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研究生: 楊智焙
Yang, Chih-Pei
論文名稱: 利用可預測使用者路徑輔助之非衛星定位系統
Destination-Aware Non-GPS Positioning using Predictive Contexts
指導教授: 金仲達
口試委員: 彭文志
徐正炘
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 29
中文關鍵詞: 定位
外文關鍵詞: positioning
相關次數: 點閱:1下載:0
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  • 根據現行的手機,越來越多的基於位置的服務(LBSS)介紹,他們越來越多地影響著人們的日常生活。Glympse作為一個例子。將社交網絡與現實世界中的位置通知用戶其附近的朋友。為了得到位置,一般在這些應用中,全球定位系統正在使用。然而,GPS的能量消耗顯著由幾個以前的研究報告[19] [12]。許多研究人員然後再打的非GPS或GPS的定位策略,但在定位精度的費用。是否有一種方法來保持低功耗定位系統的精度,同時保持?
    本論文的關鍵洞察力的精度的非GPS或更小的GPS定位系統,可以補償的地圖,這在今天的智能手機更是一應俱全。只要用戶的位置,可以在所要求的精度之內的地圖上標記,將是沒有必要使用任何定位傳感器。這需要知道的起始位置,目的地,以及用戶的動作。基於這一思路,我們建議在這篇論文中目標的非GPS定位系統(DNPS)。 DNPS的起點,適用於低功耗的傳感器,如手機信號塔的ID和加速度計,考慮對他/她的目的地在地圖上定位用戶。為了弄清楚用戶的目的地,DNPS利用隱馬爾可夫模型的用戶上下文關聯與他/她的日常運動行為。我們的實驗表明,基於真實用戶相比,GPS和non-GPS/less-GPS系統,DNPS是能夠降低功耗,同時保持定位精度的。


    With the prevailing of mobile phones, more and more location-based services (LBSs) are introduced and they increasingly influence people’s daily life. Take Glympse as an example. It associates the social network with the real world by informing users of the positions of their nearby friends. To obtain positions, generally in these applications, GPS is used. However, the energy consumption of GPS is significant as reported by several previous studies [19] [12]. Many researchers then resort to non-GPS or GPS-less positioning strategies, but at the expenses of positioning precision. Is there a way to keep the power consumption of a positioning system low while maintaining the precision?
    The key insight of this thesis is that the precision of a non-GPS or less-GPS positioning system can be compensated by a map, which is readily available in today’s smart phones. As long as the position of a user can be marked on the map within the required precision, there will be no need to use any positioning sensor. This entails knowing the starting position, the destination, and the movements of the user. Based on this idea, we propose in this thesis the Destination-aware Non- GPS Positioning System (DNPS). Given a starting point, DNPS applies low-power sensors, such as cell tower id and accelerometers, to position the user on the map with consideration about his/her destination. To figure out the destination of the user, DNPS takes advantage of a Hidden Markov Model to associate the user contexts with his/her daily moving behavior. Our experiments based on real users show that, comparing to GPS and non-GPS/less-GPS systems, DNPS is able to reduce the power consumption while maintain the positioning precision.

    Abstract i Contents i Acknowledgments v 1 Introduction 1 2 Motivation 4 2.1 LocationTransitionofHumanBehavior ........................ 5 2.2 User’sContexts..................................... 5 3 DNPS Architecture 7 3.1 DestinationPrediction ................................. 7 3.1.1 Hot-spotDetection ............................... 7 3.1.2 HMMModelConstruction........................... 9 3.2 Destination-AwarePositioning ............................. 9 3.2.1 DestinationFetching .............................. 10 3.2.2 PathReorganization .............................. 10 3.2.3 LocationEstimation .............................. 12 4 DNPS Implementation 14 4.1 SystemArchitecture................................... 14 4.2 LocationManagement ................................. 16 5 Evaluation 17 5.1 EvaluationSetting.................................... 17 i 5.2 AccuracyofLocationPrediction ............................ 17 5.3 Energy Saving and Positioning Precision Comparison with Periodic-GPS . . . . . . 18 5.4 SensitivityAnalysisofParameters ........................... 20 6 Discussion 7 Related Work 8 Conclusion 23 24 26

