近年來科技發展迅速，全球先進城市致力於開發智慧社區的相關應用技術，讓生活更加的便利。各國政府也積極整理各項公開資料並發布在網路上，以鼓勵民間以及企業運用政府開放資料來創造新的知識資產和便民服務。此外，人們所持有的智慧型手機幾乎都具備有網路網際以及無線感應的功能，可使用智慧社區的服務系統應用，構成了全新的社區生活型態。
本研究以信標感應和政府開放資料來發展智慧社區之應用，本系統包含了三個子系統，公共設施導覽系統、接近推播系統、住宅評估系統，來提供智慧社區的應用。第一，公共設施導覽子系統使用了GPS全球定位技術結合政府公開資料，以Google Map當作基底，在電子地圖上顯示出台北市的教育型機構，進修大學、圖書館、運動中心等三種教育型機構，可以讓使用者立即的定位並知道周遭有哪些服務。第二，接近推播子系統，應用了iBeacon技術，當行人經過教育型機構或是社區商家時，使廣告、宣傳推播至使用者手機中，可以取代雜亂的招牌或是廣告傳單。此子系統也提供了社區商家計算人流的功能，以便於商家研究出使用者的消費習慣以及最佳的行銷推廣。第三，住宅評估子系統，使用內政部實價登陸的房地產資料結合台北市開放資料的教育型機構，讓各筆房地產有一個住宅方便性的分數，並透過使用者的需求來調整權重，以便使用者在挑選住宅時有更多、更方便的考量。上述三個子系統可以整合來創造出更強的服務應用如：活動舉辦時的宣傳資料以及地圖指引、選購商圈附近住宅時的人流考量、以及因應人流的廣告效益。此研究讓使用者利用智慧型手機與社區連結，創造出更便利的社區環境。

In recent years, the development of technologies has enforced the world's advanced cities to develop intelligent community-related applications for their citizens. Governments also actively organize their public information and publish them on the Internet to encourage civil and business use these open materials to create new intelligent services. In addition, citizens’ smart phones almost all have access to Internet and many sensor functions, which can be used to support the intelligent community applications in their daily life.
This study includes the application of beacon sensors and government open information to develop intelligent community services. The system consists of three subsystems to provide the application of intelligent community: public facilities subsystem, information push subsystem, and residential evaluation subsystem. First, the public facilities subsystem uses the GPS global positioning technology combined with government information on the electronic map to show the Taipei City’s various educational institutions, universities, libraries, and sports centers. This subsystem allows users to immediately locate and know what services around. Second, information push subsystem using beacon technology to push information when pedestrians walk through educational institutions or community businesses. This subsystem allows advertisings and promotion events to be pushed to the user's mobile phone, so that the messy signs or advertising flyers can be reduced. This subsystem also provides a community store to calculate the man-flow and customer habits statistics for marketing promotion. Third, the residential assessment subsystem uses real estate open data from the Ministry of Interior combined with Taipei City’s educational institutions open data. This subsystem uses a residential convenience score which is derived by user’s weight preferences, so that users have more convenient considerations choosing their homes. The three subsystems can be integrated to create more services such as event promotion and map guidelines, the statistics of the man-flow in the vicinity of the shopping district, and the study of the advertising effectiveness of the man-flow. This study allows users to use smartphones to connect with the smart community to create a more convenient community environment.

[1]. Immonen, Anne, Marko Palviainen, and Eila Ovaska. "Requirements of an open data based business ecosystem." IEEE Access 2 (2014): 88-103.
[2]. Fukuta, Naoki. "Toward a Linked-Open-Data-Based Architecture for an Open Consensus Building Support." Agents (ICA), IEEE International Conference on. IEEE, 2016.
[3]. Srimuang, Chatipot, et al. "Open government data assessment model: An indicator development in Thailand." Advanced Communication Technology (ICACT), 2017 19th International Conference on. IEEE, 2017.
[4]. Ang, Li-Minn, et al. "Big Sensor Data Systems for Smart Cities." IEEE Internet of Things Journal ,2017.
[5]. Mohamed, Nader, Sanja Lazarova-Molnar, and Jameela Al-Jaroodi. "Cloud of Things: Optimizing smart city services." Modeling, Simulation, and Applied Optimization (ICMSAO), 2017 7th International Conference on. IEEE, 2017.
[6]. Islam, Md Mofijul, et al. "Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities." IEEE Access ,2017.
[7]. Sumi, Lucy, and Virender Ranga. "Sensor enabled Internet of Things for smart cities." Parallel, Distributed and Grid Computing (PDGC), 2016 Fourth International Conference on. IEEE, 2016.
[8]. Kim, Eungha. "Smart city service platform associated with smart home." Information Networking (ICOIN), 2017 International Conference on. IEEE, 2017.
[9]. Gunawan, Dani, and Amalia Amalia. "The Implementation of open data in Indonesia." Data and Software Engineering (ICoDSE), 2016 International Conference on. IEEE, 2016.
[10]. Koühne, Markus, and Jürgen Sieck. "Location-based services with iBeacon technology." Artificial Intelligence, Modelling and Simulation (AIMS), 2014 2nd International Conference on. IEEE, 2014.
[11]. Yu, Liyang. "Linked open data." A Developer’s Guide to the Semantic Web. Springer Berlin Heidelberg, 2011. 409-466.
[12]. Bizer, Christian, et al. "Linked data on the web (LDOW2008)." Proceedings of the 17th international conference on World Wide Web. ACM, 2008.
[13]. Shadbolt, Nigel, et al. "Linked open government data: Lessons from data. gov. uk." IEEE Intelligent Systems 27.3 (2012): 16-24.
[14]. Hendler, James, et al. "US government linked open data: semantic. data. gov." IEEE Intelligent Systems 27.3 (2012): 25-31.
[15]. Shadbolt, Nigel, et al. "Linked open government data: Lessons from data. gov. uk." IEEE Intelligent Systems 27.3 (2012): 16-24.