Vehicular fog computing (VFC) that supports inter-vehicular task offloading emerges as a promising complement to handle the explosive growth of computation-intensive tasks in Intelligent Transportation Systems (ITS). Nonetheless, as the fog access points (F-APs) in crowed areas are often overloaded, the conventional single F-AP VFC may become incompetent and energy-inefficient. To tackle the issue, a novel scheme of non-orthogonal multiple access (NOMA)-enabled multi-F-AP VFC with partial offloading is proposed in this work. However, the corresponding energy minimization turns out to be a highly non-trivial non-linear mixed-integer programming problem. To this end, the optimal power allocation is derived by exploiting monotonicity while good task splitting ratio and user association are found through successive convex approximation (SCA)-based interior-point method and game theoretic approach, respectively. Extensive simulations based on MATLAB show that, in the considered scenarios, the proposed scheme can fulfill a more balanced offloading and better exploit the available computing resources, thereby leading to an approximately 30% energy consumption reduction compared to the baselines.

The Internet of Things (IoT) in the sixth generation (6G) network is envisioned to evolve towards intelligence, ubiquity, and self-optimization. Large language models (LLMs) have demonstrated remarkable generalization capabilities across diverse domains, including natural language processing, computer vision, etc. In this article, we propose an LLM-empowered IoT architecture for 6G networks to achieve intelligent autonomy and facilitate diversified IoT applications. On one hand, LLM-based solutions are tailored to satisfy the quality of service requirements of IoT applications, i.e., LLM for 6G IoT. On the other hand, LLMs are expected to be deployed in IoT environments through edge fine-tuning methods and edge inference techniques to support IoT applications, i.e., LLM on 6G IoT. Furthermore, we propose a memory-efficient split federated learning framework for LLM fine-tuning on heterogeneous IoT devices to alleviate memory consumption on both IoT devices and the edge server. Finally, a case study is presented, followed by a discussion about open issues.

In recent years, various maritime applications such as unmanned surface vehicles, marine environment monitoring, target tracking, and emergency response have developed rapidly in maritime communication networks (MCNs), and these applications are often accompanied by complex computation tasks and low latency requirements. However, due to the limited resources of the vessels, it is critical to design an efficient mobile edge computing (MEC) enabled network for maritime computation. Inspired by this motivation, energy harvesting space-air-sea integrated networks (EH-SASINs) for maritime computation tasks offloading are proposed in this paper. We first make the optimal deployment of tethered aerostats (TAs) with the K-means method. In addition, we study the issue of computation task offloading for vessels, focusing on minimizing the process delay of computation task based on the proposed architecture. Finally, because of the NP-hard properties of the optimization problem, we solve it in two stages and propose an improved water-filling algorithm based on queuing theory. Simulation results show that the proposed EH-SASINs and algorithms outperform the existing scenarios and can reduce about 50% of the latency compared with local computation.

The proliferation of Internet of Things (IoT)-generated distributed personal data enables user-specific large language model (LLM) adaptation at the edge. The split federated learning (SFL) facilitates collaborative learning and reduces memory footprint by model splitting, which necessitates the transmission of intermediate activations, rendering it susceptible to reconstruction attacks and privacy breaches. In this article, we present a privacy-aware SFL scheme addressing the accuracy-efficiency-privacy trilemma in LLM fine-tuning over heterogeneous IoT devices. Particularly, we develop a privacy quantification metric based on Fisher information to assess layer-wise privacy risks in smashed data transmission. Guided by this metric, we establish an analytical model that captures the intricate relationships between privacy leakage, fine-tuning convergence time, and device energy consumption. To optimize these three aspects, we formulate a multiobjective mixed-integer programming problem. Then, an $\epsilon $ -constraint-based block coordinate descent (BCD) algorithm is proposed to jointly determine the optimal LLM split layer, transmit power, and bandwidth allocation for IoT devices under their memory and network constraints. Extensive simulation results demonstrate the proposed scheme’s effectiveness in achieving 24% faster convergence, 40% lower energy consumption, and 7% reduced privacy leakage compared to baseline approaches, while maintaining competitive model accuracy.

In this paper, we propose an edge-assisted split federated learning framework to facilitate large language model (LLM) fine-tuning on heterogeneous mobile devices while alleviating memory pressures on both mobile devices and the edge server. Specifically, mobile devices perform low-rank adaptation (LoRA) fine-tuning on only a subset of lower layers of the pre-trained LLM, tailored to their individual capacities. On the server, a full LLM is maintained, and the corresponding LoRA modules are selectively fine-tuned in a sequential manner for each device. To further enhance training efficiency, we propose a server-side training scheduling method that optimizes the processing order of devices for accelerating fine-tuning. Extensive experiments demonstrate that compared to the baselines, our scheme can reduce 79% memory footprint and 6% training time while achieving comparable performance.

Device-to-device (D2D) communication is one of the most promising technologies for relieving the pressure of demands in the 5G mobile networks. However, due to randomness of user request, limitation of storage space and transmission capacity, it is still a challenge for channel allocation to optimize the successful delivery ratio of contents. Thus, in this article, we first consider the link selection problem for mobile users in multi-D2D (MD2D)-based content delivery networks. We establish a content delivery utility that combines physical and social aspects, taking into account energy consumption, user mobility, and trust relationships. Second, we model the content delivery link selection as the maximum weighted matching problem. For this NP-hard problem, by relaxing the integer constraints, we propose a BnB-CDLS algorithm based on the branch-and-bound method for the proposed content delivery scheme. Furthermore, we prove that the problem can be computationally reduced to a monotone submodular problem subject to matroid and knapsack constraints, which can be solved by a greedy GA-CDLS algorithm. Numerical results show that as compared with the existing schemes, the proposed scheme can significantly improve the successful delivery ratio of contents.