Real-Time Mobile Cloud Computing: A Case Study in Face Recognition

Tsai

2018

Sustainability

Mobile communication and handheld devices are currently extremely popular, and provide people with convenient and instant platforms for social networking. However, existing social networking services cannot offer efficient human-machine interfaces or intuitive user experiences. Mobile users must manually input account information and find targets from search results when attempting to add someone to their friend list on social networking sites, such as Facebook and Twitter. Additionally, mobile users may not be able to identify correct targets because some usernames are identical. Typos may occur during the input process due to unfamiliar identifiers, further increasing the total operation time. To encourage social initiation between mobile users, we design an instant social networking framework, called SocialYou, to minimize the startup time based on mobile cloud computing. SocialYou proposes an efficient architecture and innovative human-machine interfaces to alleviate the complexity and difficulty for mobile users using handheld devices. In particular, we implement an Android-based prototype to verify the feasibility and superiority of SocialYou. The experimental results show that SocialYou outperforms the existing methods and saves substantial amounts of operation time for mobile social networking.

Section: System Modelmentioning

confidence: 99%

Instant Social Networking with Startup Time Minimization Based on Mobile Cloud Computing

Tsai

2018

Sustainability

“…Current smartphones are continuously getting powerful with a diverse number of embedded sensors, in the future these can be used for better contextual data collection, and better usability of the system. In addition, delegating resource intensive tasks to cloud based services approach not only further increases capabilities of the smartphones but also opens up endless possibilities: Mobile Cloud Computing (MCC) [8], Cloud-based Mobile Augmentation (CMA) [6], and Image Recognition processing i.e. mobile landmark recognition system [7].…”

Section: B Mobile Applicationmentioning

confidence: 99%

Towards a Mobile Assistive System Using Service-Oriented Architecture

Triboan

2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)

2016

As the aging population grows, age-related diseases show a cohesive increase. Ambient Assistive Living (AAL) systems are being developed and are continually evolving in various areas. While most researchers focus on robust Activity Recognition techniques, this paper investigates some of the architectural challenges of the AAL systems. This study proposes a new system architecture that brings together the service-oriented architecture (SOA), Semantic Web technologies and other methods to address some of the shortfalls in the predecessor system implementations using off-the-shelf and open source components. A partial system implementation is then presented using the proposed system architecture. The system takes some of the key design aspects such as extensibility, reusability, scalability, and maintainability into consideration that can then be seen as a base to further extend the capability of monitoring, collecting, processing and accurately recognising complex or concurrent activities in its overall aim to support assistive living.

“…In the future these can be used for better contextual data collection as well as better usability of the system. In addition, delegating resource-intensive tasks to cloud-based service approaches can not only further increase the capabilities of smartphones but also open up endless possibilities, such as Mobile Cloud Computing (MCC) [45], Cloudbased Mobile Augmentation (CMA) [46], and Image Recognition processing (i.e., mobile landmark recognition systems) [47]. The old browser-based applications in previous system implementations make a system less accessible to its users.…”

Section: Mobile Applicationsmentioning

confidence: 99%

Towards a service-oriented architecture for a mobile assistive system with real-time environmental sensing

Triboan

et al. 2016

Tinshhua Sci. Technol.

With the growing aging population, age-related diseases have increased considerably over the years.In response to these, ambient assistive living (AAL) systems are being developed and are continually evolving to enrich and support independent living. While most researchers investigate robust activity recognition (AR) techniques, this paper focuses on some of the architectural challenges of the AAL systems. This work proposes a system architecture that fuses varying software design patterns and integrates readily available hardware devices to create wireless sensor networks for real-time applications. The system architecture brings together the serviceoriented architecture (SOA), semantic web technologies, and other methods to address some of the shortcomings of the preceding system implementations using off-the-shelf and open source components. In order to validate the proposed architecture, a prototype is developed and tested positively to recognize basic user activities in real time.The system provides a base that can be further extended in many areas of AAL systems, including composite AR.