dynamic fine-grained localization in ad-hoc networks of sensors weikuan yu dept. of computer and...

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

Radio Characteristics

Received Signal StrengthSusceptible to environmental changesshadowing, fading and even altitudeNo consistent model for some factorsRestriction: 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

Estimation Algorithms

Atomic MultilaterationBasic Formula

Weighted Combination

Iterative Multilateraion

Accuracy of Iterative Multilateration

Enhanced Iterative Multilateration

Collaborative 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