scholarly journals Computation offloading technique for energy efficiency of smart devices

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaejun Ko ◽  
Young-June Choi ◽  
Rajib Paul
Keyword(s):  
Energy Efficiency ◽  
Decision Model ◽  
Measurement Model ◽  
Wearable Devices ◽  
Computation Offloading ◽  
Smart Devices ◽  
Continuous Growth ◽  
Workload Measurement ◽  
Major Development ◽  
Cloud Server

AbstractThe 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.

scholarly journals Computation Offloading Technique for Energy Efficiency of Smart Devices

2021 ◽  
Author(s):  
Jaejun Ko ◽  
Young-June Choi ◽  
Rajib Paul
Keyword(s):  
Energy Efficiency ◽  
Decision Model ◽  
Measurement Model ◽  
Wearable Devices ◽  
Computation Offloading ◽  
Smart Devices ◽  
Continuous Growth ◽  
Workload Measurement ◽  
Major Development ◽  
Cloud Server

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

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Next Generation Wearable Devices

2014 ◽  
Vol 6 (2) ◽  
pp. 25-51 ◽  
Author(s):  
Niraj Shakhakarmi
Keyword(s):  
Health Monitoring ◽  
Service Management ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Smart Devices ◽  
Smart Device ◽  
Monitoring Device ◽  
Next Generation ◽  
Spectrum Utilization ◽  
Smart Health

The next generation wearable devices are Smart health monitoring device and Smart sousveillance hat which are capable of using wearable sensors for measuring physiological information, sousveillanace, navigation, as well as smart device to smart device communications over cellular coverage. Smart health monitoring device collect and observe different health related information deploying biosensors and can predict health problems. Smart sousveillance hat provides the brainwaves based fatigue state, training and sousveillance around the wearer. The next generation wearable smart devices deploy the device to device communications in LTE assisted networks with D2D server, D2D Application server and D2D enhanced LTE signalling for D2D service management, spectrum utilization and broad cellular coverage, which make them portable, social, commercial and sustainable. Thus, the wearable device technology will merge with the smart communications besides the health and wellness. Furthermore, the simulation and performance evaluation shows that LTE-D2D wearable smart device communications provides two times more energy efficiency than LTE-UEs cellular communications. The LTE-D2D data rate is also found significantly higher with higher D2D-SINR for lower relative mobility (= 30m/s) and lower D2D distance (<400m) between devices.

A multi-objective decision model for the improvement of energy efficiency in buildings

Energy ◽  
2010 ◽  
Vol 35 (12) ◽  
pp. 5483-5496 ◽  
Author(s):  
Christina Diakaki ◽  
Evangelos Grigoroudis ◽  
Nikos Kabelis ◽  
Dionyssia Kolokotsa ◽  
Kostas Kalaitzakis ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Decision Model ◽  
Multi Objective

scholarly journals Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1446 ◽  
Author(s):  
Liang Huang ◽  
Xu Feng ◽  
Luxin Zhang ◽  
Liping Qian ◽  
Yuan Wu
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Wireless Devices ◽  
Cloud Server ◽  
Convergence Performance ◽  
Multi Server

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

Assessing Energy Efficiency of High Pressure Regenerative Feedwater Heaters in a Heating Unit of a Cogeneration Plant

2015 ◽  
Vol 236 ◽  
pp. 14-25 ◽  
Author(s):  
Bogdan Pojawa
Keyword(s):  
Energy Efficiency ◽  
Environmental Protection ◽  
Combustion Products ◽  
Primary Energy ◽  
Cogeneration Plant ◽  
Heating Unit ◽  
Continuous Growth ◽  
Fuel Prices ◽  
Energy Company ◽  
Long Time

Broadly understood technological progress, growth of the world's population and striving of individual countries for economic growth cause increased demand for energy. That energy is mainly obtained conventionally, from mineral fuels [16]. Limited fuel resources and high demand for fuels, which accompanies the increased demand for energy, result in continuous growth of fuel prices and, what it involves, the price of energy [6,16]. Another effect of the increased production of energy results is also the increased emission of combustion products which are harmful for the natural environment, mainly CO2 and NOx [6,9,10,]. Because of the above-mentioned factors, the importance of the assessment of energy efficiency, at the stage of energy production, distribution and end use as well as the problem of environmental protection gain more and more importance [4,5,15]. The idea of energy efficiency lies not only in energy conservation but also in finding ways for the present activities of producers and consumers to require reduced demand for primary energy expressed in tonnes of oil equivalent [3,7,13,14,17,19,21]. Energy companies must therefore respect a number of legal regulations concerning energy efficiency and environmental protection [3,4,5,15]. An energy company such as a cogeneration plant may achieve an improvement of energy efficiency mainly as a result of energy cogeneration itself but also as a result of improving the efficiency of internal processes (energy transformations) in the producing unit (in this case heating unit). Ensuring the maximum possible energy efficiency of the internal processes within the heating unit requires performing constant assessment of the entire unit and its components [7,11,12,13]. Even though energy cogeneration has been in use for a long time now [7,12,21], the problem of conducting a running energy efficiency assessment of the components of the heating unit still remains open [12].

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