在各種不同的無線通訊網路的技術中，個人通訊服務網路(Personal communication service, PCS)、IEEE 802.11無線區域網路以及智慧家庭網路技術發展的非常快速，同時也有很多學者發表文獻在討論這些技術的應用。具備使用者位置感知服務(Location-based Service, LBS)的應用，如旅遊導覽、展場導覽、汽車導航、智慧家庭服務、個人位置追蹤…等服務，可提供使用者與位置感知相關聯的服務。為了解決在異質網路中提供使用者位置感知服務的問題，我們提出了一個整合性無線網路環境中的位置管理機制，該機制可以提供在3G通訊服務網路以及無線區域網路的架構中的使用者位置管理服務。
在PCS網路中，使用者位置管理是透過儲存所有使用者資訊的Home Location Register(HLR)來管理。當使用者位置改變時，每一個基地台的Mobile Switching Center(MSC)，便會透過該MSC上的Visitor Location Register(LVR)的使用者位置資訊，來改變與使用者的通訊連線以及更新使用者資訊，而這些不斷改變的使用者位置資訊，都會透過MSC定期的往HLR作資訊的更新，當位置管理機制如基地台Handover進行時，HLR很容易成為系統瓶頸。為了解決此問題我們提出了在PCS環境中，透過建置一個無線區域網路中的Local Area Location(LAL)機制，來加快尋找使用者位置的方法，如果使用者具備無線區域網路的連線能力，便可以馬上根據該無線網路存取點(Access Point, AP)的ID來判斷使用者的位置。而針對PCS網路架構下的位置管理我們提出了一個透過資料探勘(Data Mining)的方法來預測使用者的移動位置，根據使用者的移動歷史路徑、使用習慣、通訊連結時間…等因素，建立規則進而預測使用者的位置。
當使用者所移動的區域範圍均可以連結到無線區域網路時，可以根據WLAN中無線網路存取點(Access Point, AP)訊號的強弱(Received Signal Strength Indication, RSSI)，提供更準確的提供使用者位置資訊。透過實際測量無線訊號值RSSI的結果，發現到無線訊號的強弱值並非一直固定不變而是呈現常態分配。當環境中存在多個無線存取點(AP)的時候，便可以透過使用者與這些AP的訊號值，來推估使用者的位置，透過Expectation Maximum(EM)演算法建立一個無線訊號的高斯混合模型(Gaussian mixture model, GMM)，當一個新的使用者進入此區域中便可根據先前所建立的GMM模型來計算使用者在無線網路中的地理位置。
隨著無線網路技術的日益進步，具備網路連線功能的設備變的越來越普遍，當家庭中的智慧家電(Intelligent Appliance, IA)均具備網路連線能力時，便可以提供家庭使用者方便以及自動化的服務。我們設計並且實做了一個具備使用者位置感知功能的智慧家庭系統，透過代理人(Agent)機制的設計，系統可以根據使用者位置的不同，提供不同的服務。我們選擇了由Microsoft主導的UPnP技術來進行系統實做，此系統包含三個子系統： User interface、Home gateway以及Home functionality subsystem。為了在智慧家庭中提供具備使用者位置感知服務，我們設計了三種不同的代理人：The manager agent負責對使用者的服務做排程、Service Agent負責處裡每一次的服務需求並提供服務給使用者，而Task agent則負責執行在一個服務流程中的所有動作以及智慧家電的控制。為了確保智慧家庭中的服務可以順暢進行，我們同時在Home gateway中加入了服務排程(Service Scheduling)機制以及服務重啟機制(Service Recovery Mechanism)。

Over the last decade there has been a rapid growth of wireless communication technology. Among numerous wireless network architectures, the personal communication services (PCS) networks and wireless local area networks (WLAN) have attracted lots of attention. One of the core functionalities in wireless networks is the location service that provides location information for subscriber services, emergency services, and various mobile networks’ internal operations. In this paper, an integrated location management mechanism is proposed for heterogeneous wireless networks that combine PCS networks and WLAN. Three major functionalities in the integrated location management are the determination of the WLAN connectivity for a mobile terminal, the development of a local area location scheme for WLAN, and the location prediction module for PCS networks. This mechanism not only determines the location of a mobile client more precisely, but also reduces the cost of locating. The performance evaluation is conducted to demonstrate the effectiveness of the proposed mechanism for heterogeneous wireless networks.
