Creating a Just-in-time Location-aware Service Using Fuzzy Logic

A fuzzy just-in-time (JIT) ubiquitous service networked system is established in this study. This system is an innovative application of mobile commerce, ubiquitous computing, and ambient intelligence. The intended user is a traveler who must decide on the best service location along the planned route; the system arranges the service when the user demands it, so that when the user arrives at the service location the required service is ready. This system applies the “just-in-time” concept to mobile commerce, ubiquitous computing, and ambient intelligence. This innovation has great potential for providing better services in these fields. The issue of how to determine the JIT service location and path in a ubiquitous service network is critical; however, because data about the user’s position is inaccurate, the task is difficult. To tackle this difficulty, a FINLP model is formulated, and a fuzzy version of Dijkstra’s algorithm is proposed. A test system has been established to evaluate the feasibility of the proposed methodology. Based on the experimental results, the proposed JIT ubiquitous service networked system was able to reduce the user’s average waiting time by 74%.

[1]  Meng Zhang,et al.  A Web Service Recommendation Approach Based on QoS Prediction Using Fuzzy Clustering , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[2]  Yu-Fang Chung,et al.  An agent-based English auction protocol using Elliptic Curve Cryptosystem for mobile commerce , 2011, Expert Syst. Appl..

[3]  貴弘 小嵜,et al.  インターネットを利用したJust-In-Time法による空気圧人工筋マニピュレータの制御 , 2012 .

[4]  Eija Kaasinen,et al.  User needs for location-aware mobile services , 2003, Personal and Ubiquitous Computing.

[5]  Ruoning Xu,et al.  Ranking fuzzy numbers based on fuzzy mean and standard deviation , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[6]  Ching-Hsue Cheng,et al.  A new approach for ranking fuzzy numbers by distance method , 1998, Fuzzy Sets Syst..

[7]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[8]  Toly Chen,et al.  A fuzzy set approach for event tree analysis , 2001, Fuzzy Sets Syst..

[9]  Binshan Lin,et al.  MoRVAM: A reverse Vickrey auction system for mobile commerce , 2007, Expert Syst. Appl..

[10]  Andrew V. Goldberg,et al.  Shortest paths algorithms: Theory and experimental evaluation , 1994, SODA '94.

[11]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[12]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[13]  Toly Chen,et al.  Finding the just-in-time service location and path in a ubiquitous service network , 2013 .

[14]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[15]  E. Lee,et al.  Comparison of fuzzy numbers based on the probability measure of fuzzy events , 1988 .

[16]  Ying-Feng Kuo,et al.  Selection of mobile value-added services for system operators using fuzzy synthetic evaluation , 2006, Expert Syst. Appl..

[17]  Wu Jian Web service fuzzy matching in internet-based manufacturing , 2006 .

[18]  Fernando Bobillo,et al.  A fuzzy framework for Semantic Web Service description, matchmaking, ranking and selection , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).