In the Vehicle to Vehicle (V2V) communication based on Optical Camera Communication (OCC), optical signals are transmitted using LED arrays and received employing cameras. In a complex scene, how to accurately detect and recognize LEDs in real time remains a problem. To solve this problem, this paper designs an end-to-end network based on You Only Look Once version 5 (YOLOv5) object detection model, which can precisely detect the LED array position in real time and alleviate motion blur simultaneously. Further, we propose an LED segmentation recognition method, which is beneficial to more reliable LED status recognition. It allows more light sources to be used for communication, which can effectively improve data rate in the vehicle OCC system. The effectiveness of our method is demonstrated by theoretical analysis and experiments in real traffic scenes. Our code is available at https://github.com/cq100/D2Net.

The underutilized resources from nearby wired devices can be shared to compute for the restricted mobile devices at the edge of network. In this letter, we propose the share-based edge computing (SEC) paradigm with the mobile-to-wired (M2W) offloading computing. By sharing the idle resources under control of SEC servers, tasks from mobile devices can be computed on wired devices. Moreover, a well-designed computing allocation and sharing (CAS) algorithm is proposed to ensure the low fail ratio and the high utilization ratio of the SEC paradigm. Numerical results demonstrate the SEC paradigm has a better performance compared to local computing and mobile edge computing (MEC).

The overwhelming data rates in next generation wireless networks impose a burden on the high-capacity network planning. One promising strategy to meet the demand for high-capacity communications is to augment radio frequency (RF) based multi-channel multi-radio (MCMR) wireless mesh network (WMN) by free-space optics (FSO). In this paper, we construct a hybrid RF/FSO MCMR WMN topology and address its resource allocation (RA) problem in terms of interface assignment, channel allocation, routing, FSO link allocation, and topology control. Considering the weather effects on FSO link availability and the fading nature of RF links, the RA problem is formulated as a two-stage optimization problem with the objective of maximizing the network throughput. In our optimization model, we formulate each stage as a mixed integer linear program, and the bottleneck RF links are gradually upgraded by FSO links. To avoid the computational complexity of the second stage optimization, an improved iterated local search algorithm is proposed. Simulation results show that our RA scheme is efficient and the throughput can be enhanced dramatically by proper FSO link augmentation.

In the multi-server multi-access edge computing (MEC) system, the computing capacity could be insufficient and the computing resource could be over-utilized when an excessive number of computing tasks are offloaded for execution. To alleviate the excessive burden in the multi-server MEC system and tap the underutilized resources of wired edge devices, which are called edge computers in this paper, we investigate the computation offloading and shunting scheme in the wireless wireline internetwork. We formulate an optimization problem to minimize the average total time delay and use the deep reinforcement learning method to obtain the optimal shunting policy. By sensing the state of the wireless wireline internetwork, the proposed scheme can adaptively adjust the shunting ratio and utilize the resources of MEC servers and edge computers intelligently and jointly. Finally, we evaluate the computational complexity and validate the convergence property of this scheme. The extensive simulation results demonstrate that this scheme can improve the resource productivity and reduce the average total time delay.

Natural disasters often destroy critical infrastructure, such as terrestrial communication networks and transportation routes, thereby severely disrupting post-disaster rescue operations. To rapidly re-establish communication links and provide flexible computational support in disaster rescue scenarios, the integration of autonomous aerial vehicles (AAVs) and mobile edge computing (MEC) has emerged as a promising solution. Nevertheless, the highly complex and resource-constrained characteristics of disaster environments pose significant challenges for AAV-assisted computation task offloading. In this article, we investigate the joint task offloading and resource allocation (JTORA) problem to minimize the energy consumption associated with communication and computation during task offloading. Specifically, we develop a twin-delayed deep deterministic policy gradient (TD3)-based JTORA (JTORA-TD3) algorithm, which enables the AAV to optimize decisions of task offloading and resource allocation intelligently. To further enhance the training efficiency of the JTORA-TD3 algorithm in a complex disaster rescue environment, we integrate a large AI model (LAM) into the TD3 framework. Based on the textual interaction, we propose an LAM-enabled TD3-based JTORA (JTORA-LAM4TD3) algorithm. Simulation results demonstrate that the proposed JTORA-LAM4TD3 algorithm significantly outperforms baselines. These findings confirm the effectiveness of integrating LAMs with deep reinforcement learning (DRL) for solving the decision optimization problem.