A context-aware collaborative model for smartphone energy efficiency over 5G wireless networks

Abstract The staggering progress of mobile computing has brought forth exciting opportunities in the research community which are currently stretching beyond the limits of modern battery technologies. Although energy efficiency is of utmost importance in mobile systems, current solutions fail to take into consideration the intrinsic mobility of handhelds and are based on overusing power-hungry cellular networks for offloading into the cloud. We propose a novel collaboration model based on context-awareness and opportunistic networking in the context of 5G wireless networks which offers the possibility of offloading tasks in an opportunistic cloud based on mobile communities. We apply our solution to a real-life use-case, namely preventive patient monitoring, and show through experimental analysis based on real user traces that it maximizes power saving and minimizes overall execution time of tasks.

[1]  Ciprian Dobre,et al.  Using Socio-Spatial Context in Mobile Cloud Process Offloading for Energy Conservation in Wireless Devices , 2019, IEEE Transactions on Cloud Computing.

[2]  Ciprian Dobre,et al.  ONSIDE: Socially-aware and Interest-based dissemination in opportunistic networks , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[3]  Roberto Nardone,et al.  Estimation of the Energy Consumption of Mobile Sensors in WSN Environmental Monitoring Applications , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[4]  Marco Conti,et al.  From opportunistic networks to opportunistic computing , 2010, IEEE Communications Magazine.

[5]  Jon Crowcroft,et al.  The case for crowd computing , 2010, MobiHeld '10.

[6]  Jordi Mongay Batalla,et al.  Advanced multimedia service provisioning based on efficient interoperability of adaptive streaming protocol and high efficient video coding , 2015, Journal of Real-Time Image Processing.

[7]  Ciprian Dobre,et al.  Exploring Predictability in Mobile Interaction , 2012, 2012 Third International Conference on Emerging Intelligent Data and Web Technologies.

[8]  Jeng-Shyang Pan,et al.  Metaheuristics for the deployment of 5G , 2015, IEEE Wireless Communications.

[9]  Joel J. P. C. Rodrigues,et al.  Editorial for MONET Special Issue on Networking in 5G Mobile Communications Systems: Key Technologies and Challenges , 2015, Mob. Networks Appl..

[10]  Ciprian Dobre,et al.  Reaching for the clouds: contextually enhancing smartphones for energy efficiency , 2013, HP-MOSys '13.

[11]  Ramachandran Ramjee,et al.  Stratus: energy-efficient mobile communication using cloud support , 2010, SIGCOMM '10.

[12]  Feng Xia,et al.  A Cooperative Watchdog System to Detect Misbehavior Nodes in Vehicular Delay-Tolerant Networks , 2015, IEEE Transactions on Industrial Electronics.

[13]  Ciprian Dobre,et al.  Interaction predictability of opportunistic networks in academic environments , 2014, Trans. Emerg. Telecommun. Technol..

[14]  Guangjie Han,et al.  Mobile cloud computing in 5G: Emerging trends, issues, and challenges [Guest Editorial] , 2015, IEEE Netw..

[15]  Marco Conti,et al.  Opportunistic networking: data forwarding in disconnected mobile ad hoc networks , 2006, IEEE Communications Magazine.

[16]  Dongman Lee,et al.  A virtual cloud computing provider for mobile devices , 2010, MCS '10.

[17]  Ciprian Dobre,et al.  A methodology for assessing the predictable behaviour of mobile users in wireless networks , 2014, Concurr. Comput. Pract. Exp..

[18]  Eugene Marinelli,et al.  Hyrax: Cloud Computing on Mobile Devices using MapReduce , 2009 .

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

[20]  Mauro Iacono,et al.  Modeling performances of concurrent big data applications , 2015, Softw. Pract. Exp..