Signal and information processing in mobile cloud computing: Trends and challenges

Mobile devices become popular with the help of hardware improvements and new functions supported by many sensors. In this paper, we propose a mobile and multi-sensing fusion platform to integrate the unstructured streaming sensing data collecting as well as processing technology and build a QoS performance model to estimate the computing resource of the platform. We also demonstrate three mobile and multi-sensing fusion applications as the examples on the platform. Besides, we discuss the trend and challenges of combining the mobile and multi-sensing fusion technology and signal and information processing in mobile cloud computing in great detail.

[1]  Liang Chen,et al.  Supporting fault-tolerance in streaming grid applications , 2007, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[2]  Charles H.-P. Wen,et al.  Fall Detection by a SVM-Based Cloud System with Motion Sensors , 2013, EMC/HumanCom.

[3]  Bugra Gedik,et al.  Fundamentals of Stream Processing: Application Design, Systems, and Analytics , 2014 .

[4]  Matt Welsh,et al.  Towards a Dependable Architecture for Internet-scale Sensing , 2006, HotDep.

[5]  Khaled A. Harras,et al.  Interest aware PeopleRank: Towards effective social-based opportunistic advertising , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[6]  Tao Li,et al.  A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[7]  Kai-Ten Feng,et al.  3D interactive augmented reality-enhanced digital learning systems for mobile devices , 2013, Electronic Imaging.

[8]  Opher Etzion,et al.  Complex event processing , 2004, Proceedings. IEEE International Conference on Web Services, 2004..

[9]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[10]  Fan Ye,et al.  An empirical study of high availability in stream processing systems , 2009, Middleware.

[11]  Li-Chun Wang,et al.  An Effective Algorithm for Interest Aware Opportunistic Advertising by Mining Social and Consuming Information , 2014, 2014 IEEE 79th Vehicular Technology Conference (VTC Spring).

[12]  Edward Walker,et al.  Benchmarking Amazon EC2 for High-Performance Scientific Computing , 2008, login Usenix Mag..

[13]  Yunheung Paek,et al.  Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing , 2014, IEEE Transactions on Mobile Computing.

[14]  Hamzeh Khazaei,et al.  Toward a Big Data Healthcare Analytics System: A Mathematical Modeling Perspective , 2014, 2014 IEEE World Congress on Services.

[15]  Byung-Gon Chun,et al.  CloneCloud: elastic execution between mobile device and cloud , 2011, EuroSys '11.

[16]  Henry Li Introduction to Windows Azure , 2009 .

[17]  Kai-Ten Feng,et al.  Demo paper: Particle-based augmented reality interactive system , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[18]  Mark Ollila,et al.  UMAR: Ubiquitous Mobile Augmented Reality , 2004, MUM '04.

[19]  Eugene Ciurana,et al.  Google App Engine , 2009 .

[20]  Kun-Lung Wu,et al.  Language level checkpointing support for stream processing applications , 2009, 2009 IEEE/IFIP International Conference on Dependable Systems & Networks.

[21]  Deepak S. Turaga,et al.  Towards Optimal Resource Allocation in Partial-Fault Tolerant Applications , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[22]  Kun-Lung Wu,et al.  Fault injection-based assessment of partial fault tolerance in stream processing applications , 2011, DEBS '11.

[23]  Albert G. Greenberg,et al.  Fault-tolerant stream processing using a distributed, replicated file system , 2008, Proc. VLDB Endow..

[24]  Kun-Lung Wu,et al.  IBM Streams Processing Language: Analyzing Big Data in motion , 2013, IBM J. Res. Dev..

[25]  Kai-Ten Feng,et al.  Cloud computing based mobile augmented reality interactive system , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[26]  Jeong-Hyon Hwang,et al.  Fast and Highly-Available Stream Processing over Wide Area Networks , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[27]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[28]  Li-Chun Wang,et al.  A queueing analytical model for service mashup in mobile cloud computing , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[29]  Alejandro P. Buchmann,et al.  Complex Event Processing , 2009, it Inf. Technol..

[30]  Bugra Gedik,et al.  A model‐based framework for building extensible, high performance stream processing middleware and programming language for IBM InfoSphere Streams , 2012, Softw. Pract. Exp..

[31]  Eugene Ciurana,et al.  Developing with Google App Engine , 2009 .