Mobile cloud computing framework for a pervasive and ubiquitous environment

The increasing use of wireless Internet and smartphone has accelerated the need for pervasive and ubiquitous computing (PUC). Smartphones stimulate growth of location-based service and mobile cloud computing. However, smartphone mobile computing poses challenges because of the limited battery capacity, constraints of wireless networks and the limitations of device. A fundamental challenge arises as a result of power-inefficiency of location awareness. The location awareness is one of smartphone’s killer applications; it runs steadily and consumes a large amount of power. Another fundamental challenge stems from the fact that smartphone mobile devices are generally less powerful than other devices. Therefore, it is necessary to offload the computation-intensive part by careful partitioning of application functions across a cloud. In this paper, we propose an energy-efficient location-based service (LBS) and mobile cloud convergence. This framework reduces the power dissipation of LBSs by substituting power-intensive sensors with the use of less-power-intensive sensors, when the smartphone is in a static state, for example, when lying idle on a table in an office. The substitution is controlled by a finite state machine with a user-movement detection strategy. We also propose a seamless connection handover mechanism between different access networks. For convenient on-site establishment, our approach is based on the end-to-end architecture between server and a smartphone that is independent of the internal architecture of current 3G cellular networks.

[1]  William G. Griswold,et al.  Mobility Detection Using Everyday GSM Traces , 2006, UbiComp.

[2]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[3]  Brian D. Noble,et al.  BreadCrumbs: forecasting mobile connectivity , 2008, MobiCom '08.

[4]  Mirco Musolesi,et al.  Transforming the social networking experience with sensing presence from mobile phones , 2008, SenSys '08.

[5]  Hee Yong Youn,et al.  Proceedings of the 10th international conference on Ubiquitous computing , 2008, UbiComp 2008.

[6]  Georg Stellner,et al.  CoCheck: checkpointing and process migration for MPI , 1996, Proceedings of International Conference on Parallel Processing.

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

[8]  Paul Dourish,et al.  Proceedings of the 8th international conference on Ubiquitous Computing , 2006 .

[9]  Norbert Gyorbíró,et al.  An Activity Recognition System For Mobile Phones , 2009, Mob. Networks Appl..

[10]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization using ambient sound and light , 2009, MOCO.

[11]  Albrecht Schmidt,et al.  Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts , 2002, Mob. Networks Appl..

[12]  Juan-Luis Gorricho,et al.  Activity Recognition from Accelerometer Data on a Mobile Phone , 2009, IWANN.

[13]  José M. Bernabéu-Aubán,et al.  Solaris MC: A Multi Computer OS , 1996, USENIX Annual Technical Conference.

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

[15]  Tanzeem Choudhury,et al.  - 1-Sensing and Modeling Activities to Support Physical Fitness , 2005 .

[16]  BeiglMichael,et al.  Multi-sensor context-awareness in mobile devices and smart artifacts , 2002 .

[17]  Hyotaek Lim,et al.  Dynamic Load Balancing and Network Adaptive Virtual Storage Service for Mobile Appliances , 2011, J. Inf. Process. Syst..

[18]  Kristian Paul Bubendorfer Resource Based Policies for Load Distribution , 1996 .

[19]  Youngki Lee,et al.  SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments , 2008, MobiSys '08.