DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices.

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible.

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices.

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely , for the early detection of sepsis using electronic health records.

5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems.

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g.

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue.

IMSC-EIoTD: Identity Management and Secure Communication for Edge IoT Devices.

The Internet of things (IoT) will accommodate several billions of devices to the Internet to enhance human society as well as to improve the quality of living. A huge number of sensors, actuators, gateways, servers, and related end-user applications will be connected to the Internet. All these entities require identities to communicate with each other.

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence.

A Blockchain-Assisted Intelligent Transportation System Promoting Data Services with Privacy Protection.

Being able to obtain various environmental and driving data from vehicles is becoming more and more important for current and future intelligent transportation systems (ITSs) to operate efficiently and economically. However, the limitations of privacy protection and security of the current ITSs are hindering users and vehicles from providing data. In this paper, we propose a new ITS architecture by using blockchain technology solving the privacy protection and security problems, and promoting users and vehicles to provide data to ITSs.