One of the core functionalities in wireless networks is the location service that provides location information for subscriber services, emergency services, and various mobile networks’ internal operations. In this paper, an integrated location management mechanism is proposed for heterogeneous wireless networks that combine PCS networks and WLAN. Three major functionalities in the integrated location management are the determination of the WLAN connectivity for a mobile terminal, the development of a local area location scheme for WLAN, and the location prediction module for PCS networks. The location management scheme provides location information to obtain from a hierarchical location database to mobile users and LBS providers. Additionally, a signal-based positioning algorithm is developed for indoor positioning based on WLAN Received Signal Strength Indication (RSSI). Approximated distribution modeling is applied to calculate the probability of users appearing in training points. This mechanism not only determines the location of a mobile client more precisely, but also reduces the cost of locating. The performance evaluation is conducted to demonstrate the effectiveness of the proposed mechanism for heterogeneous wireless networks.
This investigation also presents the Location-based services scheduling mechanism in the Agent-based Smart (ABS) Home System that automates home service operations. The ABS home system comprises three subsystems, namely user interface, home gateway and home functionality. ABS home users can demand services with handheld devices through ABS user interface, and receive them through an agent cooperation mechanism. Three agents are designed and implemented in the agent platform: the manager agent schedules the service processes; the service agent manages service requests, and the task agent executes service operations. The Universal Plug and Play (UPnP) technology is applied for home and building control in ABS home. The proposed service scheduling mechanism provides services that are conveniently-provided, efficient, and comfortably-manipulated. An implementation of the ABS home system is introduced to illustrate the feasibility of the proposed architecture. A performance evaluation is performed to demonstrate the effectiveness of the proposed mechanism.

1. A. Bhattacharya and S.K. Das, “LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks,” Wireless Networks, vol. 8, pp. 121-135, Mar. 2002.
2. T. X. Brown and S. Mohan, “Mobility management for personal communications systems,” IEEE Trans. Veh. Technol., vol.46, no. 2 pp.269–278, May 1997.
3. I. F. Akyildiz, and W. Wang, “A Dynamic Location Management Scheme for Next-Generation Multitier PCS Systems,” IEEE Trans. Wireless Commun., vol. 1, no. 1, pp.178-189, Jan. 2002.
4. S.-T. Cheng, C. Chen, C. Li, and C.-M. Chen, “Network topology management in a mobile-switch ATM network: dynamic partition algorithms,” International Journal of Network Management, vol. 12, no.2, pp.99 – 115, Mar. 2002.
5. Y.-B Lin, “Reducing Location Update Cost in a PCS Network, ” IEEE Trans. Networking, vol.5, no. 1, pp.25-33, Feb. 1997
6. Y.-B. Lin, “Determining the User Locations for Personal Communications Services Networks,” IEEE Trans. Veh. Technol., vol. 43, no. 3, pp.466-473, Aug. 1994.
7. T. Liu, P. Bahl and I. Chlamtac, “Mobility Modeling, Location Tracking, and Trajectory Prediction in Wireless ATM Networks”, IEEE J. Select. Areas Commun., vol. 16, no.6, pp.94-102, Aug. 1998.
8. P. G. Escalle, V. C. Giner, and J. M. Oltra, “Reducing Location Update and Paging Costs in a PCS Network,” IEEE Trans. Wireless Commun., vol. 1, no. 1, pp.200-209, Jan. 2002.
9. Morris, D. and Aghvami and A.H. “A novel location management scheme for cellular overlay networks,” IEEE Trans Broadcasting, vol.52, no.1, pp.108 – 115, Mar. 2006
10. Javid Taheri and Albert Y. Zomaya, “A Simulated Annealing approach for mobile location management,” Computer Commun., vol. 30, no.4, pp. 714-730, Feb. 2007.
11. Ning Chai, Boon Sain Yeo, Yong Huat and Chew “Location management for GPRS,” Computer Networks: The International Journal of Computer and Telecommunications Networking, vol.50, no.15, pp.2888 – 2901, Oct. 2006.
12. Reynold Cheng, Kam-Yiu Lam, Sunil Prabhakar and Biyu Liang, “An efficient location update mechanism for continuous queries over moving objects” Information Systems, vol.32, no.4, pp.593-620, June 2007.
