2017 International Workshop on Heterogeneous Wireless Networks and Quality-of-Experience (HWNQoE-2017)
To be held in conjunction with the 13th IEEE International Conference on
Green Computing and Communications (GreenCom-2017)
21-23 June 2017, Exeter, UK
Focus of the Workshop
With the rapid innovation in sophisticated wireless communication technologies including 4G LTE-Advanced, fixed and mobile broadband WiMAX, 3G UMTS/CDMA and high-speed Wi-Fi, the past years have witnessed a dramatic growth of mobile multimedia applications, for example, Live Mobile Video, 3D Video Stream, Digital and Mobile TV, VoIP, etc. These content-rich multimedia applications consume a huge amount of network resources and create new challenging issues for heterogeneous wireless networks to satisfy the ever-increasing Quality-of-Experience (QoE) requirements. Furthermore, the traditional criteria for the measurements and provisioning of network performances are generally based on Quality of Service (QoS), which is defined in terms of network delivery capacity and resource availability such as bandwidth, jitter, throughput, transmission delay and availability. While it is widely accepted that QoS is inadequate for successfully and comprehensively evaluating the user-perceived quality of multimedia services over heterogeneous networks, as QoE is determined not only by QoS but also by the quality of multimedia content to be delivered.
The purpose of this workshop is to bring together industry and academic researchers to provide an up-to-date picture of the state-of-the-art research in the field of heterogeneous wireless communications and QoE provisioning. It is also intended to provide a forum to exchange varying beliefs and understandings of how to seamless integrate advanced wireless networking and adaptive multimedia processing for enabling evolution of the current wireless system toward future networks.
Topics of the Workshop
Topics covered include, but are not limited to, the following:
- Heterogeneous networks architecture and performance analysis
- QoE-aware resource allocation, interference management and admission control
- Energy-aware QoE control in resource-constrained wireless networks
- QoE-aware cross-layer design and optimization
- M2M/IoT network architecture and performance analysis
- 5G network architecture/techniques, and performance analysis
- QoE-aware green technologies for multimedia applications
- Secure routing design and analysis in QoE-aware mobile networks
- Multimedia traffic modeling and characterization in heterogeneous wireless networks
- Resource-aware efficient multimedia video coding
- 2D/3D Visual media over heterogeneous networks
- Advanced image and video processing
- User-centric performance and optimisation of advanced media services
- Multimedia session signalling in wireless/mobile environments
- Multimedia content management and distribution
- Context-aware wireless multimedia applications
- Software defined networking and network function virtualization
- Subjective QoE testing of wireless social networking services
- Real experiments and testbeds for QoE evaluation in mobile networks and vehicular networks
- QoE evaluation for emerging applications and services
- Network security, reliability and survivability
- Mobile cloud computing and applications
- Mobile and wireless applications: health, environmental protection
Keynote Speaker: Prof. Mianxiong Dong (Muroran Institute of Technology, Japan)
Title: Human-Like Driving: Empirical Decision-Making System for Autonomous Vehicles
Abstract—Autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the rise of depth sensor technologies and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a human-like driving system in the paper to give autonomous vehicles the ability to make decisions like a human. In our method, a Convolutional Neural Network (CNN) model is used to detect, recognize and abstract the informations in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The most significant advantage in our work is that the proposed method can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system only on the depth informations, and avoid the unstable RGB data. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks