scholarly journals Cognition-Based Context-Aware Cloud Computing for Intelligent Robotic Systems in Mobile Education

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49103-49111 ◽  
Author(s):  
Jianbo Zheng ◽  
Qieshi Zhang ◽  
Shihao Xu ◽  
Hong Peng ◽  
Qin Wu
Keyword(s):  
Cloud Computing ◽  
Robotic Systems ◽  
Context Aware ◽  
Mobile Education

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

scholarly journals Context-Aware Cloud Computing for Construction Collaboration

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mohamad Fathi ◽  
Mohammad Abedi ◽  
Shuib Rambat ◽  
Shakila Rawai ◽  
Mohd Zakiyudin
Keyword(s):  
Cloud Computing ◽  
Context Aware

scholarly journals Mobile Cloud Computing Provides Service Availability using A Context Aware Architecture

2021 ◽  
Vol 10 (1) ◽  
pp. 252-255
Author(s):  
Anusha L* ◽  
Nagaraja G S
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Dynamic Network ◽  
Mobile Node ◽  
Mobile Systems ◽  
Mobile Cloud ◽  
Context Aware ◽  
Mobile Nodes ◽  
Resource Pooling ◽  
Emergency Education

Mobile systems are becoming increasingly important, and new promising paradigms such as Mobile Cloud Computing. Mobile Cloud Computing is an application that allows data to be stored and processed outside of the mobile node. There is a lot of interest in using the resources that can be accessed by transparently using distributed resource pooling offered by nearby mobile nodes. This type of device is used in emergency, education, and tourism. Systems basically use dynamic network topologies in which network partitions and disconnection occurs frequently, so the availability of the services has been compromised. In this paper proposes the context aware architecture to provide availability of the services deployed in mobile and dynamic network environments which provides better response time, the services need not be migrated at real time, so the bandwidth and energy used has been more efficient.

Context-Aware Cloud Computing for Personal Learning Environment

2016 ◽  
pp. 123-140
Author(s):  
Feng Chen ◽  
Ali H. Al-Bayatti ◽  
Francois Siewe
Keyword(s):  
Cloud Computing ◽  
Learning Environments ◽  
User Profile ◽  
Virtual Learning ◽  
Resource Utilisation ◽  
Context Aware ◽  
Personal Learning ◽  
Personal Learning Environment ◽  
Personal Learning Environments ◽  
Virtual Platform

Virtual learning means to learn from social interactions in a virtual platform that enables people to study anywhere and at any time. Current Virtual Learning Environments (VLEs) are normally institution centric and are designed to support formal learning, which do not support lifelong learning. These limitations led to the research of Personal Learning Environments (PLEs), which are learner-centric and provide lifelong access as well as the ability of a user to produce (share) and consume information resources easily. In this research, a context-aware cloud based PLE architecture is proposed, which is driven by a Context-Aware Engine to acquire, filter and interpret context information based on the preferences defined in user profile, where cloud computing is taken as service infrastructure. An illustrative personal learning scenario is investigated to demonstrate the proof of concept implementation. The results show the benefits of the proposed architecture on resource utilisation and user experience.

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