A Neuro-Fuzzy Hybrid Framework for Augmenting Resources of Mobile Device

2021 ◽  
pp. 1-37
Author(s):  
S. Anitha ◽  
T. Padma
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Applications ◽  
Execution Time ◽  
Mobile App ◽  
Edge Computing ◽  
Bandwidth Utilization ◽  
Modeling Framework ◽  
Neuro Fuzzy

Due to the drastic exploitation of mobile devices and mobile apps in the day-to-day activities of people, the enhancement in hardware and software tools for mobile devices is also rising rapidly to cater to the requirements of mobile users. However, the progress of resource-intensive mobile applications is still inhibited by the limited battery power, restricted memory, and scarce resources of mobile devices. By employing mobile cloud computing, mobile edge computing, and fog computing, many researchers are providing their frameworks and offloading algorithms to augment the resources of mobile devices. In the existing solutions, offloading resource-intensive tasks is adopted only for specific scenarios and also not supporting the flexible exploitation of IoT-based smart mobile applications. So, a novel neuro-fuzzy modeling framework is proposed to augment the inadequate resources of a mobile device by offloading the resource-intensive tasks to external entities, and also a Bat optimization algorithm is exploited to schedule as many tasks as possible to the augmentation entities thereby improving the total execution time of all tasks and minimizing the resource exploitation of the mobile device. In this research work, external augmentation entities like distant cloud, edge cloud, and microcontroller devices are providing Resource augmentation as a Service (RaaS) to mobile devices. An IoT-based smart transport mobile app is implemented based on the proposed framework which depicts a significant reduction in execution time, energy consumption, bandwidth utilization, and average delay. Performance analysis depicts that the neuro-fuzzy hybrid model with Bat optimization provides a significant improvement compared with proximate computing and web service frameworks on the Quality of Service (QoS) parameters namely energy consumption, execution time, bandwidth utilization, and latency. Thus, the proposed framework exhibits a feasible solution of RaaS to resource-constrained mobile devices by exploiting edge computing.

scholarly journals A Mobile Application Recommendation Framework by Exploiting Personal Preference with Constraints

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Konglin Zhu ◽  
Zexuan Liu ◽  
Lin Zhang ◽  
Xinyu Gu
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Applications ◽  
Mobile Application ◽  
Mobile Apps ◽  
Mobile App ◽  
Network Data ◽  
Mobile Data ◽  
Personal Preference ◽  
Battery Power

Explosive mobile applications (Apps) are proliferating with the popularity of mobile devices (e.g., smartphones, tablets). These Apps are developed to satisfy different function needs of users. Majority of existing App Stores have difficulty in recommending proper Apps for users. Therefore, it is of significance to recommend mobile Apps for users according to personal preference and various constraints of mobile devices (e.g., battery power). In this paper, we propose a mobile App recommendation framework by incorporating different requirements from users. We exploit modern portfolio theory (MPT) to combine the popularity of mobile Apps, personal preference, and mobile device constraints for mobile App recommendation. Based on this framework, we discuss the recommendation approaches by constraints of phone power and limited mobile data plan. Extensive evaluations show that the proposed mobile App recommendation framework can well adapt to power and network data plan constraints. It satisfies the user App preference and mobile device constraints.

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 Offloading of Mobile Apps in Hand-Held Devices using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 10536-10543
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Mobile Apps ◽  
Poor Performance ◽  
Cloud Service ◽  
Mobile App ◽  
Cloud Services ◽  
Mobile Users

The usage of hand-held and mobile devices has been increased rapidly in recentyears. The execution of sophisticated softwares and Apps on mobile phonescan lead to poor performance with respect to energy consumption and responsetime. With the emergence of the offloading concept of App workloads, an attempt has been made toimprove the performance of the hand-held devices by exploiting cloud service. The computation offloadingin hand-held devices consumes energy as well as time for transferring the datafrom hand-held devices to cloud. For the effective use of cloud services, there is a need to optimize the execution time of mobile App and energy consumedby the respective App. Many research endeavors have been made in recentyears to reduce the execution time and energy consumption during offloadingprocess. However, the usage of offloading has been evolved to quench the thirstof mobile users who execute multiple Apps simultaneously and are in dire needof seamless connectivity but some dynamic algorithms are needed to decidewhether offloading is favorable or not for a mobile App. If the mobile Apptakes more time and consumes more battery if executed on cloud then it isrecommended to use mobile platform rather than using cloud services. In thispaper, we are presenting machine learning based techniques which would help the mobile users in decision making to execute the App on mobile devices or on cloud using cloud services.

