A Service Discovery Approach Based on a Quantitative Similarity Measure for M-Tourism Platforms

Abstract

This paper presents a localization architecture for an m-tourism services delivery platform. The aim of the system is to deliver services for nomads (e-tourists) according to their localization and according to the results given by the search engine. This engine is based on a quantitative similarity measure. The discovered services are presented via a Web Map Service. Moreover, the platform integrates an adaptation sub-system for heterogeneous environments and an e-negotiation module.

Share and Cite:

F. Bouyakoub and A. Belkhir, "A Service Discovery Approach Based on a Quantitative Similarity Measure for M-Tourism Platforms," Communications and Network, Vol. 4 No. 3, 2012, pp. 227-239. doi: 10.4236/cn.2012.43027.

1. Introduction

Recently, a new evolution is taking place among the numerical applications in the tourist domain. The m-tourism or mobile tourism relates to the applications of information technologies usable either only on computers, but on a variety of mobile terminals, thus complementing the process of e-tourism (transactions and information access before and after the voyage): information access during the voyage.

Thanks to the development of smartphones, satisfying the fans of the all-in-one (phone calls, Internet, etc.) as well as unlimited Internet offers, m-tourism is in constant evolution.

With the development of these high technologies, it became possible to develop new services, well beyond simple projection of Web contents on smartphones screens.

In this paper, we present a new Services Delivery Platform (SDP), based on tourists’ localization. The platform offers to services providers (travel agencies) tools to host and to describe their services in order to simplify the search process. On the client side, the system offers a search engine based on a new quantitative similarity measure to define the correspondence rate between the client request and services descriptions.

This article is organized as follows: Section 2 presents briefly the telephony evolution and we make an overview of the different techniques of localization used in location based services (LBS) and Mobile Location Services (MLS). Section 3 presents our service delivery platform: first we present the system architecture and it basic components, and then we present the platform functionalities, in particular the search engine. The performance evaluation of the proposed similarity measure, used in the search engine, is presented in Section 4 and finally Section 5 concludes this paper.

2. Telephony Advancement: From SMS to LBS and MLS

Since its advent in 1973 by a Motorola company team, mobile phones have shown a continuous evolution. This innovation, enabling persons to be joined anywhere and at any time, became essential and very useful. Over the past few years, the use of mobile phones was limited to vocal calls, but actually its use is extended to several services like MMS (Multimedia Messaging Service), VoD (Video on Demand) … etc.

The boom in mobiles market and the emergence of new services bring current GSM networks to their limit. The defined rate (9.6 kb/s) for these networks is insufficient to support new services, in particular multimedia services.

As 2G and 2.5G supports, respectively functionalities of voice and data, the 3G and 3G+ technologies adds multimedia functionalities.

Unlike 2G-3G evolution (hardware and software changes), the 3G-3G+ evolution needs only software changes. Based on the same CDMA (Code division multiple access) and the same waveband, the deployment of 3G+ does not need physical changes. The entire 3G infrastructure is preserved; the operator has just to update codecs, access network modulations ….

The development of the 3G+ generation satisfied the strong needs of multimedia services, by offering a very high rate. Moreover, this generation makes it possible to extend Internet network, by supporting mobile phones besides to personal computers: the mobile internet was born.

The development of mobile applications is in continuous progress and the new generations of mobiles support multiple services which were not supported by the first generations, and among these services we cite Location Based Services (LBS) and Mobile Location Services (MLS).

LBS refer to any application that uses location of a device or a person as a primary source to deliver context sensitive service. Therefore it covers a wide range of applications and devices. The popularity of LBS makes it one of most essential and useful asset in almost all industries. However, its market is divided into various categories including navigation, emergency assistance, tracking, advertising, billing, management, games and leisure. However, its application is growing with innovative ideas day by day with the expansion of mobile phone market.

On the other hand, MLS refer to services that are based on the location of a mobile phone.

MLS are value-added services that utilize the user’s position information.

From the user’s perspective these services provide:

•  Localized and up-to-date information: Up-to-date information that is relevant in a particular location is given at the right moment;

•  Personalized information;

•  Increased efficiency and pertinence: The information can be more focused and of higher quality , when it is also tailored according to the user’s location;

•  Increased safety: The positioning functionality increase user safety by being able to locate someone in distress.

The location information has no value in itself; it is only a parameter for provisioning valuable applications relevant to a user at a specific location and at a specific point in time [1].

