Computation offloading technique for energy efficiency of smart devices

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.

Computation Offloading Technique for Energy Efficiency of Smart Devices

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

Overview of Studies on the Cognitive Workload of the Air Traffic Controller

Air Traffic Control Officer (ATCO) will be the branch that will have the most impact in the air transport system. The duty of ATCOs is to prevent the collision of airplanes in the air provided by the controllers on the ground and to overcome the possible confusion. Being exposed to a very high cognitive workload of ATCOs, which is one of the high-risk occupational groups, is important in terms of flight safety. However, it has been observed that studies on the differences in cognitive workload that may occur between experienced and inexperienced ATCO under different task difficulties are quite insufficient in literature. This study presents research studies on cognitive workload measurement methods and ATCO's cognitive workload. In this study, first of all, the importance of determining the cognitive workload and its measurement methods are explained. In addition, literature studies related to cognitive workload of ATCOs, particularly by using eye tracker are presented.