13. Yi-hua Zhu and V.C.M. Leung, “Optimization of Sequential Paging in Movement-Based Location Management Based on Movement Statistics,” IEEE Trans. Veh. Technol., vol.56, no.2, pp.955-964, Mar. 2007.
14. Xiaoxin Wu, Biswanath Mukherjee, and Bharat Bhargava, “A Crossing-Tier Location Update/Paging Scheme in Hierarchical Cellular Networks”, IEEE Trans. Wireless Commun., vol. 5, no. 4, pp. 839-848, April 2006.
15. Il Han and Dong-Ho Cho, “Group location management for mobile subscribers on transportation systems in mobile communication networks,” IEEE Trans. Veh. Technol., Vol.53, no. 1, pp. 181–191, Jan. 2004.
16. Zhimei Jiang, Leung K.K., Kim B.-J.J. and Henry, P. “Seamless mobility management based on proxy servers,” Wireless Communications and Networking Conference, pp. 563–568, Mar. 2002.
17. I.-R. Chen and B. Gu, “Quantitative analysis of a hybrid replication with forwarding strategy for efficient and uniform location management in mobile wireless networks,” IEEE Trans. Mobile Computing, vol.2, no.1, pp. 3–15, 2003.
18. Y. Xiao, Y. Pan and J. Li, “Design and analysis of location management for 3G cellular networks,” IEEE Trans. Parallel and Distributed Systems, vol.15, no.4, pp. 339–349, 2004.
19. K. Kaemarungsi and P. Krishnamurthy, “Modeling of indoor positioning systems based on location fingerprinting,” Twenty-Third Annual Joint Conference of the IEEE Computer and Commun. Societies, vol.2, pp.1012–1022, Mar. 2004.
20. K.M.K. Chu, K.R.P.H. Leung, J.K.-Y. Ng and C.H. Li, “Locating mobile stations with statistical directional propagation model,” Proceedings of the 18th International Conference on Advanced Information Networking and Applications, vol.2, pp. 230–235, 2004.
21. M. Youssef, A. Agrawala and A. Udaya Shankar, “WLAN location determination via clustering and probability distributions,” Proceedings of the First IEEE International Conference on Pervasive Computing and Commun., pp. 143–150, Mar. 2003.
22. P. Bahl and V.N. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol.2, pp.775–784, 2000.
23. Sheng-Po Kuo and Yu-Chee Tseng, “A scrambling method for fingerprint positioning based on temporal diversity and spatial dependency,” IEEE Trans. Knowledge and Data Engineering, vol.20, no.5, pp. 678–684, 2008.
24. A.M. Ladd, K.E. Bekris, A. Rudys, G. Marceau, L.E. Kavraki and D.S. Wallach, “Robotics-based location sensing using wireless Ethernet,” Proceedings of the 8th annual international conference on Mobile computing and networking Atlanta, Georgia, pp. 227–238, 2002.
25. Teemu Roos, Petri MyllymaÈ ki, and Henry Tirri, “A statistical modeling approach to location estimation,” IEEE Trans. Mobile Computing, vol. 1, no.1, pp. 59–69, 2002.
26. Thomas King, Stephan Kopf, Thomas Haenselmann, Christian Lubberger and Wolfgang Effelsberg, “COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses,” Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization, Los Angeles, CA, pp. 34-40, 2006.
27. Smailagic, A. and Kogan, D., “Location sensing and privacy in a context-aware computing environment,” IEEE Wireless Commun., vol.9, no. 5, pp.10–17, Oct. 2002.
28. Koshima, H. and Hoshen, J., “Personal locator services emerge,” IEEE Spectrum, vol.37, no.2, pp.41–48, Feb.2000.
29. J. Hightower and G. Borriello, “Location systems for ubiquitous computing,” IEEE Computer, vol.34, no.8, pp. 57–66, 2001.
30. Chao-Lin Wu Chun-Feng Liao and Li-Chen Fu , “Service-Oriented Smart-Home Architecture Based on OSGi and Mobile-Agent Technology” IEEE Trans. Systems Man and Cybernetics, vol. 37, no. 2, pp.193-205, March 2007.
31. Yu-Ping Tsou Jun-Wei Hsieh Cheng-Ting Lin and Chun-Yu Chen, “Building a Remote Supervisory Control Network System for Smart Home Applications,” IEEE International Conference on Systems, Man and Cybernetics, vol.3, pp.1826-1830, Oct. 2006.
