The immediate adoption of deep learning models into domain-specific tasks for edge intelligence-based services still poses several challenges to overcome. The first is efficiently constructing the most suitable neural network architecture amongst the numerous types of available architectures. Once addressing this challenge, the second is understanding how to gather knowledge to build efficient neural network models from the user’s devices (i.e., smartphone) without affecting the user’s privacy. And the third critical issue is minimizing the gap between estimated and actual performance while building models. In this work, we propose a novel framework for deploying deep learning models for domain-specific tasks called MetaFed, which combines population-based meta-learning and federated learning to resolve the three challenges mentioned earlier. MetaFed autonomously constructs the potential domain-specific models with the help of population-based meta-learning by utilizing the model knowledge base. We improve the model knowledge base by using the population’s knowledge via federated learning while minimizing the performance estimation gap for model construction. Experimental results show that our proposed framework offers a promising solution for deploying deep learning models at edge devices, with a significant performance gain than the existing alternative approaches.

Across all industries, digitalization and automation are on the rise under the Industry 4.0 vision, and the forest industry is no exception. The forest industry depends on distributed flows of raw materials to the industry through various phases, wherein the typical workflow of timber loading and offloading is finding traction in using automation and 5G wireless networking technologies to enhance efficiency and reduce cost. This article presents one such ongoing effort in Sweden, Remote-Timber—demonstrating a 5G-connected teleoperation use-case within a workflow of timber terminal—and disseminates its business attractiveness as well as first measurement results on network performance. Also, it outlines future needs of the 5G network design/optimization from teleoperation perspective. Overall, the motivation of this article is to disseminate our early-stage findings and reflections to the industrial and academic communities for furthering the research and development activities in enhancing 5G networks for verticals.

Fine-grained and wide-scale connectivity is a precondition to fully digitalize the manufacturing industry. Driven by this need, new technologies such as time-sensitive networking (TSN), fifth-generation (5G) wireless networks, and industrial Internet-of-things (IIoT) are being applied to industrial communication networks to reach the desired connectivity spectrum. With TSN emerging as a wired networking solution, converging information technology (IT) and operational technology (OT) data streams, 5G is upscaling its access and core networks to function as an independent or a transparent TSN carrier in demanding OT use-cases. In this article, we discuss the drivers for future industrial wireless systems and review the role of 5G and its industrial-centric evolution towards meeting the strict performance standards of factories. We also elaborate on the 5G deployment options, including frequency spectrum allocation and private networks, to help the factory owners discern various dimensions of solution space and concerns to integrate 5G in industrial networks.

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches.