Fifth-Generation and beyond networks are envisioned to support multi-Industrial IoT (IIoT) applications with a diverse QoS requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the networkas-infrastructure transition to the network-as-service. Motivated by the increasing IIoT computational capacity, and taking into consideration the QoS satisfaction and private data sharing challenges, federated reinforcement learning (RL) has become a promising approach that distributes data acquisition and computation tasks over distributed network agents, exploiting local computation capacities and agents self-learning experiences. This paper proposes a novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks. This involves IIoT slices resource allocation in terms of Transmission Power (TP) and Spreading Factor (SF) according to the slices QoS requirements. Toward this goal, the proposed Deep Federated Q-Learning (DFQL) is reached into two main steps: First, we propose a Multi-Agent deep Q-learning (MAQL)-based dynamic slices TP and SF adjustment process that aims at maximizing selfQoS requirements in term of throughput and delay. Second, the Deep Federated Learning (DFL) is proposed to learn Multi-Agent Self model and enable them to find an optimal action decision on the TP and the SF that satisfy IIoT virtual network slice QoS reward, exploiting the shared experiences between agents. Simulation results show that the proposed DFQL framework achieves efficient performance compared to the traditional approaches.

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploying the models, the selected one achieved a confidence score of 100%. This paper also provides a comparative study of different face detection and face mask classification models. The system performance is evaluated in terms of precision, recall, F1-score, support, sensitivity, specificity, and accuracy that demonstrate the practical applicability. The system performs with F1-score of 99%, sensitivity of 99%, specificity of 99%, and an accuracy of 100%. Hence, this solution tracks the people with or without masks in a real-time scenario and ensures social distancing by generating an alarm if there is a violation in the scene or in public places. This can be used with the existing embedded camera infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates.

The future fifth generation (5G) networks are expected to support a huge number of connected devices with a various and multitude service having different Quality of Service (QoS) requirements. Communication in Industry 4.0 is one of the flagships and special applications of the 5G due to the specificity of the industrial environment as well as the variety of its services such as safety communication, robot s communications and machine monitoring. In this context, we propose a new resource allocation for the future industry 4.0 based on SDN and NFV technologies, machine learning tools and the slicing paradigm where each slice of the network is dedicated to a category of services having similar QoS requirement level. The proposed solution ensures the allocation of the resources to the slices depending on their requirement in terms of bandwidth, delay and reliability. Toward this goal, our solution is performed in three main steps: 1) IoT devices assignment to the slices step based on OGMMC algorithm, 2) Inter-slices resources reservations step based on mini batch gradient descent, 3) Intra-slices resources allocations based on the max-utility algorithm. We performed extensive simulations in a realistic industrial scenario using NS3 simulator. Numerical results show the effectiveness of our proposed solution in term of reducing Packet Error Rate (PER), energy consumption, and in terms of increasing the percentage of served devices in delay comparing to the traditional approaches.