A Context-Aware Technical Information Manager for Presentation in Augmented Reality

Technical documentation is evolving from static contents presented on paper or via digital publishing to real-time on-demand contents displayed via virtual and augmented reality (AR) devices. However, how best to provide personalized and context-relevant presentation of technical information is still an open field of research. In particular, the systems described in the literature can manage a limited number of modalities to convey technical information, and do not consider the ‘people’ factor. Then, in this work, we present a Context-Aware Technical Information Management (CATIM) system, that dynamically manages (1) what information as well as (2) how information is presented in an augmented reality interface. The system was successfully implemented, and we made a first evaluation in the real industrial scenario of the maintenance of a hydraulic valve. We also measured the time performance of the system, and results revealed that CATIM performs fast enough to support interactive AR.

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

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00211-9 ◽

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.

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