32. Sebastian Lühr, Geoff West and Svetha Venkatesh, “Recognition of emergent human behavior in a smart home: A data mining approach,” Pervasive and Mobile Computing, vol.3, no.2, pp. 95-116 Mar. 2007.
33. W. J. Hwang, H. Tode, and K. Murakami, "Design of the HomeMAC: QoS based MAC Protocol for the Home Network," IEICE Trans. Commun., vol. E85-B, no. 5, pp. 1002-1011, May 2002.
34. Henning Schulzrinne, Xiaotao Wu, Stylianos Sidiroglou and Stefan Berger, "Ubiquitous Computing in Home Networks," IEEE Commun. Magazine, vol. 41, no. 11, pp.128-135, Nov. 2003.
35. Cabrer, M.R. Redondo, R.P.D. Vilas, A.F. Arias, J.J.P. Duque and J.G., “Controlling the smart home from TV” IEEE Trans. Consumer Electronics, vol.52, no.2, pp.421- 429, May 2006.
36. Jens H. Jahnke, Marc d’Entremont and Jochen Stier, “Facilitating the Programming of The Smart Home,” IEEE Wireless Commun., vol. 9, no.6, pp.70-76, Dec. 2002.
37. Yu-Ju Lin, Haniph A. Latchman and Minkyu Lee, “A Power Line Communication Network Infrastructure for The Smart Home,” IEEE Wireless Commun., vol. 9, no.6, pp.104-111, Dec. 2002.
38. Brent A. Miller, Toby Nixon, Charlie Tai, and Mark D. Wood, “Home Networking with Universal Plug and Play,” IEEE Communication Magazine, pp.104-109, Dec. 2001.
39. C. Lee, D. Nordstedt, and S. Helal,”Enabling smart spaces with OSGi,” IEEE Pervasive Computing, vol.2, no.3, pp. 89-94, July 2003.
40. HyunYong Lee, SungTae Moon and Jong Won Kim, “Enhanced UPnP QoS Architecture for Network-adaptive Streaming Service in Home Networks,” IEEE Trans. Consumer Electronics, vol.53,no.3, pp.898-904, Aug. 2007.
41. R. Gupta, S. Talwar and Dharma P. Agrawal, "Jini Home Networking: A Step toward Pervasive Computing," Computer, vol.35, no.8, pp.34-40, Aug. 2002.
42. Diane J. Cook, Michael Youngblood, Edwin O. Heierman, III, Karthik Gopalratnam, Sira Rao, Andrey Litvin, and Farhan Khawaja, "MavHome: An Agent-Based Smart Home," Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp.521-524, Mar. 2003.
43. Sajal K. Das, Diane J. Cook, Amiya Bhattacharya, Edwin O. Heierman III and Tze-Yun Lin, “The Role of Prediction Algorithms in the MavHome Smart Home Architecture,” IEEE Wireless Commun., vol.9, no.6, pp.77-84, Dec. 2002.
44. Kwang Hyoun Joo and Tetsuo Kinoshita and Norio Shiratori, "Design and Implementation of an Agent-Based Grocery Shopping System," IEICE Trans. Inf. & Syst., vol.E83-D, no.11, pp.1940-1951, 2000.
45. Qing Long Wu, Fei-Yue Wang and Yuetong Lin, “A Mobile-Agent Based Distributed Intelligent Control System Architecture For Home Automation,” Proceedings of the 2001 IEEE International Conference on Systems, Man, & Cybernetics, Tucson, Arizona, vol.2, pp. 1599-1605, Oct. 2001.
46. Jecing-Joon Yoo and Dong-Ik Lee, "Scalable Home Network Interaction Model Based on Mobile Agents," Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp.543-546, Mar. 2003.
47. Reza Olfati-Saber, “Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory,” IEEE Trans. Automatic Control, vol.51, no.3, pp.401- 420, Mar. 2006.
48. Amy L. Murphy, Gian Pietro Picco and Gruia-Catalin Roman, “LIME: A coordination model and middleware supporting mobility of hosts and agents” ACM Trans. Software Engineering and Methodology, vol.15, no.3, pp.279 – 328, July 2006.
