With the development of artificial intelligence and Internet of Things (IoT), the era of industry 4.0 has come. According to the prediction of IBM, with the continuous popularization of 5G technology, the IoT technology will be more widely used in factories. In recent years, federated learning has become a hot topic for Industrial Internet of Things (IIoT) researchers. However, many devices in the IIoT currently have a problem of low computing power, so these devices cannot perform well facing the tasks of training and updating models in federated learning. In order to solve the above problems, we introduce edge computing into the IIot, so that the device can complete the federated learning operation. In order to ensure the security of data transmission, blockchain is introduced as the main algorithm of equipment authentication in the system. What's more, in order to increase the efficiency and versatility of training model in IIoT, we introduce transfer learning to improve the system performance. The experimental results show that our algorithm can achieve high security and high training accuracy.

The networking of virtual reality applications will play an important role in the emerging global Internet of Things (IoT) framework and it is expected to provide the foundation of the expected 5G tactile Internet ecosystem. However, considerable challenges are ahead in terms of technological constraints and infrastructure costs. The raw data rate (5 Gbps–60 Gbps) required achieving an online immersive experience that is indistinguishable from real life vastly exceeds the capabilities of future broadband networks. Therefore, simply providing high bandwidth is insufficient in compensating for this difference, because the demands for scale and supply vary widely. This requires exploring holistic solutions that exceed the traditional network domain, and integrating virtual reality (VR) data capture, encoding, network, and user navigation. Emerging services are extremely inefficient in terms of mass use and data management, which significantly reduces the user experience, due to their heuristic design choices. Other key aspects must be considered, such as wireless operation, ultra-low latency, client/network access, system deployment, edge computing/cache, and end-to-end reliability. A vast number of high-quality works have been published in this area and they will be highlighted in this survey. In addition to a thorough summary of recent progress, we also present an outlook of future developments in the quality of immersive experience networks and unified data set measurement in VR video transmission, focusing on the expansion of VR applications, security issues, and business issues, which have not yet been addressed, and the technical challenges that have not yet been completely solved. We hope that this paper will help researchers and developers to gain a better understanding of the state of research and development in VR.

With the advent of the information age, VR video streaming services have emerged in large numbers in scenarios such as immersive entertainment, smart education, and the Internet of Vehicles. People are also demanding an increasing number of virtual-reality (VR) services, and service providers must ensure a good user experience. Therefore, the quality of the VR user’s experience is receiving increasing attention from academia and industry. The review in this paper focuses on a comprehensive summary of the current state of quality-of-experience (QoE) technologies applied to VR video streaming. First, we review the main influencing factors of QoE and VR video streaming. Second, the user QoE for VR evaluation is discussed. Third, the modeling of QoE for VR video streaming, the QoE-oriented VR optimization problem, and enabling techniques of machine learning for VR video streaming improvement are summarized. Lastly, we present current challenges and possible future research directions.

Most of existing virtual network embedding (VNE) algorithms only consider how to construct virtual networks more efficiently on a physical infrastructure, without considering the possibility that the constructed virtual networks may be further virtualized to multiple smaller ones. We define the former scenario as single-layer VNE and the later as multi-layer VNE. As the increasing popularity of deploying large datacenter networks and wide area networks with Software Defined Network (SDN) architectures, it becomes a new requirement and possibility to provide multi-layer encapsulated network services for large tenants who have hierarchical organizational structures or need fine-grained service isolation. However, existing VNE algorithm are not specifically designed for the above requirement and not flexible enough to deal with mapping virtual network requirements (VNRs) to a physical network and smaller VNRs to a mapped virtual network. In this paper, we aim to propose a unified and flexible multi-layer VNE algorithm combining with reinforcement learning to solve the embedding of multi-layer VNRs, which can better distinguish the differences between VNRs and physical networks. Simulation results show that our algorithm achieves good performance both in single-layer and multi-layer VNE scenarios.

The attacks, faults, and severe communication/ system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Depending on the type of data corruption, random or successive/mass, we design two versions of LightLRFMS. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 20 times faster speed thanks to its lower computation cost.

Xuewen Yang, Yingru Liu, Dongliang Xie, Xin Wang, Niranjan Balasubramanian. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.