Location Determination Technologies for Sensor Networks Moustafa Youssef University of Maryland at College Park UMBC Talk March, 2007
Motivation Location is important: Determining the location of an event Geographic-aware routing Node coverage Node ID Ubiquitous computing Problem: Given a set of sensor nodes, determine their locations Location Determination Technologies, 2006 Moustafa Youssef 2/50
Road Map Motivation Taxonomy Ranging technologies The PinPoint System Summary Location Determination Technologies, 2006 Moustafa Youssef 3/50
Taxonomy Features Use of anchor nodes anchor-based/anchor-free Range-estimation method range free, rang-based Range-combining technique triangulation, tri-lateration, multi-lateration, etc Computational-model centralized, distributed, clusters Location Determination Technologies, 2006 Moustafa Youssef 4/50
Anchor-based Algorithms Initial nodes with known position Anchors Unknown Nodes Location Determination Technologies, 2006 Moustafa Youssef 5/50
Anchor-free Algorithms Uses connectivity information Gives relative position Can be mapped to global position With the knowledge of three nodes position Location Determination Technologies, 2006 Moustafa Youssef 6/50
Anchor-free Algorithms GPS-free [GPS-Free] Fully distributed, uses distances to immediate neighbors. Each node builds a local coordinate system. Use law of cosines to calculate node position Build local map for 1-hop neighbors. All nodes communicate with each other to build a global map R 2 = (x 2, y 2 ) Location Determination Technologies, 2006 Moustafa Youssef 7/50 R 0 θ d 1 d 2 y 2 x 2 d 3 R 1
SALAM [Salam] Each gateway node has inter-node node distance matrix D for the nodes that are within K-hopsK X-axis Build a local map for the cluster Merge Clusters Y-axis R R 1 2 Gateway Node Sensor Node Reference Node Boundary Node R 1 R 2 Location Determination Technologies, 2006 Moustafa Youssef 8/50
Taxonomy Features Use of anchor nodes Range-estimation method range free, rang-based Range-combining technique Computational-model Location Determination Technologies, 2006 Moustafa Youssef 9/50
Range-Free Algorithms APS: DV-hop [DV-Hop] Use network connectivity to estimate ranges. Average Hop Distance (AHD) Known through anchors Distance= HC* AHD Anchors Unknown Nodes Location Determination Technologies, 2006 Moustafa Youssef 10/50
Range-Free Algorithms (Cont d) Approximate Point In Triangle Test (APIT) [APIT] Area-based algorithm Intersection of n C 3 triangles IN OUT Location Determination Technologies, 2006 Moustafa Youssef 11/50
Range-Free Algorithms (Cont d) if there exists a direction such that a point adjacent to M is further/closer to points A, B, and C simultaneously, then M is outside ABC otherwise, it s inside Location Determination Technologies, 2006 Moustafa Youssef 12/50
Range-Free Algorithms (Cont d) Approximate test. Check neighbor distance (Signal Strength) Assumption: from any anchor in one direction, the received signal strength is monotonically decreasing APIT(A,B,C,M) = IN APIT(A,B,C,M) = OUT Location Determination Technologies, 2006 Moustafa Youssef 13/50
Range-Free Algorithms (Cont d) Convex position estimation [Convex] If one node can communicate with another, a proximity constraint exists between them. For example, if particular RF system can transmit 20m and two nodes are in communication, their separation must be less than 20m. These constraints restrict the feasible set of unknown node positions This linear programming problem requires centralized computation. Location Determination Technologies, 2006 Moustafa Youssef 14/50
Range-based Algorithms Range estimation method Signal strength Time-Of-Arrival (ToA) Angle of Arrival (AoA) Location Determination Technologies, 2006 Moustafa Youssef 15/50
Taxonomy Features Use of anchor nodes Range-estimation method Range-combining technique triangulation, tri-lateration, multi-lateration, etc Computational-model Location Determination Technologies, 2006 Moustafa Youssef 16/50
Range Combining Technique B B B C A S No rth A S C A E S D Triangulation Trilateration Multilateration Collaborative Multilateration Location Determination Technologies, 2006 Moustafa Youssef 17/50
