A mobile crowd sensing based task assignment in Internet of Things

Smart phones with integrated sensors have the ability to provide different mobile crowd based services which in turn called mobile crowd sensing (MCS) in Internet of Things (IoT). But utilization ratio of this ability by mobile users is less. Several interaction issues exist between the mobile users. This paper mainly focuses on building an effective route for crowd sourcing services from requester to provider. Crowd sensing is a process of obtaining information or data from the people and providing services to the crowd using the same data. Users are selected for being a part of the route only if they contribute to successful services history. Android application model is developed using these ideas which is the client side. Provision for connecting to server based on the user location and route map is also included in the model design to improve the effectiveness of Mobile Crowd Sensing (MCS) technology.

[1]  Daqing Zhang,et al.  4W1H in mobile crowd sensing , 2014, IEEE Communications Magazine.

[2]  Mirco Musolesi,et al.  The Rise of People-Centric Sensing , 2008, IEEE Internet Comput..

[3]  Bin Guo,et al.  From participatory sensing to Mobile Crowd Sensing , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[4]  Marco Conti,et al.  Context- and social-aware middleware for opportunistic networks , 2010, J. Netw. Comput. Appl..

[5]  Tzung-Shi Chen,et al.  Mining User Movement Behavior Patterns in a Mobile Service Environment , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  Ram Dantu,et al.  Socioscope: Human Relationship and Behavior Analysis in Social Networks , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  Huan Liu,et al.  Social computing in blogosphere , 2009 .

[8]  Yunhao Liu,et al.  CitySee: not only a wireless sensor network , 2013, IEEE Network.

[9]  John A. Stankovic,et al.  Research Directions for the Internet of Things , 2014, IEEE Internet of Things Journal.

[10]  Tao Zhou,et al.  Understanding continuance usage of mobile services , 2013, Int. J. Mob. Commun..

[11]  Giuseppe Sansonetti,et al.  An approach to social recommendation for context-aware mobile services , 2013, TIST.

[12]  Kyle Chard,et al.  Social Cloud Computing: A Vision for Socially Motivated Resource Sharing , 2012, IEEE Transactions on Services Computing.

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

[14]  Xin He,et al.  A Crowdsourcing Assignment Model Based on Mobile Crowd Sensing in the Internet of Things , 2015, IEEE Internet of Things Journal.

[15]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[16]  Wenji Mao,et al.  Social Computing: From Social Informatics to Social Intelligence , 2007, IEEE Intell. Syst..

[17]  Nuria Oliver,et al.  Influence of personality on satisfaction with mobile phone services , 2013, TCHI.