    [1] Glympse, http://glympse.com
    [2] Cell-ID location technique, limits and benefits: an experimental study. WMCSA ’04. Emiliano Trevisani, and Andrea Vitaletti.
    [3] WiFi-based positioning.
    [4] Accuracy of iphone locations: A comparison of assisted gps, wifi and cellular positioning. GIS
    ’09. Paul A Zandbergen.
    [5] MetroSense Project: People-Centric Sensing at Scale. SenSys ’06. Shane B. Eisenman, Nicholas
    D. Lane, Emiliano Miluzzo, Ronald A. Peterson, Gahng-Seop Ahn, and Andrew T. Campbell.
    [6] Place-Its: A Study of Location-Based Reminders on Mobile Phones. UbiComp ’05. Timothy Sohn1, Kevin A. Li1, Gunny Lee1, Ian Smith2, James Scott3, and William G. Griswold1.
    [7] PeopleNet: engineering a wireless virtual social network. MobiComp ’05. Mehul Motani, Vikram Srinivasan, and Pavan S. Nuggehalli.
    [8] MyExperience:ASystemforInsituTracingandCapturingofUserFeedbackonMobilePhones. MobiSys ’07. Jon Froehlich, Mike Y. Chen, Sunny Consolvo, Beverly Harrison, and James A. Landay.
    [9] RADAR: an in-building RF-based user location and tracking system. INFOCOM ’00, vol. 2, pp. 775-784, Tel Aviv.Israel. P. Bahl and V. N. Padmanabhan.
    [10] Signal strength based indoor geolocation. ICC ’02, vol. 1, pp. 436-439. Y. Chen and H. Kobayashi.
    27
    [11] Energy-Efficient Rate-Adaptive GPS-based Positioning for Smartphones. MobiSys ’10. Jeongyeup Paek, Joongheon Kim, and Ramesh Govindan.
    [12] Improving Energy Efficiency of Location Sensing on Smartphones. MobiSys ’10. Zhenyun Zhuang, Kyu-Han Kim, and Jatinder Pai Singh.
    [13] Tracking a suspect by any mobile phone: Tracking SIM and handset. BBC News. 2005-08-03. Retrieved 2010-01-02.
    [14] Transforming the social networking experience with sensing presence from mobile phones. In proceeding of SenSys ’08. Andrew T. Campbell, Shane B. Eisenman, Kristif Fodor, Nicholas D. Lane, Hong Lu, Emiliano Miluzzo, Mirco Musolesi, Ronald A. Peterson, and Xiao Zheng.
    [15] Multi-sensor context-awareness in mobile devices and smart artifacts. Published in Published in Mobile Networks and Applications archive, Volume 7 Issue 5, October 2002. Hans W. Gellersen, Albercht Schmidt, and Michael Beigl.
    [16] SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments. In proceeding of MobiSys ’08. Seungwoo Kang KAIST, Jinwon Lee, Hyukjae Jang, Hyonik Lee KAIST, Youngki Lee, Souneil Park, Taiwoo Park, and Junehwa Song.
    [17] Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In proceeding of SenSys ’08. Emiliano Miluzzo, Nicholas D. Lane, Kristof Fodor, Ronald Peterson, Hong Lu, Mirco Musolesi, Shane B. Eisenman, Xiao Zheng, and An- drew T. Campbell.
    [18] Aframeworkofenergyefficientmobilesensingforautomaticuserstaterecognition.inproceed- ing of MobiSys ’09. Yi Wang, Jialiu Lin, Murali Annavaram, Quinn A. Jacobson, Jason Hong, Bhaskar Krishnamachari, and Norman Sadeh.
    [19] Less is more: energy-efficient mobile sensing with senseless. In proceeding of MobiHeld ’09. F Ben Abdesslem.
    [20] EnLoc: Energy-Efficient Localization for Mobile Phones. In proceeding of INFOCOM ’09. Ionut Constandache, Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury, and Landon Cox.
    28
    [21] Energy-accuracy trade-off for continuous mobile device location. In proceeding of MobiSys ’10 . Kaisen Lin, Aman Kansal, Dimitrios Lymberopoulos, and Feng Zhao.
    [22] Energy-Efficient Positioning for Smartphones using Cell-ID Sequence Matching. In proceeding of MobiSys ’11. Jeongyeup Paek, Kyu-Han Kim, Jatinder P. Singh, and Ramesh Govindan.
    [23] Optimalestimationofpositionandheadingformobilerobotsusingultrasonicbeaconsanddead- reckoning. In proceeding of the 1992 IEEE International Conference on Robotics and Automa- tion, pp 2582-2587.. Lindsay Kleeman, Lindsay Kleeman.
    [24] TheMapMatchingAlgorithmofGPSDatawithRelativelyLongPollingTimeIntervals.Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 2561 - 2573, 2005. Jae-seok YANG, Seung-pil KANG, and Kyung-soo CHON.
    [25] Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems. A. Bouk- erche et al., Vehicular Ad Hoc Networks: A New Challenge for ..., Comput. Commun. (2008), doi:10.1016/j.comcom.2007.12.004. Azzedine Boukerche, Horacio A.B.F. Oliveira, Eduardo F. Nakamura, and Antonio A.F. Loureiro.
    [26] Place lab: A privacy-observant location system.
    [27] Statistical Inference for Probabilistic Functions of Finite State Markov Chains. The Annals of Mathematical Statistics 37 (6): 1554âA ̆S ̧1563. doi:10.1214/aoms/1177699147. Retrieved 28 November 2011. Baum, L. E., and Petrie, T.
    [28] Indoor Localization Using a Context-Aware Dynamic Position Tracking Model. International Journal of Navigation and Observation Volume 2012, Article ID 293048, 12 pages. Montserrat Ros,JoshuaBoom,GavindeHosson,andMatthewDâA ̆Z ́Souza.
    [29] Exploring Spatial-Temporal Trajectory Model for Location Prediction. Mobile Data Manage- ment (MDM), 2011 12th IEEE International Conference. Po-Ruey Lei, Tsu-Jou Shen, Wen-Chih Peng, Ing-Jiunn Su.

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