A context-aware assistance system for maintenance applications in smart factories based on augmented reality and indoor localization

2015 ◽  
Author(s):  
Holger Flatt ◽  
Nils Koch ◽  
Carsten Rocker ◽  
Andrei Gunter ◽  
Jurgen Jasperneite
Keyword(s):  
Augmented Reality ◽  
Indoor Localization ◽  
Assistance System ◽  
Context Aware ◽  
Smart Factories

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.

Using RSSI Simple Localization Method to Implement the Context-Aware and Social Recommendation System

2013 ◽  
Vol 479-480 ◽  
pp. 1213-1217
Author(s):  
Mu Yen Chen ◽  
Ming Ni Wu ◽  
Hsien En Lin
Keyword(s):  
Social Media ◽  
Social Relations ◽  
Context Awareness ◽  
Recommendation System ◽  
Product Information ◽  
Indoor Localization ◽  
Context Aware ◽  
Social Recommendation ◽  
Localization Method ◽  
The Social

This study integrates the concept of context-awareness with association algorithms and social media to establish the Context-aware and Social Recommendation System (CASRS). The Simple RSSI Indoor Localization Module (SRILM) locates the user position; integrating SRILM with Apriori Recommendation Module (ARM) provides effective recommended product information. The Social Media Recommendation Module (SMRM) connects to users social relations, so that the effectiveness for users to gain product information is greatly enhanced. This study develops the system based on actual context.

scholarly journals A Portable Driver Assistance System Headset Using Augmented Reality

2018 ◽  
Vol 7 (3.6) ◽  
pp. 294
Author(s):  
Shantanu Misra ◽  
Vedika Parvez ◽  
Tarush Singh ◽  
E Chitra
Keyword(s):  
Augmented Reality ◽  
Driver Assistance System ◽  
Pedestrian Detection ◽  
Cost Effective ◽  
Weather Conditions ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Vehicle Collision ◽  
Life Threatening

Vehicle collision leading to life threatening accidents is a common problem which is incrementing noticeably. This necessitated the need for Driver Assistance Systems (DAS) which helps drivers sense nearby obstacles and drive safely. However, it’s inefficiency in unfavorable weather conditions, overcrowded roads, and low signal penetration rates in India posed many challenges during it’s implementation. In this paper, we present a portable Driver Assistance System that uses augmented reality for it’s working. The headset model comprises of five systems working in conjugation in order to assist the driver. The pedestrian detection module, along with the driver alert system serves to assist the driver in focusing his attention to obstacles in his line of sight. Whereas, the speech recognition, gesture recognition and GPS navigation modules together prevent the driver from getting distracted while driving. In the process of serving these two root causes of accidents, a cost effective, portable and holistic driver assistance system has been developed.  

scholarly journals Context-Aware Driver Assistance System

2012 ◽  
Vol 10 ◽  
pp. 785-794 ◽  
Author(s):  
Wael Alghamdi ◽  
Elhadi Shakshuki ◽  
Tarek R.Sheltami
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Context Aware ◽  
Driver Assistance
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