Artificial intelligence for cognitive health assessment: State-of-the-art, open challenges and future directions
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led
many researchers to explore ways to automate the process to make it more objective and to …
many researchers to explore ways to automate the process to make it more objective and to …
Learning IoT in edge: Deep learning for the Internet of Things with edge computing
Deep learning is a promising approach for extracting accurate information from raw sensor
data from IoT devices deployed in complex environments. Because of its multilayer structure …
data from IoT devices deployed in complex environments. Because of its multilayer structure …
Collaborative computation offloading for multiaccess edge computing over fiber–wireless networks
By offloading the computation tasks of the mobile devices (MDs) to the edge server, mobile-
edge computing (MEC) provides a new paradigm to meet the increasing computation …
edge computing (MEC) provides a new paradigm to meet the increasing computation …
Mobile-edge computation offloading for ultradense IoT networks
The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the
deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT …
deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT …
Enforcing position-based confidentiality with machine learning paradigm through mobile edge computing in real-time industrial informatics
AK Sangaiah, DV Medhane, T Han… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Position-based services (PBSs) that deliver networked amenities based on roaming user's
positions have become progressively popular with the propagation of smart mobile devices …
positions have become progressively popular with the propagation of smart mobile devices …
Joint DNN partition deployment and resource allocation for delay-sensitive deep learning inference in IoT
Nowadays, the widely used Internet-of-Things (IoT) mobile devices (MDs) generate huge
volumes of data, which need analyzing and extracting accurate information in real time by …
volumes of data, which need analyzing and extracting accurate information in real time by …
Energy efficiency on fully cloudified mobile networks: Survey, challenges, and open issues
A Alnoman, GHS Carvalho… - … Surveys & Tutorials, 2017 - ieeexplore.ieee.org
Fully cloudified mobile network infrastructure, which is featured by the joint deployment of
heterogeneous cloud radio access networks and edge computing, will successfully cope …
heterogeneous cloud radio access networks and edge computing, will successfully cope …
Blind detection: Advanced techniques for WiFi-based drone surveillance
The great availability of commercial drones has raised growing interest among people, since
remotely piloted vehicles can be employed in numerous applications. The pervasive use of …
remotely piloted vehicles can be employed in numerous applications. The pervasive use of …
A novel non-supervised deep-learning-based network traffic control method for software defined wireless networks
SDN has been regarded as the next-generation network paradigm as it decouples complex
network management from packet forwarding, which significantly simplifies the operation of …
network management from packet forwarding, which significantly simplifies the operation of …
Energy-aware computation offloading and transmit power allocation in ultradense IoT networks
To meet the surging demands on network throughput and spectrum resources arising with
billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to …
billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to …