There is three generation of localisation services [1]: In the first generation, users were obliged to specify their position before receiving the service. In the second generation, the localization is done automatically when the user asks for a service. The third generation is characterized by a bigger precision of localization. It can also, in some cases, send alerts to users.

2.1. MLS Components

A common MLS architecture is composed of three elements: A Mobile Operator (MO), a Service Provider and a Mobile User (MU). Usually, the MO works as an intermediary between the provider and the MU. This includes the identification of customers for payment purposes, the transmission of user’s location to the Provider and the delivery of services via mobile communication networks.

2.2. Localization Technologies

Localization technologies provide means to locate a subscriber and/or a valid mobile equipment in order to optimize, to adapt and to deliver services.

Positioning technologies can basically be divided into handset-based and handset-assisted, and similarly network-based and network-assisted, each of which offer different levels of accuracy. Hybrid positioning solutions combine two or more positioning technologies thus achieving an improved accuracy in positioning.

A successful positioning technology must meet the accuracy requirements set by the specific service, at the lowest possible cost and with good sensitivity and minimal impact on the network and subscriber equipment.

We can classify these techniques into three classes:

•  The first class (based on the mobile terminal) is composed of: Observed Time Difference of Arrival (OTDOA) [2], Enhanced Observed Time Difference (EOTD) [3], Global Positioning System (GPS) [4] and Assisted Global Positioning System (AGPS) [5].

•  The second class we have user-self-locating and for example, complementary local area technologies, such as Bluetooth [6] may be used to improve coverage.

•  In the third class (based on the mobile network), several techniques have been developed like Cell Identification (Cell-ID) and variations [7]. In this class, mobile network localize the user and determine his position. This solution is simple and little expensive to achieve, however, the localization is not very precise, it localizes a mobile at 250 meters in an urban area and about 10 kilometres in rural area [1].

Each localization technology has its advantages and its inconveniences; the choice of a localization technology is relative to the application domain. According to an achieved comparative study in [1], we note that the main comparison criterion is the accuracy. For non-critical services, Cell-ID is sufficient, especially in an urban area. For critical services (security domain), the accuracy is very important; the usage of the GPS or A-GPS is required.

In general, there is a trade-off between the accuracy of the location method and the modification needed for the mobile terminals. As a rule of thumb, the better the measurement accuracy the more modifications are needed for the mobile terminals and, therefore the higher the added costs are for terminals.

Currently most commercial applications use information-based services over Cell-ID due to its broad coverage and cost applications [1].

Figure 1 presents accuracy of most important positioning methods.

The idea presented in this article consists on proposing a service delivery platform based on clients’ localization (first level of search). Next, we propose to the subscribers, a search engine based on a quantitative similarity measure, to improve the result of the first search level by weighting up the correspondence rate between the client request and services descriptions.

3. A Service Delivery Platform for Tourism Mobile Location Based Services

With the development of new communication systems we assist to the birth of a new generation of users called “nomads”. Indeed, with the appearance of wireless networks and mobile devices (PDA, smartphones…), it became possible to connect from any place to search for services; however these nomads prefer to have services near of their position. The constraint of localization is important in many domains like tourism.

Electronic tourism or e-tourism, representing all the activities of tourism using Internet, proposes means to organize travels via Internet. E-tourism makes it possible to reserve hotel rooms, to define a travel route or to exchange information with other e-tourists via forums.

Several actors intervene in the market of e-tourism, we enumerate:

•  Virtual travel agency: Thanks to the novel modes of communication, and in particular Internet, travel agencies present their services on the Web.

•  Tour operator: These organizations aim to gather different services offers, and to sell them as packages.

•  The e-tourist.

The m-tourism use mobile communications (PDAs, mobile phones, tablets…) for tourism services. The mtourism is more personal as you hold your mobile de-

Figure 1. Accuracy of localization technologies.

vice all the time with you and can easily send or receive information at any time.

The proposed solution, presented in this paper, is a services delivery platform (SDP) for m-tourism, based on the functional diagram (Figure 2) of Web services (SOA architecture).

The aims of the platform are to:

•  Propose publishing functionalities to travel agencies to hosts services and services descriptions in UDDI (Universal Description, Discovery and Integration), to make easy the discovery process;

•  Carry out the communication between the m-tourist and the system;

•  Allow users to search for services according to their localization, theirs requirements and theirs needs;

•  Manage users’ heterogeneity;

•  Manage contents heterogeneity;

•  And finally, manage users’ profiles.

