近幾年隨著無所不在的服務需求之增加，於手機網路上之適地性服務(Location-Based Services, LBS)的定位技術相關研究日趨重要。其中一個著名的LBS之應用為行動廣告(Mobile Advertisement)。行動廣告系統帶給使用者(users)，服務提供者(service provider)和廣告主(advertiser)諸多利益和發展機會。為了解決在某些情況下GPS無法定位使用者位置的問題，本篇碩士論文提出用於手機網路上之訊號感知樣式比對定位技術(Signal-Aware FingerPrinting-based Positioning Technique, SAFPPT)。SAFPPT包含四個定位方法：(1) 線之定位方法(Positioning Method of Line, PMoL)、(2) 面之定位方法(Positioning Method of Plane, PMoP)、(3) 接近點之感測方法(Approaching Detection Method of Point, ADMoP)、(4) 接近線之感測方法(Approaching Detection Method of Line, ADMoL)。這些定位方法採用樣式比對為主的定位技術，此技術藉由找尋user與指紋資料庫(fingerprinting database)間資料的關聯性來達到定位user位置的目的。本碩士論文提出的定位方法可以應用在不同的情境下，這些情境可分為(1) 點(POIs, point)、(2) 線(road, line)、(3) 面(region, plane)三種。這些定位方法可以達到兩個主要的目標：(1) 推估user的位置、(2) 找尋user現在正接近的POIs。此外，本篇碩士論文提出一個以旅行為主之行動廣告系統─「個人化情境感知行動廣告系統」(Personalized-Context-Aware Mobile Advertisement System, PCA-MAS)。PCA-MAS藉由ADMoP定位方法找到user正在接近的POIs，再透過情境感知廣告定向方法(Context-Aware AD Targeting Method, CAADTM)傳送這些POI間適合user的廣告到user的手機。本篇碩士論文之實驗結果呈現出：(1) 本論文提出定位方法PMoL和PMoP之精確度高於Google的「My Location」、(2) 某些影響PMoL之精確度的參數，例如：user的移動速度和fingerprinting database資料採集的次數、(3) user在一個定點停留的時間會影響PMoP之定位精確度、(4) ADMoP和ADMoL在150公尺半徑的targeted range內具有可接受的命中率(hit rate)去找到user正在接近的POIs、(5) CAADTM透過favorite content table filtering、annoying content table filtering和advertisement clicking feedback具有不錯的hit rate找到user喜愛的廣告。

With the growing demands of ubiquitous services, research of the positioning issue for location-based services (LBS) using cellular networks becomes more important in recent years. One famous application of LBS is the mobile advertisement. A mobile advertisement system brings the benefits and opportunities among users, service providers, and advertisers. To solve the problem of inability of GPS positioning, this thesis proposes Signal-Aware FingerPrinting-based Positioning Technique (SAFPPT) using cellular networks. SAFPPT contains four positioning methods: (1) Positioning Method of Line (PMoL), (2) Positioning Method of Plane (PMoP), (3) Approaching Detection Method of Point (ADMoP), and (4) Approaching Detection Method of Line (ADMoL). These four methods are based on the idea of the fingerprinting-based technique, which maps some user’s information and fingerprint records of the fingerprinting database to achieve the purpose of positioning. The proposed positioning methods in SAFPPT can be used in different scenarios of the interested area: (1) POIs (point), (2) road (line), and (3) region (plane). Two main goals of the proposed positioning methods in SAFPPT are (1) estimating a user’s location and (2) determining the corresponding POIs that a user is approaching. In addition, we develop one application of the traveling-centric mobile advertisement system called Personalized-Context-Aware Mobile Advertisement System (PCA-MAS). The proposed Context-Aware AD Targeting Method (CAADTM) of PCA-MAS is to deliver appropriate advertisements of some POIs to users, in which those POIs are found by ADMoP. Our experimental results show that (1) the positioning accuracy of PMoL and PMoP are higher than Google’s “My Location”, (2) some parameters may affect the positioning accuracy of PMoL, e.g., the moving speed of a user and the number of sampling of the fingerprinting database, (3) the staying time length of a user may affect the positioning accuracy of PMoP, (4) ADMoP and ADMoL have great hit rates to determine the corresponding POIs that a user is approaching within the 150-meter radius of the approaching range, (5) CAADTM has the good hit rate of finding appropriate advertisements that a user prefers through the favorite content table filtering, the annoying content table filtering and the advertisement clicking feedback.

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