Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters

位置感知服務可藉由準確及可靠的室內定位系統獲得幫助。現今普遍設置的802.11x無線網路架構使得利用此無線網路作為定位的服務僅需要極小的額外花費。雖然近來各項研究已發表出準確性及效能令人滿意的結果，但事實上，當人群擁擠或移動時，定位系統的誤差卻會急遽地增加。這篇論文提出了合作定位的方式，利用在同一人群中，鄰近且定位較精準的其他使用者的資訊，來增進定位系統的效能。我們利用了 Zigbee 無線電來偵測鄰近的使用者。這篇論文介紹了合作定位的基本模型及演算法。我們在各種不同形式所組成的人群中進行實驗。實驗結果表示，在將我們的方法套用至一個商業的定位系統 Ekahau 之後，使得定位系統的準確性增加了28.2% 到 56% 的效能。Location-aware services can benefit from accurate and reliable indoor location tracking. The widespread adoption of 802.11x wireless LAN as the network infrastructure creates the opportunity to deploy WiFi-based location services with few additional hardware costs. While recent research has demonstrated adequate performance, localization error increases significantly in crowded and dynamic situations due to electromagnetic interferences. This paper proposes collaborative localization as an approach to enhance position estimation by leveraging more accurate location information from nearby neighbors within the same cluster. The current implementation utilizes ZigBee radio as the neighbor-detection sensor. This paper introduces the basic model and algorithm for collaborative localization. We also report experiments to evaluate its performance under a variety of clustering scenarios. Our results have shown 28.2-56% accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system.Chapter 1 Introduction 1 Chapter 2 Clustering 3 Chapter 3 Design And Implementation 6 3.1 Neighborhood Detection 7 3.2 Confidence Estimation 8 3.3 Collaborative Error Correction 10 Chapter 4 Experimental Results 12 4.1 Neighborhood Sensing 12 4.2 Performance Evaluation 13 4.2.1 Stationary Clusters (Scenario I) 14 4.2.2 Mobile Cluster (Scenario II) 16 4.2.3 Impact of Space Geometry 18 4.3 Evaluation of Confidence Estimator 19 Chapter 5 Related Work 21 Chapter 6 Conclusion and Future Work 2

Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption

Collaborative Localization -- Enhancing WiFi-Based Position Estimation with Neighborhood

Abstract. Location-aware services can benefit from accurate and reliable indoor location tracking. The widespread adoption of 802.11x wireless LAN as the network infrastructure creates the opportunity to deploy WiFi-based location services with few additional hardware costs. While recent research has demonstrated adequate performance, localization error increases significantly in crowded and dynamic situations due to electromagnetic interferences. This paper proposes collaborative localization as an approach to enhance position estimation by leveraging more accurate location information from nearby neighbors within the same cluster. The current implementation utilizes ZigBee radio as the neighbor-detection sensor. This paper introduces the basic model and algorithm for collaborative localization. We also report experiments to evaluate its performance under a variety of clustering scenarios. Our results have shown 23.4-56.4 % accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system.

Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics

Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positioning accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems, instability in positioning accuracy due to changing environmental dynamics, and the need for manual offline calibration during site survey. To address these two challenges, three environmental factors (people, doors, and humidity) that can interfere with radio signals and cause positioning inaccuracy are identified. Then, we have proposed a sensor-assisted adaptation method that employs RFID sensors and environment sensors to adapt the location systems automatically to the changing environmental dynamics. The proposed adaptation method performs online calibration to build multiple contextaware radio maps under various environmental conditions. Experiments were performed on the sensor-assisted adaptation method. The experimental results show that the proposed adaptive method can avoid adverse reduction in positioning accuracy under changing environmental dynamics