Intensive Energy Aware Mobile Computational Offloading using Machine Learning Strategies

In the modern-world each and everyone needs a Smartphone to achieve their communication needs globally as well as an acceptable fact is mobile devices plays a vital role in individual's life. Smartphones now-a-days are fully consumeroriented, in which it contains a huge-variety of applications to provide service to its users. This case leads more computational power to the mobile devices as-well-as the processing ability of such devices are expected to be highly concentrated. The usage of multiple applications in simultaneous manner over mobile phones causes several issues to users such as poor-batterylifetime, speed-issues, mobile-heating and so on. In this system, a novel' and intelligent approach is proposed to solve the issues rising due to the computational abilities of mobile offloading as well as empirically proves the advantages of mobile offloading with remote servers. The term offloading explores a hidden meaning of remote accessibility, in which the mobile devices can process the storage mechanisms and computational-needs are in outside of the mobile device, so that the processing overhead of the mobile devices are highly reduced. This combination of Mobile Devices and Remote Server Manipulation is generally called as Mobile-Cloud-Computing. The term cloud refers the remote server, all the computational needs are performed over there and the resulting summaries are portrayed over the mobile devices within fraction of seconds.The accessing nature of cloud services usually follows an important strategy called MobileCrowdsensing, in which it also plays a major role in CloudService Selection procedures. In which the Mobile-Crowdsensing effectively sense the crowd ratio of mobile-devices and share the resources of cloud to their requirements as-well-as the MobileCrowdsensing also analyze and predict the application processes of general-interest. The advancement of Machine learning strategies gives hand to this nature of handling such difficult process like remote data handling and processing. This paper explores a new machine learning based approach called, Intensive Energy-aware Mobile Computational Offloading Model (IEMCOM), which concentrates more on mobile offloading issues such as huge-data transfers, complex-mobile application processing-scenarios, network-interruptions and so on. A final outcome empirically proves the integration of mobile and cloud computing results good battery-lifetime, enhanced offloading-process and security as well.