Dirk Pesch

Andrew Nash, Dirk Pesch, Krishnendu Guha

With the ever-increasing range of applications of Internet in Things (IoT) and sensor networks, challenges are emerging in various categories of classification tasks. Applications such as vehicular networking, UAV swarm coordination and cyber-physical systems require global classification over distributed sensors, with tight constraints on communication and computation resources. There has been much research in decentralized and distributed data-exchange for communication-efficient collective inference. Likewise, there has been considerable research involving the use of cloud and edge computing paradigms for efficient task allocation. To the best of our knowledge, there has been no research on the integration of these two concepts to create a hybrid cloud and distributed approach that makes dynamic runtime communication strategy decisions. In this paper, we focus on aspects of combining distributed and hierarchical communication and classification approaches for collective inference. We derive optimal policies for agents that implement this hybrid approach, and evaluate their performance under various scenarios of the distribution of underlying data. Our analysis shows that this approach can maintain a high level of classification accuracy (comparable to that of centralised joint inference over all data), at reduced theoretical communication cost. We expect there is potential for our approach to facilitate efficient collective inference for real-world applications, including instances that involves more complex underlying data distributions.

Mohamed Seliem, Utz Roedig, Cormac Sreenan et al.

The integration of 5G with IEEE 802.1 Time-Sensitive Networking (TSN) is essential for enabling flexible and mobile deterministic communication in industrial automation. The 3GPP Release 16 specification defines a bridge architecture where the 5G system operates as a transparent TSN bridge, incorporating Network-side and Device-side TSN Translators (NW-TT, DS-TT), a TSN Application Function, and QoS mapping between TSN Priority Code Points and 5G QoS Flow Identifiers. However, existing simulation frameworks model only subsets of this architecture, either QoS mapping without time synchronization, or time synchronization without data plane traffic, and none implements the complete QoS pipeline through the 3GPP SDAP layer with per-flow Data Radio Bearer selection. We present nascTime[20], an open simulation framework built on OMNeT 6.3, INET 4.6, and Simu5G that implements the complete 3GPP Release 16 5G-TSN bridge model. The framework provides end-to-end QoS mapping from TSN PCP through to 5G QFI via the SDAP/DRB pipeline, IEEE 802.1AS transparent clock behavior with measured residence time correction through L2-in-GTP-U gPTP transport, and multi-endpoint scaling with bidirectional traffic. The bridge ports integrate with INET's LayeredEthernetInterface and streaming PHY for compatibility with TSN features including Time-Aware Shaping and frame preemption. We validate nascTime with a three-endpoint factory scenario demonstrating near-perfect packet delivery across two traffic classes, correct gPTP synchronization with residence time correction, and zero packet loss. nascTime is the first simulation framework to model the full 5G-TSN bridge data path with SDAP-based QoS differentiation and measured IEEE 802.1AS transparent clock behavior in a multi-endpoint topology.

Sahraoui Dhelim, Liming Luke Chen, Huansheng Ning et al.

Online social media provides a channel for monitoring people's social behaviors and their mental distress. Due to the restrictions imposed by COVID-19 people are increasingly using online social networks to express their feelings. Consequently, there is a significant amount of diverse user-generated social media content. However, COVID-19 pandemic has changed the way we live, study, socialize and recreate and this has affected our well-being and mental health problems. There are growing researches that leverage online social media analysis to detect and assess user's mental status. In this paper, we survey the literature of social media analysis for mental disorders detection, with a special focus on the studies conducted in the context of COVID-19 during 2020-2021. Firstly, we classify the surveyed studies in terms of feature extraction types, varying from language usage patterns to aesthetic preferences and online behaviors. Secondly, we explore detection methods used for mental disorders detection including machine learning and deep learning detection methods. Finally, we discuss the challenges of mental disorder detection using social media data, including the privacy and ethical concerns, as well as the technical challenges of scaling and deploying such systems at large scales, and discuss the learnt lessons over the last few years.

Leila Sedghi, Zohaib Ijaz, Md. Noor-A-Rahim et al.

Networked control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world wireless networked control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.