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.

This paper proposes gradient routing with two-hop information for industrial wireless sensor networks to enhance real-time performance with energy efficiency. Two-hop information routing is adopted from the two-hop velocity-based routing, and the proposed routing algorithm is based on the number of hops to the sink instead of distance. Additionally, an acknowledgment control scheme reduces energy consumption and computational complexity. The simulation results show a reduction in end-to-end delay and enhanced energy efficiency.

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.

This paper proposes a routing algorithm that enhances throughput and decreases end-to-end delay in industrial cognitive radio sensor networks (ICRSNs) based on ISA100.11a. In ICRSNs, the throughput is downgraded by interference from primary networks. The proposed routing algorithm is targeted at large-scale networks where data are forwarded through different clusters on their way to the sink. By estimating the maximum throughput for each path, the data can be forwarded through the most optimal path. Simulation results show that our scheme can enhance throughput and decrease end-to-end delay.

Network Function Virtualization (NFV) and service orchestration simplify the deployment and management of network and telecommunication services. The deployment of these services require, typically, the allocation of Virtual Network Function - Forwarding Graph (VNF-FG), which implies not only the fulfillment of the service's requirements in terms of Quality of Service (QoS), but also considering the constraints of the underlying infrastructure. This topic has been well-studied in existing literature, however, its complexity and uncertainty unveil many challenges for researchers and engineers. This issue is especially complex when it comes to placing a service on several non-cooperative domains, where the network operators hide their infrastructure to other competing domains. In this paper, we address these problems by proposing a deep reinforcement learning based VNF-FG embedding approach. The results provide insights into behaviors of non-cooperative domains. They also show the efficiency of proposed VNF-FG deployment approach having automatic inter-domain load balancing.