Comparison of energy consumption between a mobile device and a collection of dedicated devices

2011 ◽  
Author(s):  
Zhongliang Hu ◽  
Jussi Ruutu
Keyword(s):  
Energy Consumption ◽  
Mobile Device

Energy Consumption Optimization by Increasing The Processor Speed of Moving Communication Devices in Transport Layer Protocol

2019 ◽  
Vol 16 (2) ◽  
pp. 30
Author(s):  
Fakhrur Razi ◽  
Ipan Suandi ◽  
Fahmi Fahmi
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Device ◽  
Packet Loss ◽  
Video Data ◽  
Transmission Delay ◽  
Transport Layer ◽  
Tcp Protocol ◽  
Transport Layer Protocol ◽  
Processor Speed

The energy efficiency of mobile devices becomes very important, considering the development of mobile device technology starting to lead to smaller dimensions and with the higher processor speed of these mobile devices. Various studies have been conducted to grow energy-aware in hardware, middleware and application software. The step of optimizing energy consumption can be done at various layers of mobile communication network architecture. This study focuses on examining the energy consumption of mobile devices in the transport layer protocol, where the processor speed of the mobile devices used in this experiment is higher than the processor speed used in similar studies. The mobile device processor in this study has a speed of 1.5 GHz with 1 GHz RAM capacity. While in similar studies that have been carried out, mobile device processors have a speed of 369 MHz with a RAM capacity of less than 0.5 GHz. This study conducted an experiment in transmitting mobile data using TCP and UDP protocols. Because the video requires intensive delivery, so the video is the traffic that is being reviewed. Energy consumption is measured based on the amount of energy per transmission and the amount of energy per package. To complete the analysis, it can be seen the strengths and weaknesses of each protocol in the transport layer protocol, in this case the TCP and UDP protocols, also evaluated the network performance parameters such as delay and packet loss. The results showed that the UDP protocol consumes less energy and transmission delay compared to the TCP protocol. However, only about 22% of data packages can be transmitted. Therefore, the UDP protocol is only effective if the bit rate of data transmitted is close to the network speed. Conversely, despite consuming more energy and delay, the TCP protocol is able to transmit nearly 96% of data packets. On the other hand, when compared to mobile devices that have lower processor speeds, the mobile devices in this study consume more energy to transmit video data. However, transmission delay and packet loss can be suppressed. Thus, mobile devices that have higher processor speeds are able to optimize the energy consumed to improve transmission quality.Key words: energy consumption, processor, delay, packet loss, transport layer protocol

scholarly journals Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

2021 ◽  
Author(s):  
Marzieh Ranjbar Pirbasti
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Device ◽  
Fault Injection ◽  
Computation Offloading ◽  
Failure Rates ◽  
Recovery Mechanisms ◽  
Efficient Allocations ◽  
Cloud Servers ◽  
Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

scholarly journals Energy Consumption Metrics for Mobile Device Dynamic Malware Detection

2019 ◽  
Vol 159 ◽  
pp. 1045-1052 ◽  
Author(s):  
Fausto Fasano ◽  
Fabio Martinelli ◽  
Francesco Mercaldo ◽  
Antonella Santone
Keyword(s):  
Energy Consumption ◽  
Mobile Device ◽  
Malware Detection

Reliability and mobility aware task offloading strategy and scheduling algorithm in wisdom medical scenario

2020 ◽  
pp. 1-19
Author(s):  
Ping Qi ◽  
Hong Shu ◽  
Qiang Zhu
Keyword(s):  
Energy Consumption ◽  
Mobile Device ◽  
Evaluation Model ◽  
Scheduling Algorithm ◽  
Cognitive Model ◽  
Computation Offloading ◽  
Task Execution ◽  
Computing Paradigm ◽  
Energy Consumption Optimization ◽  
Moving Path

Computation offloading is a key computing paradigm used in mobile edge computing. The principle of computation offloading is to leverage powerful infrastructures to augment the computing capability of less powerful devices. However, the most existing computation offloading algorithms assume that the mobile device is not moving, and these algorithms do not take into account the reliability of task execution. In this paper, we firstly present the formalized description of the workflow, the wireless signal, the wisdom medical scenario and the moving path. Then, inspired by the Bayesian cognitive model, a trust evaluation model is presented to reduce the probability of failure for task execution based on the reliable behaviors of multiply computation resources. According to the location and the velocity of the mobile device, the execution time and the energy consumption model based on the moving path are constructed, task deferred execution and task migration are introduced to guarantee the service continuity. On this basis, considering the whole scheduling process from a global viewpoint, the genetic algorithm is used to solve the energy consumption optimization problem with the constraint of response time. Experimental results show that the proposed algorithm optimizes the workflow under the mobile edge environment by increasing 20.4% of successful execution probability and decreasing 21.5% of energy consumption compared with traditional optimization algorithms.

scholarly journals Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism

2021 ◽  
Vol 29 (1) ◽  
pp. 53-62
Author(s):  
Anastasia V. Daraseliya ◽  
Eduard S. Sopin
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Applications ◽  
Optimization Problem ◽  
Fog Computing ◽  
Computing System ◽  
Battery Life ◽  
Average Response Time

The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

scholarly journals Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

2021 ◽  
Author(s):  
Marzieh Ranjbar Pirbasti
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Device ◽  
Fault Injection ◽  
Computation Offloading ◽  
Failure Rates ◽  
Recovery Mechanisms ◽  
Efficient Allocations ◽  
Cloud Servers ◽  
Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

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