Muhammad Shuaib Siddiqui

As the limitations of the current cellular network generation have become apparent to tackle current connectivity needs in 6G, the scientific communities have started to investigate novel techniques to bolster the capabilities of the communication infrastructure. Within 6G, Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) are envisioned as key enablers to drive network performance and guarantee process integrity. However, from a security standpoint, those methods are dual-edged as they introduce a new threat surface that could be used to jeopardise the platform security, the trust in service utility and data privacy. This survey provides a consolidated review of the security, trust, and privacy impact of key 6G enabling technologies. The analysis begins by identifying the primary architectural drivers anticipated for next-generation mobile networks, systematically mapping their impact on the threat surface to identify critical resilience challenges. Conversely, we pinpoint protection methodologies enacted by these drivers, outlining concrete countermeasures that enhance the network’s security posture. Finally, we propose a unified reference architecture that integrates these benefits for holistic security, privacy, and trust management, complemented by a system-level evaluation.

In multi-automated guided vehicle (AGV) environments, inefficient service placement increases energy consumption, and charging cycles, lowering battery lifespan. Consequently, minimizing energy consumption is key for maintaining operational efficiency and sustainability. Additionally, the unpredictable arrival of service requests in multi-AGV systems can lead to system saturation. However, previous research overlooked the energy costs of on-device computation, especially under dynamic service arrivals. To address these challenges, this work proposes an energy minimization service placement algorithm (EMSPA). The results demonstrate that EMSPA outperforms a baseline random selection (RS) algorithm for different numbers of AGVs, services, and tasks per service, reducing normalized energy consumption by up to 2.34% and improving mean service acceptance rates by up to 16.09% with lineal execution time overhead. Further, EMSPA outperforms a queue-aware scheduling and deadlock mitigation strategy (QASDMS) in terms of processing power ratio by over 58.94%.

In modern novel collaborative multi-Automated Guided Vehicle (AGV) systems, vehicles are responsible for executing both mission-critical process-related operations and purely computational tasks, such as collision avoidance. This work investigates the problem of joint inter-AGV task placement and intra-AGV computational resource allocation in MEC-enabled multi-AGV environments. To address this challenge, a two-step strategy is proposed to maximize the number of scheduled and completed tasks across multiple AGVs while ensuring fair and efficient resource use within each AGV. The problem of inter-AGV task placement is solved by dynamically applying a catalog of deep reinforcement learning (DRL) models for varying numbers of AGVs. Training time for these models is reduced threefold by using datasets from existing optimization solvers. Transfer learning further reduces training times by up to 51%. Second, a multiagent deep reinforcement learning (MADRL)-based collaborative protocol for dynamic intra-AGV resource allocation (MACP-DRA) is proposed, allowing AGVs to adjust computational resources dynamically. It incorporates a minimum guaranteed share strategy to ensure fair resource distribution while optimizing performance under dynamic workloads. Compared to existing MADRL approaches, MACP-DRA enhances conflict resolution efficiency while maintaining low computational cost. Evaluation results demonstrate that the proposed inter-AGV scheduling strategy approaches optimal performance while achieving a superior trade-off between decision time and task completion rates. Compared to a multi-agent DRL baseline, the proposed MACP-DRA models reduced resource conflicts by 54.9%, task processing delays by 35.7%, and resource underutilization by 9.93%, while maintaining minimal computational and energy consumption overhead.

The recent o-ran specifications promote the evolution of ranran architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by ric entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by ai and ml, novel solutions targeting traditionally unsolved ran management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such ai/ml solutions, affecting their trust in such novel tools. xai aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the xai methods and metrics before studying their deployment over the o-ran Alliance ran architecture along with its main building blocks. We then present various use-cases and discuss the automation of xai pipelines for o-ran as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of ai/ml decision entities in this context, focusing on how xai can help to interpret, understand, and improve trust in o-ran operational networks.

AI/ML techniques play a key role in 5G/6G networks providing connectivity to IoT devices. In such scenarios, not only is it necessary to run time-sensitive applications with strict latency requirements without human intervention, but it is also key to apply automation techniques at both the application and the network levels. The chapter is composed of three sections. In the first section, we present different cloud native (CN) technologies enabling scalable, costefficient, and reliable IoT solutions. The second section details different distributed and hierarchical monitoring frameworks and metrics collection schemes as inputs to AI engines. In the last section, application placement problems focused on delay minimization in geographically distributed singlecluster environments arc first discussed. Afterwards, application placement issues ensuring latency requirements for the applications and energy consumption in distributed multi-access edge computing (MEC) systems using AI pipelines arc presented.