Figure 3 summarizes the delivery process. In our preceding work, we have presented solutions for different stages of services delivery (adaptation, E-negotiation…). In this paper we will focus our work on services’ discovery process by proposing a service search engine based on a new quantitative similarity measure.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] C. Boonstra, G. Van Knippenbergh and S. Meijers, “Location Based Services on Mobile Internet,” White Paper Open Mobile Internet initiative OMI2, 2008. http://www.omi2.nl/wp-content/uploads/2008/12/omi-whitepaper-on-lbs-nov-2008.pdf
[2] D. Bartlett, “Software Blanking for OTDOA Positioning,” Proceedings of the 16th 3GPP TSG-RAN Meeting, Florida, 4-7 June 2002, pp. 1-7.
[3] S. Fischer and A. Kangas, “Time-of-Arrival Estimation for E-OTD Location in GERAN,” Proceedings of the 12th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, San Diego, 30 September-3 October 2001, pp. 121-125.
[4] D. K. Elliott and C. J. Hegarty, “Understanding GPS: Principles and Applications,” 2nd Edition, Artech House Publisher, Boston, 2005.
[5] C. Steinfield, “The Development of Location Based Services in Mobile Commerce,” In: B. Preissl, H. Bouwman and C. Steinfield, Eds., E-Life after the Dot Com Bust, Springer Verlag, Berlin, 2004, pp. 177-198.
[6] N. Boulos, “Analyse, Définition et Conception d’une Solution de Sécurité de la Voix,” DEA Thesis, SAINTJOSEPH University, Beyrouth, 2003.
[7] J. Borkowski, J. Niemela and J. Lempiainen, “Performance of Cell ID+RTT Hybrid Positioning Method for UMTS Radio Networks,” Proceedings of the 5th European Wireless Conference Mobile and Wireless Systems beyond 3G, Barcelona, 24-27 February 2004, pp. 24-27.
[8] G. Klyne, F. Reynolds, C. Woodrow, H. Ohto, J. Hjelm, M. H. Butler and L. Tran, “Composite Capability/Preference Profiles (CC/PP): Structure and Vocabularies 1.0,” World Wide Web Consortium (W3C) Recommendation, 2004. http://www.w3.org/TR/CCPP-structvocab/
[9] C. L. Velasco, M. Villanova, J. Gensel and H. Martin, “Services Web Adaptés aux Utilisateurs Nomades,” Proceedings of the 2nd French-Speaking Conference on Mobility and Ubiquity Computing, Grenoble, 31 May-3 June 2005, pp. 133-136.
[10] World Wide Web Consortium. http://www.w3.org
[11] D. Bulterman, “Synchronized Multimedia Integration Language (SMIL2.0),” W3C Recommendation, 2001. http://www.w3.org/TR/SMIL2
[12] M. Handley, H. Schulzrinne, E. Schooler and J. Rosenberg, “SIP: Session Initiation Protocol,” IETF Request for Comments RFC 2543, 1999. http://www.ietf.org/rfc/rfc2543.txt
[13] M. Handley and V. Jacobson, “SDP: Session Description Protocol,” IETF Request for Comments RFC 2327, 1998. http://www.ietf.org/rfc/rfc2327.txt
[14] F. M. Bouyakoub and A. Belkhir, “A Similarity Measure for the Negotiation in Web Services,” Multimedia Tools and Applications, Vol. 50, No. 2, 2009, pp. 279-312. doi:10.1007/s11042-009-0383-8
[15] L. Maesano, C. Bernard and X. Le Galles, “Services Web Avec J2EE et. NET: Conception et Implémentation,” Eyrolles, Eyrolles, 2003.
[16] D. Booth, H. Haas, F. McCabe, E. Newcomer, M. Champion, C. Ferris and D. Orchard, “Web Services Architecture,” W3C Web Services Architecture Working Group note, 2004. http://www.w3.org/TR/ws-arch/
[17] M. Chelbabi, “Découverte de Services Web Sémantiques: Une Approche Basée sur le Contexte,” Master Thesis, CERIST, Algiers, 2006.
[18] P. Rompothong and T. Senivongse, “A Query Federation of UDDI Registries,” Proceedings of the 1st International Symposium on Information and Communication Technologies, (ISICT 03), 24-26 September, Dublin, 2003, pp. 561-566.
[19] P. Palathingal and S. Chandra, “Agent Approach for Service Discovery and Utilization,” Proceedings of the 37th Annual Hawaii International Conference on System Science HICSS-37, Hawaii, 5-8 January 2004, pp. 1-9.
