Observational research on the social impact of cell phone usage in public places suggests that the mere presence of cell phones in public conflicts the private and public spheres and inhibits social interaction with proximate others,saving the energy of the mobile phones , storage of user profile data and make sharing quickly becomes difficult. In addition to mobility, another defining characteristic of mobile systems is user social interaction. To manage this entire problem two methods have been proposed, initially E-Shadow method is proposed for distribute mobile local social networking system. E-Shadow has two main components: (1) Local profiles. They enable E-Shadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. In second design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. The proposed design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods. It continuously tune continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently; it helps receivers find the direction of a specific location with accuracy. The experiments demonstrate that can recognize not only whether a social interaction is taking place, but also the type of social interaction, distinguishing between formal and informal user social settings.Focusing on helping behaviour in particular, Results Indicate That While On The Cell Phone, Users Are Less Likely To Offer Help.

E-Shadow, Mobile Phone, Layered Publishing, Direction-driven Matching, Energy- Aware Localization, Bayesian Estimation framework and Social Interaction Network

[1]. InfoWorld: More mobile Internet users than wired in Japan (July 2006), http://www.infoworld. Com /article/ 06/07/05/HNjapannetusers 1.html
[2]. CNET News.com. Mobile browsing becomes mainstream (2006), http://news.com.com/ Mobile +browsing +becoming+mainstream/ 2100-1039 3-606 2365.html.
[3]. Kangourouge. Proxidating, the first ever Bluetooth dating software for mobile phones (2007), http://www.proxidating.com.
[4]. ComputerWorld. Cabir Worm Wriggles into U.S. Mobile Phones (2005), http:// www. computerworld. com/securitytopics/security/virus/story/ 0,10801,999 35,00.html
[5]. M. Bugeja (2005). Interpersonal divide. New York, NY: Oxford University Press.
[6]. G. Roussos, A. J. Marsh, and S. Maglavera. “Enabling Pervasive Computing with Smartphones.” IEEE Pervasive Computing, Special Issue, The Smartphone, pp. 20-27, Apr. 2005.
[7]. E. Miluzzo, N. D. Lane, K. Fodor, et al. “Sensing meets mobile social networks: The design, implementation and evaluation of cenceme application.” In ACM Sensys, 2008.
[8]. M. Raento, A. Oulasvirta, R. Petit, et al. “ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications.” IEEE Pervasive Computing, Special Issue, The Smartphone, pp. 51-59, Apr.2005.
[9]. Eagle N, (Sandy) Pentland A (2005) Reality mining: sensing complex social systems. PersUbiquitComput 10(4):255–268
[10]. Do TMT, Gatica-Perez D “Contextual grouping: discovering real-life interaction types from longitudinal Bluetooth data,” infoscience.epfl.ch
[11]. Wyatt D, Choudhury T, Bilmes J (2011) “Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science.” ACM Transactions onIntelligent Systems and Technology (TIST) 2(1)
[12]. Cristani M, Pesarin A, Vinciarelli A (2011) “Look at who’s talking: voice activity detection by automated gesture analysis,” in Workshopon Interactive Human Behavior Analysis in Open or Public Spaces.
[13]. Bergvik, S. (2004). Disturbing cell phone behavior – A psychological perspective. Implications for mobile technology in tourism. Retrieved March 12, 2005.
[14]. MRaento, AOulasvirta, R Petit, H Toivonen (2005) ContextPhone: a prototyping platform for context-aware mobile applications. IEEE Pervasive Comput 4(2):51–59
[15]. S Mardenfeld, D Boston, SJ Pan, Q Jones, A Iamntichi, C Borcea (2010) “GDC: Group Discovery using Co-location traces,” 2010 Mobile NetwAppl Author's personal copy IEEE Second International Conference on Social Computing, pp. 641–648
[16]. Do TMT, Gatica-Perez D (2011) “GroupUs: Smartphone proximity data and human interaction type mining,” in 5th annual International Symposium on Wearable Computers, no. 2
[17]. N Banerjee, S Agarwal, P Bahl, R Chandra, A Wolman, M Corner (2010) “Virtual compass: relative positioning to sense mobile social interactions,” Pervasive Computing, pp. 1–21.
[18]. Z. Yang, B. Zhang, J. Dai, A. Champion, D. Xuan. “E-SmallTalker: A Distributed Mobile System for Social Networking in Physical Proximity.” In Proc. of ICDCS, 2010.
[19]. M. Azizyan, I. Constandache, and R. Roy Choudhury. “Surroundsense: mobile phone localization via ambience fingerprinting.” In MobiCom, pp. 261-272, 2009.