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.

Network Function Virtualization (NFV) will simplify deployment and management of network and telecommunication services. NFV provides flexibility by virtualizing the network functions and moving them to a virtualization platform. In order to achieve its full potential, NFV is being extended to mobile or wireless networks by considering virtualization of radio functions. A typical network service setup requires the allocation of a Virtual Network Function -Forwarding Graph (VNF-FG). A VNF-FG is allocated considering the resource constraints of the lower infrastructure. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering also VNF migration. Then we formulate the optimization problem as an Integer Linear Programming (ILP) and provide a heuristic algorithm for allocating multiple VNF-FGs. The idea is that VNF-FGs can be reallocated dynamically to obtain the optimal solution over time. First, a centralized optimization approach is proposed to cope with the ILP-resource allocation problem. Next, a decentralized optimization approach is proposed to deal with cooperative multi-operator scenarios. We adopt AD3, an ADMM-based algorithm, to solve this problem in a distributed way. The results confirm that the proposed algorithms are able to optimize the network utilization, while limiting the number of reallocations of VNFs which could interrupt network services.

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.

In the present letter, a closed-form framework is derived for the system-level modelling, analysis and optimization of the energy efficiency (EE) in LoRa networks. The proposed framework is derived by exploiting stochastic geometry tools and associates the Long Range (LoRa) EE with the density of end-devices (EDs) and the ED transmit power. The analysis reveals the trends of the EE curve with respect to each of the two parameters while revealing the existence of an optimal transmit power that maximizes the EE. Thus, the proposed framework arises as a valuable tool, of significant practical value, for the optimization of the receiver/transmitter design, and of the network deployment in LoRa networks. The robustness of the framework is verified even at the asymptotic cases of fully-loaded or sparsely-loaded LoRa networks.

As a result of the increasing number of machine-type devices connected through the Internet, several challenges remain to date to support machine-to-machine (M2M) communications over long term evolution (LTE) cellular networks. In this paper, we tackle one important challenge of scheduling M2M traffic in uplink over LTE-M mobile networks. We propose a novel cross-layer scheme that considers packet scheduling in time and frequency domain using a memetic-based algorithm and aims to optimize resource allocation of M2M devices by considering their quality of service (QoS) needs while minimizing at the same time their energy consumption. After integrating an energy module for LTE in NS3 simulator, we perform simulations in a realistic M2M scenario and we evaluate the results which show how our proposed scheduling scheme outperforms other existing scheduling methods from the literature such as round robin and proportional-fair algorithms in terms of throughput, energy consumption and the percentage of satisfied devices with regard to their delay and throughput requirements.