA3 s yA4 A2

SeRLoc (5) ROI Sensor Search Area 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 … 1 1 1 2 3 3 3 3 4 4 4 3 3 3 3 3 3 1 1 2 2 2 3 4 4 4 4 4 4 4 3 3 2 2 1 1 2 2 4 4 4 4 4 4 4 4 4 4 3 3 2 2 2 2 2 3 4 4 4 4 4 4 4 4 3 2 2 2 2 2 3 3 3 3 4 4 4 4 4 4 3 3 2 2 2 2 2 2 3 3 3 3 4 4 4 4 3 3 2 2 2 2 1 2 2 2 3 3 3 3 4 4 3 2 2 2 3 4 3 2 2 2 3 3 3 3 3 2 2 2 2 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 ROI

Anchor based localization Centroid Algorithm APIT SeRLoc Gradient algorithm APS – AdHoc Positioning System Five papers !!!

Gradient Algorithm (1) Step 1: Distance Estimation The anchor nodes broadcast their position first counts the hops to anchor then estimate its distance from the anchor Step 2: Multilateration With known distances from three anchors, a node can use multilateration to estimate its own coordinates

Step 1: Distance Measurement (1) Each anchor broadcasts a packet (gradient) with location and sequence number. (x1, y1) Anchors (x1, y1) 2 Sensor (x1, y1) 1

Step 1: Distance Measurement (2) A distance estimate is obtained from hop count and average per hop distance = estimated Euclidian distance (Kleinrock-Silvester formula)

Step 2: Multilateration (1) How to find self position knowing the distances from three anchors?

Step 2: Multilateration (2) Ideally the desired coordinates are located at the point of intersection of three circles How to solve especially with noisy measurements? Gradient Descent Iterative Solution True location Initial estimation

Multilateration (3) Gradient descent iterative solution Start with initial guess Compute the distances based on Define a measure of error Update based on error gradient

Gradient Algorithm (3) With distances from three anchors a node can find its own location by iteratively solving the equations (multilateration with gradient descent)

Anchor based localization Centroid Algorithm APIT SeRLoc Gradient algorithm APS – Ad-Hoc Positioning System

APS Algorithm (1) Ad-Hoc Positioning System Similar to Gradient algorithm Difference lies in estimating one-hop Euclidean distance In APS, an anchor node Ai uses correction factor to estimate one-hop Euclidean distance

APS Algorithm (2) Example of correction factor 30 + 60 = 12.9m 3 + 4

APS Algorithm (3) With correction factor, each node estimates the distance Use least square to solve the multilateration equations

If you decide to finish a project on localization What are the key issues you need to consider before hand?

Discussion: Issues to consider Cost (extra HW) Degree of accuracy needed Indoors/outdoors Line of sight or not 2D-3D Efficiency (Energy budget) (Number of messages) Clock synchronization accuracy Error Assumptions Security attacks

Summary Taxonomy: Range-based and Range-free Range-free solutions localize sensor nodes without expensive localization hardware. Proximity-based Solution: Centroid Area-based Solutions: APIT and SerLoc Hop-count-based solutions: Gradient and APS

MDS Algorithm (1) Multidimensional Scaling (MDS) Conventional data analysis technique Idea: To model data in 2-D or 3-D based on similarity or dissimilarity (scaling) For localization distance matrix coordinate matrix 1 2 3 1 1 2 2 3 3

MDS Algorithm (2) How to get C from D ? Example

MDS Algorithm (2) Double centering (subtracting column and row mean) Compute the singular value decomposition (SVD) of Keeping the largest singular values