Cyber-Physical Systems (CPS) drive Industry 4.0 by integrating physical processes with digital control and communication. While conventional deployments heavily rely on Internet of Things (IoT) sensors for monitoring and automation, these setups can incur excessive hardware overhead and fragmented data flows. In contrast, Integrated Sensing and Communication (ISAC) offers a promising alternative by reusing communication signals to perform sensing without dedicated sensor hardware. ISAC can be used in CPS to reduce overhead, improve real-time situational awareness, and enable advanced industrial applications. This paper explores how ISAC can be incorporated into a 6 G architecture for smart factories, highlighting new network functions to coordinate sensing activities and manage Sensing Data. Through a detailed use case and reference architecture, we identify key challenges, including resource allocation, data fusion, distributed computing, and coordination among 6 G entities. We then propose techniques for resource- and energy-efficient data collection and predictive sensing, demonstrating their viability via an example tracking scenario. This work provides insights into the design choices and opens research areas for future 6 G systems aiming to unify sensing and communication within CPS.

Industrial applications and Extended Reality vertical sectors have expressed the need for dedicated Quality of Service considerations from 3GPP to support time-sensitive, bursty and high throughput communications. Consequently, 3GPP enabled support for Time-Sensitive Networking in Release 17 and started specifying the concept around Packet Data Unit Sets in Release 18. This paper presents a novel solution for any IP-based communication enabling time-sensitive communication while utilising 3GPP Packet Data Unit Set feature. This paper proposes extensions to the IP header that can be utilised by any IP based network. The proposed solutions introduce the concept of a Data Unit Group to describe the entirety of an Application Data Unit and its fragmentation into individual IP packets to be delivered over a packet-switched network. This paper defines Data Unit Group Rules to communicate packet header detection and action rules to Time-Sensitive Networking switches. The rules can be used by Time-Sensitive Networking switches to prioritise and re-order/pre-empt packets and by User Equipments and User Plane Functions to write Packet Data Unit Set Markings.

This paper provides an extensive analysis of Reliable and Available Wireless (RAW) enhancements and solutions needed to manage industrial environments more effectively. Starting from the description of a representative industrial use case, an analysis of gaps and promising new extensions is performed. Namely, the need to (i) support multi-domain operation, at both technology and administrative levels; (ii) integrate RAW with edge architectures; and, (iii) increase the mobility support in RAW networks. The identified gaps are indeed not yet tackled by the relevant standardization development organizations, mainly the Internet Engineering Task Force (IETF), and are thus object of our future work.

Following the Industry 4.0 vision of a full digitization of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to-end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed.

5G promises to bring back value to the network operator and not only serve as a data pipe solution anymore. Operators have started to adopt cloud-native paradigms for the deployment of the 5G core architecture. With it goes the introduction of the service-based architecture as a key design pattern for realizing future control, and ultimately user planes of mobile networks. New design patterns offer a more flexible network and address new use cases utilising the features of a 5G system, such as slicing. We present aspects of a dynamic slicing approach as well as low-layered slicing and forwarding methodology within a service delivery platform entirely built upon service-based architecture principles.

With the technological evolution in telecommunication networks, performance requirements such as better coverage, higher bandwidth, and lower latency have been pushed to new horizons. However, as a direct result network complexity has increased dramatically over the recent years, and with this complexity manageability has suffered. This paper presents the architecture of the E-Stream project which aims to support Next Generation Operations Support Systems. E-Stream applies dimension reduction, data mining, and recommender system techniques in order to handle very high volumes of management events, identify and predict network incidents, and recommend candidate corrective actions to domain experts in Network Operations Centres.

Currently the volume of telecom network management data is expanding exponentially, mainly due to the explosive growth in the number of communicating devices along with the increase in heterogeneity of the networks. Such scale of data obsoletes the traditional approach of extracting offline analytics from the network traces governed by some pre-defined schemes. In order to increase the efficiency of the Operations Support System (OSS) and gain in-depth understanding of the generic relationship between network entities, the monitoring data needs to undergo large-scale deep analytics processing. In this paper we present i-MagNet, an integrated analytics framework developed with the popular real-time stream processing paradigm Storm. The components of i-MagNet intelligently micro-batch segments of incoming streams to enable high-throughput online analytics of management trace streams. Inter-dependence metrics (temporal and statistical) are exploited to extract contiguous event subsequences, which can then be independently examined as part of a network incident analysis system.

Monitoring and management of modern telecommunication networks has become more and more challenging due to the explosion in scale of data generated by network elements. Not only has the size of the network, the number of nodes, and the number of customers increased, but the amount and dimensionality of the data coming from each managed element has also increased. To support sophisticated monitoring and management strategies it is desirable to forward as much trace data as possible into operators' operations support systems (Operations Support Systems (OSSs)). In this paper a heuristic algorithm is presented which reduces the data-stream by removing uncorrelated noise events by determining the degree of inter-relationship between the events in the data-stream. With a sophisticated open source control plane emulator used as the source generator, the results show that the presented algorithm is capable of differentiating noise from useful information thus significantly reducing scale and dimensionality of network monitoring data-streams.

This paper presents the high level specification of a Self Configuration Framework for 802.11 multi-radio Wireless Mesh Networks based on a centralised topology discovery and channel assignment mechanism. The framework was designed to support the introduction of a new Mesh Node into the network at any time of the network's operational phase. Further, it enables a new Mesh Node to locate and communicate with the Mesh Gateway and vice versa before any routing protocol has been activated. Finally, the paper presents an estimation of time required to boot a single node and the entire multi-radio Wireless Mesh Network.

There is already a vast literature on the efficiency of the 802.11a MAC using Markov Chain models. This current literature states that the output of these mathematical models are aggregated throughput values related to the number of radios sharing the same physical medium. The study presented in this paper shows an inaccuracy in the proposed Markov Chain models; when increasing the number of radios for saturated 802.11a MAC links using the Distributed Coordination Function the models no longer return true aggregated throughput values. The evaluation process of the presented work is based on aggregated throughput, delay and delay variation results obtained from conducted simulations and experiments. It will be further shown that more than three radios which are saturating the 802.11a MAC and sharing the same medium will lead to an unacceptable network performance. The evaluation of the results is based on ITU-T recommendations for IP networks under predefined network topology assumptions.

In this paper the accuracy and distribution of measured 802.11 Receive Signal Strength values will be analysed using three different Atheros based cards. The analysis is performed using empirical methods, e.g., sample mean and Empirical Cumulative Distribution Functions, in order to demonstrate the dependency of the accuracy of Receive Signal Strength values on the used chipset and on the number of captured frames to derive the sample mean with a certain probability. A comprehensive section on the experimental environment ensures a possible repeatability of the measurements which underlines the complexity of the performed analysis at the same time.