![]() In a wireless network considering a bad channel condition, devices can have significant energy consumption to transfer the task to a cloud server. The offloading technique brings several benefits however, it also hinders the energy consumption performance of limited power devices. To alleviate this problem, the concept of computation offloading has been proposed that transfers complex computation job to cloud server with powerful computing resources. Therefore, there are limitations in performing complex calculations using various sensors such as heart rate, camera, or using network communication service with other devices. Smart devices such as wearable devices and smartphones have limitations in battery capacity and processor performance that can be mounted with limitations on device size in order to improve portability due to the characteristics of mobile devices. ![]() Despite a huge number of work on mobile cloud computing, so far very few studies focus specifically In addition, CISCO estimates that the number of world-class wearable devices will increase year by year and will reach 924 million by 2021. According to a report issued by Tractica, a market intelligence agency, in 2016, the market for health care wearable devices is expected to grow rapidly from 2015 to 2021, reaching about 18 billion by 2021. Global Information and Communication Technology (ICT) companies are launching different advanced wearable devices specifically, the healthcare wearable devices are growing every year. Recently, wearable devices has become an integral part of human life and refining several regular activity such as healthy eating, active lifestyle, sufficient exercise, sleep tracking, emergency alert, and many more. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. In our model, we increase the energy efficiency of smart devices. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading.
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