چكيده به لاتين
Abstract:
The use of information technology in all areas of life has brought a great change to human life as well as health sector services. Telemedicine aims to improve patient care, easier access to physicians, reduce medical costs, raise awareness for disease outbreaks, train health care workers, reduce patient transportation to medical centers, and patients' self-care. Mobile technology is experiencing rapid growth in Iran and it is very useful for deprived areas that are facing with geographic and economic constraints in terms of access to medical services. Mobile devices for computational applications in addition to voice communications have become very popular. However, these devices have limitations due to the small battery and limited energy to do heavy calculations. Doing calculations on a cloud is a technique for dealing with this obstacle. Reducing power consumption is a fundamental design principle for mobile devices and other small computing devices that are not permanently connected to the power source. In this study, we tried to investigate the current medical priorities in Iran, especially in deprived areas, and to examine their current conditions for the acceptance of mobile health systems. Then, some suggestions for providing a system which is appropriate for the treatment and follow-up of such diseases were proposed and implemented and using the capabilities and benefits of cloud computing to improve the limitations of mobile, optimizing was done. So, with the aim of reducing energy consumption in mobile applications by using segmentation health promotion program, which was improved after evaluation, and using offloading technique to do heavy computing segments, efforts have been made via reducing energy consumption to save battery consumption and to increase battery life. Also, with the increase in processing speed, run time decreases. Offloading does not always save energy, thus the decision for offloading is a necessity. Via finding a threshold in our case study, we found a certain point for offloading to cloud with inspecting low, medium and high processing loads. Moreover, for processing video files, we calculated the amount of data that save energy by transferring it to the cloud according to the implementation conditions.
Keywords: Mobile health, e-health, cloud computing, mobile cloud computing, energy consumption, battery life, offloading
