scholarly journals A Context-Aware Mobile User Behavior-Based Neighbor Finding Approach for Preference Profile Construction

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 143 ◽  
Author(s):  
Qian Gao ◽  
Deqian Fu ◽  
Xiangjun Dong
Keyword(s):  
User Behavior ◽  
Mobile User ◽  
Preference Profile ◽  
Context Aware ◽  
Neighbor Finding ◽  
Behavior Based

scholarly journals A Context-Aware Mobile User Behavior Based Preference Neighbor Finding Approach for Personalized Information Retrieval

2015 ◽  
Vol 56 ◽  
pp. 471-476 ◽  
Author(s):  
Qian Gao ◽  
Xiangjun Dong ◽  
Deqian Fu
Keyword(s):  
Information Retrieval ◽  
User Behavior ◽  
Mobile User ◽  
Context Aware ◽  
Neighbor Finding ◽  
Behavior Based

Mobile user behavior based topology formation and optimization in ad hoc mobile cloud

2019 ◽  
Vol 148 ◽  
pp. 132-147 ◽  
Author(s):  
Chunlin Li ◽  
Zhu Liye ◽  
Tang Hengliang ◽  
Luo Youlong
Keyword(s):  
Ad Hoc ◽  
User Behavior ◽  
Mobile User ◽  
Mobile Cloud ◽  
Topology Formation ◽  
Behavior Based

SFIM: Identify user behavior based on stable features

2021 ◽  
Author(s):  
Hua Wu ◽  
Qiuyan Wu ◽  
Guang Cheng ◽  
Shuyi Guo ◽  
Xiaoyan Hu ◽  
...  
Keyword(s):  
User Behavior ◽  
Behavior Based

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.

User-Generated Video Composition Based on Device Context Measurements

2017 ◽  
Vol 11 (01) ◽  
pp. 65-84 ◽  
Author(s):  
Denny Stohr ◽  
Iva Toteva ◽  
Stefan Wilk ◽  
Wolfgang Effelsberg ◽  
Ralf Steinmetz
Keyword(s):  
User Study ◽  
Visual Analysis ◽  
User Behavior ◽  
Context Aware ◽  
Video Streams ◽  
Video Composition ◽  
Video Recordings ◽  
Quality Aspects ◽  
Monitoring Service ◽  
View Change

Instant sharing of user-generated video recordings has become a widely used service on platforms such as YouNow, Facebook.Live or uStream. Yet, providing such services with a high QoE for viewers is still challenging, given that mobile upload speed and capacities are limited, and the recording quality on mobile devices greatly depends on the users’ capabilities. One proposed solution to address these issues is video composition. It allows to switch between multiple recorded video streams, selecting the best source at any given time, for composing a live video with a better overall quality for the viewers. Previous approaches have required an in-depth visual analysis of the video streams, which usually limited the scalability of these systems. In contrast, our work allows the stream selection to be realized solely on context information, based on video- and service-quality aspects from sensor and network measurements. The implemented monitoring service for a context-aware upload of video streams is evaluated in different network conditions, with diverse user behavior, including camera shaking and user mobility. We have evaluated the system’s performance based on two studies. First, in a user study, we show that a higher efficiency for the video upload as well as a better QoE for viewers can be achieved when using our proposed system. Second, by examining the overall delay for the switching between streams based on sensor readings, we show that a composition view change can efficiently be achieved in approximately four seconds.

Research on Correlation Analysis of Power User Behavior Based on Coupled Meteorological Factors

2021 ◽  
Author(s):  
Yixiao Li ◽  
Tianguang Lv ◽  
Xin Zhao ◽  
Jiyan Liu ◽  
Wenjie Ju ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
User Behavior ◽  
Meteorological Factors ◽  
Behavior Based

Performance of User Behavior-Based Similarity on Top-N Recommendation

2021 ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh Bharata Adji
Keyword(s):  
User Behavior ◽  
Behavior Based

Dynamic User Behavior-Based Piracy Propagation Monitoring in Wireless Peer-to-Peer Networks

2013 ◽  
pp. 44-55 ◽  
Author(s):  
Benke Qu ◽  
Wenjia Niu ◽  
Tianqing Zhu ◽  
Lei Wu ◽  
Shijun Liu ◽  
...  
Keyword(s):  
User Behavior ◽  
Peer To Peer ◽  
Peer Networks ◽  
Peer To Peer Networks ◽  
Behavior Based
Sign in / Sign up

Export Citation Format

Share Document