Mohammed Atiquzzaman

Connected and autonomous vehicles (CAVs) are increasingly functioning as mobile edge nodes in 6G vehicular networks. However, the inherent mobility of CAVs causes link fluctuations and resource variability, challenging conventional orchestration frameworks designed for static infrastructure and compromising service continuity. To address this issue, we propose Vehicle-aware Container Orchestration (VeCO), a proactive framework that combines mobility-aware prediction with lightweight online control. Specifically, the prediction module forecasts near-term link capacity and resource availability, while quantifying connectivity risks via link degradation probability. These predictions guide a risk-aware orchestration mechanism that minimizes a multi-objective cost function, jointly accounting for expected service latency, disruption penalties, and resource utilization efficiency. Trace-driven experiments show that VeCO reduces end-to-end latency by 30%–45%, cuts unnecessary migrations by up to 70%, and maintains balanced vehicle/edge utilization with low overload under dynamic vehicular workloads.

Vehicular ad hoc networks (VANETs) are becoming popular due to autonomy and intelligent transportation systems (ITSs), and they play a key role in enabling the broader concept of the Internet of Vehicles (IoV). Moreover, VANETs will have cutting-edge improvements with the emergence of 5G and 6G technologies. Recent research has concentrated on reducing delay and increasing throughput. To improve the performance of VANETs, a novel MIMO-OFDM-based cooperative vehicular safety message communication mechanism is proposed in this work. Optimal relay selection in cooperative communication is essential in maximizing system performance. This article presents an optimal relay selection mechanism based on the signal-to-noise ratio (SNR) and vehicle velocity for VANETs. First, an SNR-based relay selection mechanism is analyzed separately, and then an optimal relay selection mechanism is suggested based on vehicle velocity. To maximize the benefits of multiple-input–multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM), both are combined, and a novel access mechanism is suggested to accommodate MIMO with OFDM. A Markov chain-based mathematical model is derived. Nakagami-m fading channels are considered in the analysis. The performance is evaluated in terms of successful transmission probability, outage probability, throughput, and delay terms. Simulation of urban mobility (SUMO) generates a microscopic mobility model for practical application. For the sake of comparison and to highlight the performance achievable gains, comparisons with existing mechanisms are presented, which reveal significant improvement in performance. The simulation results reveal that significant improvements can be obtained with the proposed system relative to other existing schemes.

Low Earth Orbit (LEO) satellite networks are a promising solution for global connectivity, Internet access, disaster management, and remote sensing applications. This letter defines execution-time feasibility under delayed and partially observed telemetry based on a telemetry-consistent belief set. We show that, under bounded state evolution and non-zero execution intervals, decision-time validation is insufficient. An execution-time feasibility framework is formulated that can adapt to existing control plane mechanisms in LEO satellite networks. The simulation results show that delay and uncertainty induce violations in the decision-time validation scheme, while the proposed execution-time feasibility framework certifies that network constraints remain satisfied throughout the execution interval.

Large Language Models (LLMs) demonstrate strong potential across a variety of tasks in communications and networking due to their advanced reasoning capabilities. However, since different LLMs have different model structures and are trained using distinct corpora and methods, they may offer varying optimization strategies for the same network issues. Moreover, the potential maliciousness of its hosting device can result in LLM responses with low confidence or even bias. To address these challenges, we propose a collaborative framework that organizes distributed LLMs into an Agentic Multi-LLM Network (Agentic MultiLLMN). This novel architecture transforms individual network nodes into collaborative autonomous agents equipped with LLMs. Through agentic functionalities such as autonomous planning, reasoning, and collaborative decision-making, the framework can provide high-quality responses for complex network optimization problems. Specifically, we first review related work and highlight the limitations of existing LLMs in collaboration and trust. We then introduce a workflow of the proposed Trustworthy Agentic MultiLLMN framework, which leverages blockchain to provide a verifiable audit trail for autonomous agent interactions. Given the severity of False Base Station (FBS) attacks in wireless communications, we present FBS defense as a case study. In this scenario, each Legitimate Base Station (LBS) acts as an agentic LLM node, collaborating to optimize power allocation and mitigate attacks. The simulation shows that this framework can more effectively resist this attack compared to other solutions. Finally, we outline promising future research directions in this emerging area.

The proliferation of Internet of Things (IoT) devices has intensified the need for scalable and energy-efficient federated learning (FL). While hierarchical federated learning (HFL) improves scalability by adding an edge aggregation tier, it still suffers from high communication costs, slow convergence, and degraded accuracy under non-independent and identically distributed (non-IID) data. Existing methods, such as quantization, sparsification, and static pruning, alleviate specific bottlenecks but fail to jointly optimize efficiency, robustness, and accuracy. This article proposes an intent-driven dual-layer model pruning framework for HFL, where an energy management system (EMS) and an Intent-driven Dual-layer Pruning Orchestrator (IDPO) dynamically translate system-level intents (e.g., energy minimization or accuracy preservation) into pruning actions at both edge and cloud layers. Experiments on MNIST, CIFAR-10, and FEMNIST show up to 41% smaller models, $12\times $ faster training, 28%–35% lower energy use, and +12.9% accuracy gain under non-IID data, establishing the framework as a robust and sustainable solution for IoT learning.