A framework for energy efficient and flexible offloading scheme for handheld devices

Patra, Biswajit; Roy, Sarbani; Chowdhury, Chandreyee

doi:10.1109/ants.2015.7413625

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…In [18], authors have proposed an offloading scheme with a workflow consisting of an assessment phase, scanning phase, selection phase, and offloading phase. Their technique initially discovers a device in the neighborhood and then paired the device with Bluetooth to offload complex tasks with high energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Computation offloading technique for energy efficiency of smart devices

Choi

Paul

2021

J Cloud Comp

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The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. 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. 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. 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. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly.

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Section: Related Workmentioning

confidence: 99%