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.

scholarly journals Mobile Application for Electric Power Monitoring on Energy Consumptions at a Campus University

2018 ◽  
Vol 11 (2) ◽  
pp. 637
Author(s):  
Murizah Kassim ◽  
Maisarah Abdul Rahman ◽  
Cik Ku Haroswati Che Ku Yahya ◽  
Azlina Idris
Keyword(s):  
Energy Consumption ◽  
Electric Power ◽  
Mobile Applications ◽  
Working Hours ◽  
Mobile App ◽  
Smart Meter ◽  
University Campus ◽  
Smart Meters ◽  
Power Monitoring ◽  
Energy Consumptions

This paper presents a research on electric power monitoring prototype mobile applications development on energy consumptions in a university campus. Electric power energy consumptions always are the issue of monitoring usage especially in a broad environment. University campus faces high used of electric power, thus crucial analysis on cause of the usage is needed. This research aims to analyses electric power usage in a university campus where implemented of few smart meters is installed to monitor five main buildings in a campus university. A Monitoring system is established in collecting electric power usage from the smart meters. Data from the smart meter then is analyzed based on energy consume on 5 buildings. Results presents graph on the power energy consume and presented on mobile applications using Live Code coding. The methodology involved the setup of the smart meters, monitoring and data collected from main smart meters, analyzed electrical consumptions for 5 buildings and mobile system development to monitor. A Live Code mobile app is designed then data collected from smart meter using ION software is published in graphs. Results presents the energy consumed for 5 building during day and night. Details on maximum and minimum energy consumption presented that show load of energy used in the campus. Result present Tower 1 saved most eenergy at night which is 65% compared to block 3 which is 8% saved energy although block 3 presents the lowest energy consumption in the working hours and non-working hours. This project is significant that can help campus facility to monitor electric power used thus able to control possible results in future implementations.

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 Defining Stable Touch Area Based on a Large-Screen Smart Device in 3D-Touch Interface

2019 ◽  
Vol 13 (02) ◽  
pp. 115
Author(s):  
Younghoon Seo ◽  
DongRyeol Shin ◽  
Choonsung Nam
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Execution Time ◽  
Specific Area ◽  
Smart Device ◽  
Font Size ◽  
Large Screen ◽  
Force Data ◽  
Stable Area ◽  
Touch Interfaces

<p><span lang="EN-US"><span style="font-family: Gulim; font-size: medium;">Touch interface technologies for mobile devices are essentially in use. The purpose of such touch interfaces is to run an application by touching a screen with a user’s finger or to implement various functions on the device. When the user has an attempt to use the touch interface, users tend to grab the mobile device with one hand. Because of the existence of untouchable areas to which the user cannot reach with the user’s fingers, it is possible to occur for a case where the user is not able to touch a specific area on the screen accurately. This results in some issues that the mobile device does not carry out the user’s desired function and the execution time is delayed due to the wrong implementation. Therefore, there is a need to distinguish the area where the user can stably input the touch interface from the area where the users cannot and to overcome the problems of the unstable touch area. Furthermore, when the size of the screen increases, these issues will become more serious because of an increase in the unstable touch areas. Especially, an interface that receives position and force data like 3D-touch requires the stable area setting different from the conventional 2D-touch. In this paper, we search and analyze the stable touch areas on the large screen where the user can do 3D-touch inputs.</span></span></p>

scholarly journals Analyzing of Customer Requirements to Eric Mobile Application

2018 ◽  
Vol 14 (2) ◽  
pp. 15-22
Author(s):  
Juraj Čamaj ◽  
Jaroslav Mašek ◽  
Martin Kendra
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Application ◽  
Mobile App ◽  
Mobile Technologies ◽  
Customer Requirements ◽  
Application Development ◽  
Information Centre ◽  
Software Application ◽  
Basic Characteristics

Abstract Users in transport, forwarding and logistics companies use the mobile technologies for connect to existing information systems. By solving the ERIC Mobile project, these services will also be available on mobile devices. The article is aimed at lancing the requirements of all types of customers for the ERIC Mobile app. After the basic characteristics of the mobile device, the operation systems, the application development typology, the authors focus on the developing application “ERIC Mobile”. The aim of the article is to provide relevant requirements for further research and development of the software application of the rail freight information centre in Europe for end users of mobile devices such as smartphones and tablets.

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