Taxonomy Features Use of anchor nodes Range-estimation method Range-combining technique Computational-model centralized, distributed, clusters Location Determination Technologies, 2006 Moustafa Youssef 18/50
Computational Model Centralized. Fully distributed. Locally centralized (clusterbased). Location Determination Technologies, 2006 Moustafa Youssef 19/50
Road Map Motivation Taxonomy Ranging technologies The PinPoint System Summary Location Determination Technologies, 2006 Moustafa Youssef 20/50
Existing Techniques for Location Determination GPS systems Cellular-based systems Infrared-based systems Ultrasonic systems Computer vision Physical proximity Magnetic Tracking PinPoint RF-based techniques Location Determination Technologies, 2006 Moustafa Youssef 21/50
GPS Systems TOA of the signal from 3 satellites 4th satellite needed for synchronization A-GPS Location Server used as a reference point Accuracy: 5 m to 50 m Assistance Information Location Determination Technologies, 2006 Moustafa Youssef 22/50
GPS Systems Advantages Global Coverage Accurate timing Privacy Disadvantages GPS: Requires LOS to satellites: does not work indoors Long acquisition time Power consumptions Cost Location Determination Technologies, 2006 Moustafa Youssef 23/50
Cellular Systems r A A B r B Base Station Base Station TOA/TDOA Measures time/time difference it takes for A a signal to arrive at 3 BSs AOA Measures angle of a wireless device with respect to 2 BSs Accuracy: 50 m to 150 m r C C Base Station C Base Station A Base Station θ A Base Station TDOA B-A B Base Station TDOA C-A θ B B Base Station Location Determination Technologies, 2006 Moustafa Youssef 24/50
Cellular Systems Advantages Asset already exists Disadvantages Multi-path effect TOA requires clock synchronization AOA requires antenna array Accuracy not suitable for indoors Cost Location Determination Technologies, 2006 Moustafa Youssef 25/50
Infrared-Based Systems Active Badge (AT&T) Badge worn by a person emits a unique IR signal IR receivers pick up signal and relay it to the location manager software Walls blocks IR signal: user can be identified accurately within a room Accuracy:Room Location Determination Technologies, 2006 Moustafa Youssef 26/50
Infrared-Based Systems Advantages Accuracy suitable for indoors Disadvantages Scales poorly due to limited range of IR Significant installation and maintenance cost Affected by sunlight and fluorescent light Privacy issues Location Determination Technologies, 2006 Moustafa Youssef 27/50
Ultrasonic Systems: Active Bat Active Bat (AT&T) A controller sends an RF request to the object Short pulse of ultrasound is emitted from a transmitter (a Bat) attached to the object in response Ceiling-mounted receivers measure the times-of-flight of the pulse Accuracy:9 cm (95%) Location Determination Technologies, 2006 Moustafa Youssef 28/50
Ultrasonic Systems: Active Bat Advantages Accuracy suitable for indoors Disadvantages Large fixed-sensor infrastructure required through ceiling System sensitive to precise placement of sensors Scalability, ease of deployment, and cost Privacy issues Location Determination Technologies, 2006 Moustafa Youssef 29/50
Ultrasonic Systems: Cricket Cricket Similar to Active Bat but computation performed at mobile unit (more private) Accuracy:4x4 feet Location Determination Technologies, 2006 Moustafa Youssef 30/50
Ultrasonic Systems: Cricket Advantages More privacy Decentralized control Disadvantages Computational/power burden Scalability, ease of deployment, and cost Location Determination Technologies, 2006 Moustafa Youssef 31/50
Computer Vision Easy Living (Microsoft) 3D cameras capture images Computer vision algorithms used to determine object positions Accuracy: variable Location Determination Technologies, 2006 Moustafa Youssef 32/50
Computer Vision Disadvantages Use substantial processing power to analyze captured frames Suffer from occlusion problems Cost Location Determination Technologies, 2006 Moustafa Youssef 33/50
Physical Proximity Smart ID readers Smart Floor (GATECH) Embedded pressure sensors capture footfalls System compares foot signature to users profiles Accuracy: spacing of pressure sensor Location Determination Technologies, 2006 Moustafa Youssef 34/50
Physical Proximity Advantages User not required to wear tags or carry devices Disadvantages Poor scalability High incremental cost Does not scale with large populations Location Determination Technologies, 2006 Moustafa Youssef 35/50
