Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors Weikuan Yu Dept. of Computer and Info. Sci. The Ohio State University

Presentation Outline Problem Statement General Ideas and Related Work Current System at Study  Goals aimed  Processing Steps  Algorithms  Critical Factors Node and beacon placement Traffic and energy consumption Conclusion

Problem Statement Wireless sensors network widespread deployed  signal sensing,  emergence detection  ground vibration Location awareness is indispensable  Immediate information transmission  Quick routing of query  Tracking of objects

Problem Statement Problems with GPS  Not work indoors  High power consumption, short lifetime  High cost

General Ideas and Related Work Localization Basics  Ranging RSSI ToA, TDoA AoA  Estimation

Related Work RADAR  Use RF signals to track indoor objects  Offline and online phases  High cost Cricket location support  Low cost for location awareness  Use Ultrasound singals  4 x 4 feet granularity BAT  Centralize configuration  Granularity at centimeters level Both Cricket and BAT are infrastructures-based networks

ADLOS (Ad-Hoc Localization System) Goals  Ad-Hoc Sensor Network (Dynamic network)  Fine granularity  Low cost  Distributed location awareness Processing Phases  Ranging  Estimation

Ranging Characterization Received Signal Strength  Susceptible to environmental changes, e.g., shadowing, fading and even altitude

Radio Characteristics  Received Signal Strength  Susceptible to environmental changes  shadowing, fading and even altitude  No consistent model for some factors  Restriction: all nodes are at ground level  r: distance, X and n are constants  WINS nodes

WINS node RSSI characterization

ToA using RF and Ultrasound

Ultrasound Ranging characterization

Signal Strength and ToA Ranging ToA is more robust and fine-grained Susceptible to environmental changes Consider the combination of ToA and RF

Estimation Algorithms

 Atomic Multilateration  Basic Formula  Weighted Combination

Iterative Multilateraion

Accuracy of Iterative Multilateration

Enhanced Iterative Multilateration

Collaborative Multilateration

Node and Beacon Placement Connectivity of a node Probability of having a connected node

Number of nodes per unit area, lamda

Distribution of Connectivity Results

Required Beacon Nodes

Power Chacterization

Power consumption at different operational modes

Traffic with different implementation

Energy with different implementation

Conclusion A new localization system scheme for Ad-Hoc wireless sensor networks  Distributed, low cost  Fine-grained ToA ranging is better; hybrid can be even better Distributed is advocated for estimation  Less energy  Less traffic  Although less accurate