49. Yih-Farn Chen, Huale Huang, Rittwik Jana, Trevor Jim, Matti Hiltunen, Sam John, Serban Jora, Radhakrishnan Muthumanuckam and Bin Wei, “iMobile EE – An Enterprise Mobile Service Platform,” Wireless Networks, vol.9, no.4, pp.283-297, July 2003.
50. Wenchao Ma and Yuguang Fang, “Dynamic hierarchical mobility management strategy for mobile IP networks,” IEEE J. Select. Areas Commun., vol. 22, no.4, pp.664-676, May 2004.
51. J.M. Gil, C.Y. Park, C.S. Hwang, D.S. Park, J.G. Shon and Y.S. Jeong, “Restoration Scheme of Mobility Databases by Mobility Learning and Prediction in PCS Networks”, IEEE J. Select. Areas Commun., vol. 19, no.10, pp.1962-1973, Oct. 2001
52. W. Ma and Y. Fang, “Dynamic hierarchical mobility management strategy for mobile IP networks,” IEEE J. Select. Areas Commun., vol.22, no.4, pp. 664–676, 2004.
53. Combined GSM and Mobile IP Mobility Handling in UMTS IP CN, 3rd Generation Partnership Project (3GPP), Tech. Spec. TR 23.923 version 3.0.0, May. 2000.
54. R. Agrawal and R. Srikant, “Fast Algorithm for Mining Association Rules,” in Proceedings of the 20th VLDB Conference, Santiago, Chile pp.487-499, Sept. 1994.
55. A.W. Drake, Fundamentals of Applied Probability Theory. New York: McGraw-Hill, 1967.
56. S. Orlando, P. Palmerini and R. Perego, “Enhancing the Apriori Algorithm for Frequent Set Counting”, 3rd International Conference on Data Warehousing and Knowledge Discovery, pp.71-82, 2001.
57. I.-Ray Chen, B. Gu and S.-T. Cheng,” On integrated location and service management for minimizing network cost in personal communication systems,” IEEE Trans. Mobile Computing, vol.5, no.2, pp. 179–192, 2006.
58. Y. Xiao, “Hierarchical mobility database overflow control,” Wireless Communications and Mobile Computing, vol.3, no.3, pp. 329–343, May 2003.
59. Zygmunt J. Haas and Yi-Bing Lin, “On Optimizing the location update costs in the presence of database failure” ACM Wireless Networks, vol.4, no.5, pp. 419-426, Aug. 1998.
60. A.P. Dempster, N.M. Laird and D.B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, vol.39, no.1, pp. 1–38, 1977.
61. A.D. Subramaniam and B.D. Rao, “PDF optimized parametric vector quantization of speech line spectral frequencies,” IEEE Trans. Speech and Processing, vol.11, no.2, 130–142, 2003.
62. G.J. McLachlan and K.E. Basford, Mixture Models: Inference and Applications to Clustering, Marcel Dekker, New York, 1988.
63. S.-K. Ng and G.J. McLachlan, “Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification,” IEEE Trans. Networks, vol.15, no.3, pp.738–749, 2004.
64. Ichiro Satoh: "SpatialAgents: Integrating User Mobility and Program Mobility in Ubiquitous Computing Environments," Wireless Commun. and Mobile Computing, vol. 3, no. 4, pp.411-423, June 2003.
65. Yu-Chee Tseng, Ting-Yu Lin, Yen-Ku Liu and Bing-Rong Lin, "Event-Driven Messaging Services over Integrated Cellular and Wireless Sensor Networks: Prototyping Experiences of a Visitor System," IEEE J. Select. Areas Commun., vol. 23, no.6, pp.1133-1145, June 2005.
66. Bluetooth SIG Specification V1.2. (2003) [Online] Available: http://www.bluetooth.com
67. AT Command Set (2002) [Online] Available: http://www.sonyericsson.com/
68. Hamid Harroud, Mohamed Ahmed and Ahmed Karmouch, “Policy-Driven Personalized Multimedia Services for Mobile Users,” IEEE Trans. Mobile Computing, vol. 2, no.1, pp.16-24, Jan. 2003.
69. VSCP Specification v1.30 (2005) [Online] Available: http://www.vscp.org.
70. Hsing-Pei Kao, Ben Hsieh and Yingchieh Yeh, “A Petri-Net Based Approach for Scheduling and Rescheduling resource-Constrained Multiple Projects,” Journal of the Chinese Institute of Industrial Engineers, vol. 23, no. 6, pp. 468-477, 2006.