Magnetic Tracking Motion Star (Ascension) System generates axial DC magnetic field pulses System computes the position and orientation by measuring response in 3 orthogonal axes Combined with the fixed effect of the earth's magnetic field Accuracy: 1 mm Location Determination Technologies, 2006 Moustafa Youssef 36/50
Magnetic Tracking Advantages Very high precision Disadvantages Steep implementation costs Tracked object needs to be tethered to a control unit Sensors must remain within 1 to 3 meters of the transmitter Accuracy degrades with the presence of metallic objects Location Determination Technologies, 2006 Moustafa Youssef 37/50
RF-Based Systems RADAR Microsoft Ekahau Finland Research Projects UCLA CMU Horus PinPoint Location Determination Technologies, 2006 Moustafa Youssef 38/50
The Horus System Signal strength based Software only solution: Works with standard WiFi cards Offline phase Construct radio map Online phase Infer user location Better than 2 feet accuracy Location Determination Technologies, 2006 Moustafa Youssef 39/50
WLAN-Based Systems Advantages No extra hardware Good accuracy (3 feet) Disadvantages Multi-path effect Location Determination Technologies, 2006 Moustafa Youssef 40/50
The PinPoint System Time of arrival based Software only solution: Works with standard WiFi cards No calibration required No synchronized clocks required No handshaking required Efficient (constant number of messages per node to locate all other nodes Few feet accuracy R 1 R 2 Location Determination Technologies, 2006 Moustafa Youssef 41/50
Road Map Motivation Taxonomy Ranging technologies The PinPoint System Summary Location Determination Technologies, 2006 Moustafa Youssef 42/50
PinPoint Technology Location determination system for ad hoc networks 3 Phases Measurement phase Information Exchange phase Computation Phase Location Determination Technologies, 2006 Moustafa Youssef 43/50
PinPoint Technology Measurement phase Each node transmits a message containing its ID and the transmit timestamp of the message, and records the receive timestamp of the messages sent by other nodes Location Determination Technologies, 2006 Moustafa Youssef 44/50
PinPoint Technology τa1 τa2 τa3 τa4 ( A, τa 1) ( B, τb2) ( B, τb4) ( A, τa 3) τb1 τb2 τb3 τb4 First Cycle Second Cycle Location Determination Technologies, 2006 Moustafa Youssef 45/50
PinPoint Technology Information Exchange phase Each node transmits a message containing its receive timestamp for messages transmitted by other nodes in a measurement phase Location Determination Technologies, 2006 Moustafa Youssef 46/50
PinPoint Technology Computation Phase Each node computes the spatial coordinates and clock attributes of every other node Redundant information used to reduce errors No communication takes place Accuracy: few centimeters Synchronized clocks Location Determination Technologies, 2006 Moustafa Youssef 47/50
PinPoint Results 4-6 feet accuracy using 3 ns clock NIC based implementation Similar accuracy with 25 ns clock Location Determination Technologies, 2006 Moustafa Youssef 48/50
Summary Localization is an important problem in sensor networks Provided a taxonomy Described different localization technologies Described the PinPoint system Location Determination Technologies, 2006 Moustafa Youssef 49/50
To probe further www.mindlab.umd.edu www.cs.umd.edu/~moustafa/publications www.cs.umd.edu/~moustafa/downloads Location Determination Technologies, 2006 Moustafa Youssef 50/50
References [GPS-Free] Benbadis, F. et al, GPS-free-free positioning system for wireless sensor networks, Wireless and Optical Communications Networks, 2005. WOCN 2005. Second IFIP [Salam] Adel Youssef et al, Accurate Anchor-Free Node Localization in Wireless Sensor Networks, CS-TR-4614, Department of Computer Science, University of Maryland, College Park, July 2004. [DV-Hop] D. Niculescu and B. Nath, "Ad hoc positioning system (APS), GLOBECOM '01 [APIT] Tian He et al, Range-Free Localization Schemes for Large Scale Sensor Networks Mobicom 03 [Convex] Doherty, L. et al, Convex position estimation in wireless sensor networks, INFOCOM 2001 Location Determination Technologies, 2006 Moustafa Youssef 51/50