Abstract
In modern internet the usability of smart mobile devices is rapidly increased. It is not only used for commercial and communication purposes, it can be extended in the field like mobile health care, mobile learning, and mobile games. More access of smart mobile devices has a restriction due to resource constraint, this issue overwhelming via cloud computing. Heavy computations from mobile devices are offloaded to cloud and the application got executed. The problem occurs when the Electrogram data is entered into a mobile network in cloud environment which holds void and energy holes occurrences with heavy traffic and unbalanced load distribution.This paper aims to develop hybrid intelligence behavioural patterns for void and energy hole in mobile cloud offloading. The research work carried out in this paper extends its novelty in stage partitioning for concentric circles and sensor node distribution. The network area distribution of partitioning are uniformly distributed among void hole. The simulation results show the proposed approach produces high energy consumption in the mobile cloud paradigm.
Highlights
Mobile cloud computing paradigm gets the mobility from mobile computing and flexibility from cloud computing and the reliability from network providers which would be the excellent combination to have a best business solution
Performance metrics The performance of the AVEH-SIMCO method is compared with the existing Lagrange Relaxation based Aggregated Cost (LRAC) [13] and wireless sensor network energy hole alleviating (WSNEHA) [12]
Considering this factor, AVEH-SIMCO during the network setup it partitions the network are into concentric circles and deploys the sensor nodes uniformly random
Summary
Mobile cloud computing paradigm gets the mobility from mobile computing and flexibility from cloud computing and the reliability from network providers which would be the excellent combination to have a best business solution. Mobile clouds computing highly increases the performance and optimize the energy [1]. Journey of several computational parts to a controlling cloud server to avoid the unsteady connectivity the solution provider is computation offloading which enables services like optimize carrying out time and energy efficiency [2]. In circumstance of complete mobile application offloading, problem of transmission time highly affects the performance of the application in cloud environment. Diverse wireless communication channels are accessible such as 2G, 3G, Wi-Fi, and 4G to offload the data between the mobile gadget and cloud server [3]. ECG biosensor, mobile device and healthcare center alerts about heart attack in the early hours [4]
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