HPC cloud for scientific and business applications: taxonomy, vision, and research challenges

MAS Netto, RN Calheiros, ER Rodrigues… - ACM Computing …, 2018 - dl.acm.org
High performance computing (HPC) clouds are becoming an alternative to on-premise
clusters for executing scientific applications and business analytics services. Most research …

Homecare robotic systems for healthcare 4.0: Visions and enabling technologies

G Yang, Z Pang, MJ Deen, M Dong… - IEEE journal of …, 2020 - ieeexplore.ieee.org
Powered by the technologies that have originated from manufacturing, the fourth revolution
of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution …

An efficient deep learning model to predict cloud workload for industry informatics

Q Zhang, LT Yang, Z Yan, Z Chen… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Deep learning, as the most important architecture of current computational intelligence,
achieves super performance to predict the cloud workload for industry informatics. However …

When weather matters: IoT-based electrical load forecasting for smart grid

L Li, K Ota, M Dong - IEEE Communications Magazine, 2017 - ieeexplore.ieee.org
Electrical load forecasting is still a challenging open problem due to the complex and
variable influences (eg, weather and time). Although, with the recent development of IoT and …

A user-centric data protection method for cloud storage based on invertible DWT

H Qiu, H Noura, M Qiu, Z Ming… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Protection on end users' data stored in Cloud servers becomes an important issue in today's
Cloud environments. In this paper, we present a novel data protection method combining …

Energy-efficient scheduling for real-time systems based on deep Q-learning model

Q Zhang, M Lin, LT Yang, Z Chen… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Energy saving is a critical and challenging issue for real-time systems in embedded devices
because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic …

Deep reinforcement scheduling for mobile crowdsensing in fog computing

H Li, K Ota, M Dong - ACM Transactions on Internet Technology (TOIT), 2019 - dl.acm.org
Mobile crowdsensing becomes a promising technology for the emerging Internet of Things
(IoT) applications in smart environments. Fog computing is enabling a new breed of IoT …

An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing

Q Zhang, LT Yang, Z Chen, P Li… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Deep computation, as an advanced machine learning model, has achieved the state-of-the-
art performance for feature learning on big data in industrial Internet of Things (IoT) …

-MPTCP: A Learning-Driven Latency-Aware Multipath Transport Scheme for Industrial Internet Applications

Y Cao, R Ji, L Ji, G Lei, H Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With various industrial wireless networks greeting booming development, modern industrial
devices configured with several network interfaces increasingly become the norm. Such …

Privacy-preserving double-projection deep computation model with crowdsourcing on cloud for big data feature learning

Q Zhang, LT Yang, Z Chen, P Li… - IEEE Internet of Things …, 2017 - ieeexplore.ieee.org
Recent years have witness a considerable advance of Internet of Things with the
tremendous progress of communication theories and sensing technologies. A large number …