Qihui Wu

TL;DR: A literature survey of the latest advances in researches on machine learning for big data processing finds some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning.

TL;DR: In this paper, a cognitive Internet of Things (CIoT) paradigm is proposed to enable general objects to learn, think, and understand both physical and social worlds by themselves.

TL;DR: This paper proposes an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning, and provides a systematic tutorial on key enabling techniques involved in the cognitive tasks.

TL;DR: It is shown that with the proposed games, global optimization is achieved with local information, specifically, the local altruistic game maximized the network throughput and the local congestion game minimizes the network collision level.

TL;DR: In this article, the authors propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance, and provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where an important event is being held and only authorized drones are allowed to fly over.