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Self-configurable Indoor Localization Using Non-intrusive Distance Measurements

Self-configurable Indoor Localization Using Non-intrusive Distance Measurements
Translated Title
비간섭 거리 측정을 이용한 자율 구성 가능한 실내 측위 기법
Yoon, Jong Wan
DGIST Authors
Yoon, Jong Wan; Son, Sang Hyuk; Le, Jong Hun
Son, Sang Hyuk
Le, Jong Hun
Issue Date
Available Date
Degree Date
2016. 8
Indoor localizationSelf-configurationInter-sensor distance estimationNon-intrusive distance measurementSensor grouping실내추위자율 구성센서간 거리 추정비간선 거리측정센서 그룹화
In indoor localization, it is crucial to guarantee a high level of accuracy for various location-based services. An ultrasonic technique is one of the best candidates to meet this need because it is capable of performing precise distance measurements as well as enabling non-intrusive localization that requires no receiver to be carried. Nevertheless, its applicability is severely limited by the fact that ultrasonic waves are likely to collide with one another if a multiple access scheme is not equipped, as is usually the case for low-cost ultrasonic sensors. Also, environmental changes such as addition/removal of obstacles or dislocation of sensors themselves may further degrade the localization performance. In addition, the target tracking relies on sensors with known locations to estimate and keep track of the path taken by the target, and hence, it is crucial to have an accurate map of such sensors. However, the need for manually entering their locations after deployment and expecting them to remain fixed, significantly limits the usability of target tracking. So, precise location estimation of deployed sensors is essential, but many disturbances such as obstacles in indoors need to consider when determine the sensor location. In order to overcome aforementioned limitations of the ultrasonic distance measurement sensors, we introduce a genetic approach-based self-configurable, device-free, and low-cost ultrasonic sensor grouping technique for indoor localization that precisely quantifies the degree of collisions by using kernel distance and forms an optimal number of sensing groups to maximize the spatial reuse as well as to detect environmental changes in real time. After that, we present a self-configuring and device-free localization protocol based on genetic algorithms that autonomously identifies the geographic topology of a network of ultrasonic range sensors as well as automatically detects any change in the established network structure in less than a minute and generates a new map within seconds. And then, we suggest a cost-effective, scalable, asynchronous solution to estimate inter-sensor distances based solely on measurements of distances to a moving object is proposed which can estimates uncharted distances using trigonometry and processes these estimated distances with a distributed weighted multi-dimensional scaling algorithm for more precise localization of sensors. To verify the performance of proposed techniques, we conduct comprehensive experiments on the real testbed to demonstrate that our techniques achieve a high level of accuracy using off-the-shelf ultrasonic sensors. ⓒ 2016 DGIST
Table Of Contents
1 Introduction 1-- 1.1 Background and Challenging Issues 1-- 1.2 Solution Approaches 3-- 1.3 Thesis Organization 6-- 2 Maximizing Localization Accuracy via Self-confi gurable Ultrasonic Sensor Grouping Using Genetic Approach 7-- 2.1 Introduction 7-- 2.2 Related Work 9-- 2.3 Problem Formulation 10-- 2.4 Quanti fication of Collision Relation 12-- 2.5 Ultrasonic Sensor Grouping 15-- 2.5.1 GA-based Grouping Algorithm 15-- 2.5.2 Detection of Environmental Changes 19-- 2.5.3 Choice of the Number of Groups 20-- 2.6 Simulation Results 21-- 2.6.1 E ffects of Moving Speeds and Sensing Intervals 21-- 2.6.2 E ffects of Deployment Density 22-- 2.7 Experiment Results 24-- 2.7.1 Verifi cation of Kernel Distance Metric 25-- 2.7.2 Comparison with Simulation Results 25-- 2.7.3 Comparison with Other Techniques 27-- 2.7.4 Detection of Environmental Changes 29-- 2.8 Conclusion 30-- 3 Self-con figuring Indoor Localization Based on Low-cost Ultrasonic Range Sensors 32-- 3.1 Introduction 32-- 3.2 Related Work 34-- 3.3 Sensor Hardware and Related Challenges 35-- 3.3.1 Sensor Hardware 35-- 3.3.2 Formation of Input Database 35-- 3.4 Self-Con figuring Localization for Landmarks 37-- 3.5 Target Tracking and Displacement Handling 43-- 3.6 Evaluation 45-- 3.7 Conclusion 50-- 4 Cost-eff ective, Asynchronous Inter-sensor Distance Estimation Using Trigonometry 51-- 4.1 Introduction 51-- 4.2 Trigonometry-based Inter-sensor Distance Estimation 54-- 4.3 IoT Device Localization 57-- 4.3.1 Self-localization through dwMDS 57-- 4.3.2 Error correction after localization 58-- 4.4 Simulation Results 59-- 4.4.1 Eff ects of Deployment Density 60-- 4.4.2 E ffects of Moving Speeds and Sensing Intervals 60-- 4.5 Experimental Results 64-- 4.6 Related Work 65-- 4.7 Conclusion 66-- 5 Conclusion and Future Work 68--
Information and Communication Engineering
Information and Communication EngineeringThesesPh.D.

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