Chien Aun Chan

TL;DR: This work proposes a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles, and uses the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications.

TL;DR: It is experimentally characterize and analytically show that the fast settling time and low power consumption during active and power-saving modes allow the VCSEL-ONU to achieve significant energy savings over the distributed feedback laser (DFB) based ONU.

TL;DR: A set of models for assessing the use-phase power consumption and carbon dioxide emissions of telecom network services to help telecom providers gain a better understanding of the GHG emissions associated with the energy required for their networks and services are developed.

TL;DR: A new Personalized Edge Caching System (PECS) architecture is proposed that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network and provides guidance on how key technologies of PECS can be employed for current and future networks.

TL;DR: This work introduces the proposed proactive caching (PC) algorithm for mobile edge caching with Zipf request patterns, which could potentially improve the cache hit rates compared to other caching algorithms such as least recently used, least frequently used, and popularity-based caching.