• Trust as a new intent for orchestrating the future 6G networks. • Continuous network assurance for cloud continuum services. • The Level of Trust framework monitors and double checks trust level agreements. • Trust-oriented ontology for Cloud Continuum and intent-based scenarios. Intent-Based Networking (IBN) has gained prominence from both industry and research communities for boosting network automation and reducing network complexity in multi-stakeholder scenarios. IBN helps service and network providers understand and translate high-level business goals by escaping from technical details. Nevertheless, the rapid evolution of service requirements in dynamic multi-stakeholder environments needs to be considered by suppliers in order to meet the continuous demands of their customers. In this regard, trustworthiness is recognized as a key feature to assess reliability, compliance, and user perception of 6G services. In that spirit, this article aims to pave the way for encompassing trustworthiness, or Level of Trust (LoT), as a new intent to properly deliver service provisioning in 6G solutions. In particular, the LoT framework introduces a set of functionalities to guarantee a proper definition of requirements (Trust Level Agreements, TLAs), their interpretation, translation, consistency, and assurance during an ongoing relationship. To this end, this article presents a trust-based intent management approach with an ad-hoc interpreter that is, in turn, powered by a trust-based ontology for Cloud Continuum scenarios. Besides, a Level of Trust Assessment Function (LoTAF) is designed to monitor and report updates and events regarding LoT continuously. Such a monitoring engine follows the IETF basics of service assurance for IBN architecture. Last but not least, experiments in a collaborative robot warehouse scenario showcase that LoTAF enables workload allocation to the most trustworthy compute nodes, improving confidence in service orchestration, while introducing negligible overheads (1.40 % RAM, 5.01 % CPU, and 0.1069s latency).

The evolution towards sixth generation (6G) wireless networks necessitates innovative approaches to achieve ubiquitous connectivity, especially in remote and underserved regions. Traditional mobile network operator (MNO) infrastructures often fall short in addressing the dynamic demands of 6G applications, such as extended reality (XR), virtual reality (VR), and massive Internet of Things (IoT) deployments. This paper introduces a novel framework for the on-demand formulation of flexible, unstructured network topologies leveraging various devices, including Unmanned Aerial Vehicles (UAVs). We validate our framework through two distinct use cases: UC1 for outdoor, remote setups, and UC2 for indoor, capacity-heavy warehouse inventory management. Central to our approach is the concept of trustworthiness, which extends beyond security to encompass capacity, energy efficiency, performance, and cost. We present a comprehensive system model that emphasizes the exposure and utilization of critical network metrics, integrating AI/ML-driven resource optimization. Our results demonstrate significant improvements in deployment cost, energy consumption, and network efficiency compared to static infrastructures. Additionally, we address key challenges related to node selection, trust management, and realtime adaptability.

Wireless communication has profoundly transformed the way we experience the world. For instance, at most events, attendees commonly utilize their smartphones to document and share their experiences. This shift in user behavior largely stems from the cellular network’s capacity for communication. However, as networks become increasingly sophisticated, new opportunities arise to leverage the network for services beyond mere communication, collectively termed Beyond Communication Services (BCS). These services encompass joint communications and sensing, network as a service, and distributed computing. This paper presents examples of BCS and identifies the enablers necessary to facilitate their realization in sixth generation (6 G). These enablers encompass exposing data and network capabilities, optimizing protocols and procedures for BCS, optimizing compute offloading protocols and signalling, and employing application and device-driven optimization strategies.

Network traffic anomalies can have a detrimental effect on end-to-end network performance and reliability, compromising severely (demanding) services and applications, such as those offered by the fifth-generation (5G) mobile networks and beyond. To prevent the network drift towards increasingly inefficient operating modes, such traffic anomalies need to be detected first, so that the proper actions be taken to avoid such undesirable network drift. This paper proposes a novel framework focusing on proactively detecting such traffic anomalies, that is, predicting upcoming network traffic anomalies before they actually occur. To this end, two mechanisms are developed: a mechanism aiming at identifying different network traffic behaviors in an automated manner; and a mechanism for predicting network traffic behaviors for the next time interval spanning several seconds. The automated network traffic anomaly identification is realized via the application of clustering and decision tree-based learning. A time series model (specifically, a Bidirectional Long Short Term Memory (BiLSTM) Autoencoder) is employed for the proactive detection of forthcoming network traffic behaviors. Evaluation results are derived showing a prediction accuracy of up to 90.02%, demonstrating the effectiveness and viability of the proposed framework, as well as its potential for higher effectiveness compared to a state-of-the-art solution.

Beyond 5G networks bring a new era in system automation, by introducing new and demanding, in terms of Quality of Service (QoS), use cases and applications. Predicting the QoS for end users in a timely manner and enabling service adaptation methods to react in advance in case of QoS degradation is of high importance, especially for safety-critical applications such as in vehicular communications. Current state-of-the-art approaches propose solutions towards the identification of potential QoS deterioration in a centralized manner. However, centralized solutions may raise privacy issues, since sensitive user information may need to be transmitted to communication network entities for processing and analysis. Other practical limitations of centralized solutions may also arise, such as the computational bottleneck and the fast increase of signaling overhead with number of end users. This study proposes a distributed QoS prediction scheme based on the well-known Long Short-Term Memory (LSTM) architecture to account for the natural high correlation of samples closely located in time. The primary target of the proposed scheme is to provide accurate QoS predictions up to several seconds, while preserving data privacy and reducing signaling overheads related to the exchange of information between the involved nodes. The evaluation of the proposed scheme indicates its potential gains and effectiveness compared to centralized state-of-the-art OoS prediction solutions.

In the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the - often scarce - computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework.

The design of an efficient radio access selection mechanism for 5G cellular networks is of paramount importance. Several proposals exist in the literature, but up to now the deployed systems are still using simple mechanisms mainly related to the evaluation of the RSS to make a handover decision. However, this is an inadequate solution for 5G networks. In this paper, we describe a novel multi-criteria handover scheme, we provide details on solutions for acquiring the necessary contextual information and we describe the algorithm to select the most appropriate RAT. Our solution is based on the use of fuzzy logic controllers for combining diverse inputs (such as a user's mobility, the load of the candidate base stations etc.) The efficiency of our mechanism is evaluated through appropriate simulations. The results related to throughput, delay and the number of the executed handovers clearly show the merits of our proposal when compared to a well-established LTE handover algorithm.