Computation offloading technique for energy efficiency of smart devices
Abstract 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 powe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-08-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-021-00260-8 |
| _version_ | 1818681735780499456 |
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| author | Jaejun Ko Young-June Choi Rajib Paul |
| author_facet | Jaejun Ko Young-June Choi Rajib Paul |
| author_sort | Jaejun Ko |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-17T10:07:41Z |
| format | Article |
| id | doaj.art-f78d90d3b6c1493baecc184d08fc795a |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-12-17T10:07:41Z |
| publishDate | 2021-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-f78d90d3b6c1493baecc184d08fc795a2022-12-21T21:53:07ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2021-08-0110111410.1186/s13677-021-00260-8Computation offloading technique for energy efficiency of smart devicesJaejun Ko0Young-June Choi1Rajib Paul2Department of Software and Computer Eng, Ajou UniversityDepartment of Software and Computer Eng, Ajou UniversityDepartment of Software and Computer Eng, Ajou UniversityAbstract 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.https://doi.org/10.1186/s13677-021-00260-8Wearable deviceSmartphoneComputation offloadingEnergy efficiency |
| spellingShingle | Jaejun Ko Young-June Choi Rajib Paul Computation offloading technique for energy efficiency of smart devices Journal of Cloud Computing: Advances, Systems and Applications Wearable device Smartphone Computation offloading Energy efficiency |
| title | Computation offloading technique for energy efficiency of smart devices |
| title_full | Computation offloading technique for energy efficiency of smart devices |
| title_fullStr | Computation offloading technique for energy efficiency of smart devices |
| title_full_unstemmed | Computation offloading technique for energy efficiency of smart devices |
| title_short | Computation offloading technique for energy efficiency of smart devices |
| title_sort | computation offloading technique for energy efficiency of smart devices |
| topic | Wearable device Smartphone Computation offloading Energy efficiency |
| url | https://doi.org/10.1186/s13677-021-00260-8 |
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