MINING DATA FOR TRAFFIC DETECTION SYSTEM USING GPS_ ENABLE MOBILE PHONE IN MOBILE CLOUD INFRASTRUCTURE

The increasing need for traffic detection system has become a vital area in both developing and developed countries. However, it is more important to get the accurate and valuable data to give the better result about traffic condition. For this reason, this paper proposes an approach of tracking traffic data as cheap as possible in terms of communication, computation and energy efficient ways by using mobile phone network. This system gives the information of which vehicles are running on which location and how much speed for the Traffic Detection System. The GPS sensor of mobile device will be mainly utilized to guess a user’s transportation mode, then it integrates cloud environment to enhance the limitation of mobile device, such as storage, energy and computing power. This system includes three main components: Client Interface, Server process and Cloud Storage. Some tasks are carried out on the Client. Therefore, it greatly reduces the bottleneck situation on Server side in efficient way. Most of tasks are executed on the Server and history data are stored on the Cloud Storage. Moreover, the paper mainly uses the distance based clustering algorithm in grouping mobile devices on the same bus to get the accurate data.

[1]  Hans D. Schotten,et al.  Access Schemes for Mobile Cloud Computing , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[2]  Ouri Wolfson,et al.  Extracting Semantic Location from Outdoor Positioning Systems , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[3]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[4]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[5]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[6]  Wei-Ying Ma,et al.  Understanding mobility based on GPS data , 2008, UbiComp.

[7]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[8]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[9]  Philip S. Yu,et al.  Transportation mode detection using mobile phones and GIS information , 2011, GIS.

[10]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

[11]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[12]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[13]  Sunny Consolvo,et al.  Learning and Recognizing the Places We Go , 2005, UbiComp.

[14]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.