[20] D. Martin, M. Burstein, J. Hobbs, O. Lassila, D. McDermott, S. McIlraith, et al., “Owl-S: Semantic Mark-Up for Web Services,” W3C Member Submission, 2004. http://www.w3.org/Submission/OWL-S/
[21] E. Motta, J. Domingue, L. Cabral and M. Gaspari, “Irs-II: A Framework and Infrastructure for Semantic Web Services,” Proceedings of the Second International Semantic Web Conference (ISWC 2003), Florida, 20-23 October 2003, pp. 306-318.
[22] L. Vu, M. Hauswirth and K. Aberer, “Towards p2p-Based semantic Web Service Discovery with QOS Support,” Proceedings of the International Workshop on Business Process Management (BPM 2005), Nancy, 6-7 September 2005, pp. 18-31.
[23] K. Verma, R. Mulye, Z. Zhong, K. Sivashanmugam and A. Sheth, “Speed-R: Semantic p2p Environment for Diverse Web Service Registries,” W3C Technical Report, 2004. http://webster.cs.uga.edu/~mulye/SemEnt/Speed-R.html
[24] S. Pokraev, J. Koolwaaij and M. Wibbels, “Extending UDDI with Context-Aware Features Based on Semantic Service Descriptions,” Proceedings of the International Conference on Web Services (ICWS '03), Nevada, 23-26 June 2003, pp. 184-190.
[25] M. Keidl and A. Kemper, “Toward Context/Aware Adaptable Web Services,” Proceedings of the 13th World Wide Web Conference (W3C), New York, 19-21 May 2004, pp. 55-65.
[26] S. Kouadri-Mostéfaoui and G. Kouadri-Mostéfaoui, “Towards a Contextualization of Service Discovery and Composition for Pervasive Environments,” Proceedings of the AAMAS workshop on Web Services and Agent-Based Computing (WSABE’2003), Melbourne, 14-18 July 2003.
[27] M. F. Bruandet and J. P. Chevallet, “Utilisation et Construction de Base de Connaissances Pour la Recherche d’Information,” In: M. H. Stefanini and E. Gaussier, Eds., Assistance Intelligente à la Recherche d’Information, Hermès Sciences Edition, 2003, pp. 85-118.
[28] G. Bisson, “La Similarité: Une Notion Symbolique/Numérique,”Apprentissage Symbolique-Numérique (Tome 2) Editions CEPADUES, 2000, pp. 169-201.
[29] M. Rajman, “Similarités Pour Données Textuelles,” Proceedings of the 4th Journées Internationales d’Analyse Statistique des Données textuelles JADT-98, Nice, 19-21 February 1998, pp. 545-556.
[30] M. M. Richter, “Classification and Learning of Similarity Measures,” In O. Opitz, B. Lausen and R. Klar, Eds., Studies in Classification, Data Analysis and Knowledge Organization, Springer Verlag, Berlin, 1992.
[31] A. Belkhirat, A. Bouras and A. Belkhir, “A New Similarity Measure for the Anomaly Intrusion Detection,” Proceedings of the 3rd International Conference on Network and System Security (NSS2009), Queensland, 19-21 October 2009, pp. 431-436.
[32] A. Belkhirat and A. Belkhir, “A New Similarity Measure for the Profiles Management,” Proceedings of the 10th International Conference on Computer Modelling and Simulation UKSIM '11, Cambridge, 30 March-1 April 2011, pp. 255-259.
[33] F. M. Bouyakoub and A. Belkhir, “AdaMS: An Adaptation Multimedia System for Heterogeneous Environments,” Proceedings of the 2nd IFIP International Conference on New Technologies, Mobility and Security (NTMS 2008), Tangier, 5-7 November 2008, pp. 42-46.
[34] F. M. Bouyakoub and A. Belkhir, “Automatic Generation of User’s Profiles for Location-Based Adaptation of Multimedia Documents,” Proceedings of the 1st IEEE International Workshop on Generation C Wireless Networks (GenCWiNets'08), Texas, 7-9 December 2008, pp. 400-405.
[35] S. Bouyakoub and A. Belkhir, “SMIL BUILDER: An Incremental Authoring Tool for SMIL Documents,” ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 7, No. 1, 2011, pp. 1-30. doi:10.1145/1870121.1870123

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.