The rapid advancement of sixth-generation (6G) networks and artificial intelligence technologies is leading to the emergence of collaborative intelligent transportation systems (C-ITS), which is regarded as an essential trend in the future of transportation. Integrating Internet of Things (IoT) with C-ITS is an efficient solution to provide real-time data collection and status monitoring for vehicles and infrastructures to improve the intelligence and reliability of C-ITS. In order to address the challenges of limited battery energy and low computing power of IoT nodes, integrating wireless power transfer (WPT) with mobile edge computing (MEC) is considered as a promising solution to improve their lifetime and computational capability for IoT nodes. In this paper, we investigate a distributed dynamic computing offloading model for an MEC-enabled C-ITS, where multiple roadside units (RSUs) collaborate to provide offloading services to wireless devices (WDs). We formulate the task offloading and bandwidth resource allocation as a distributed Stackelberg game. The WDs act as leaders, aiming to maximize their computing rate by offloading tasks to RSU or performing local computing. The RSUs act as followers, optimizing their bandwidth allocation based on the WDs’ offloading decisions, thereby improving the overall system computing rate. We prove the existence of a Stackelberg equilibrium (SE) and propose a multi-agent reinforcement learning algorithm to enable WDs to select offloading decisions and help RSUs optimize bandwidth allocation. Numerical simulation results demonstrate that the proposed scheme offers significant performance improvements over existing methods.

Deep learning (DL)-based pansharpening methods have shown great advantages in extracting spectral-spatial features from multispectral (MS) and panchromatic (PAN) images compared with traditional methods. However, most DL-based methods ignore the local inner connection between the source images and high-resolution MS (HRMS) image, which cannot fully extract spectral-spatial information, and attempt to improve the quality of fusion by increasing the complexity of the network. To solve these problems, a lightweight network based on adaptive weighted feature learning (AWFLN) is proposed for pansharpening. Specifically, a novel detail extraction model is first built by exploring the local relationship between HRMS and source images, thereby improving the accuracy of details and the interpretability of the network. Guided by this model, we then design a residual multiple receptive-field structure to fully extract spectral-spatial features of source images. In this structure, an adaptive feature learning block based on spectral-spatial interleaving attention is proposed to adaptively learn the weights of features and improve the accuracy of the extracted details. Finally, the pansharpened result is obtained by a detail injection model in AWFLN. Numerous experiments are carried out to validate the effectiveness of the proposed method. Compared to traditional and state-of-the-art methods, AWFLN performs the best both subjectively and objectively, with high efficiency. The code is available at https://github.com/yotick/AWFLN.

In this paper, we develop a novel framework to analyze the successful content delivery performance of device to device (D2D) networks with cache-enabled and self-sustained mobile helpers (MHs). Particularly, to facilitate the access of multimedia services for user equipments (UEs) under intermittent energy arrivals, an energy based content delivery strategy, namely the energy dependent nearest MH transmission (ED-NMT) strategy is considered. Under the proposed ED-NMT strategy, a MH is capable of delivering the content to the requesting UE if sufficient energy is harvested in its battery and it is the nearest MH of the tagged UE. Assuming infinite battery capacity, with ED-NMT strategy, we characterize the the energy availability and thereby the transmission probability of MHs which cache the ƒ-th file at the local storage. Then, we derive the cache-hit probability and the coverage probability of the ƒ-th file. Finally, with the obtained results, we evaluate the successful content delivery probability (SCDP) of the D2D network with cache-enabled and self-sustained MHs. It is shown through both analytical and numerical results that under certain conditions, there is no performance loss due to stochastic and intermittent energy arrivals.

The development of personalized medical systems should be supported by a fast and stable network system. The FAST TCP network system is the appropriate support system for this purpose. However, when the FAST TCP is deployed, the static mapping selection method for protocol parameters is unable to guarantee the small queuing delay and fast convergence of the network simultaneously. By conducting theoretical analysis and simulation experiments, the relationships among FAST TCP protocol slow start condition, control law gain parameters, and FAST TCP system convergence rate were examined. To ensure the stability of the FAST TCP system and to select the smallest protocol parameters, an improved method to effectively accelerate the convergence velocity of the FAST TCP system is proposed in this study. In this method, the number of packets for staying in the buffer for FAST TCP connections was taken as the criterion of the slow start, and the gain parameter of the control law was dynamically adjusted according to the local information of each FAST TCP connection. Using this improved method, the FAST TCP system can achieve a stable and small queuing delay, whilst the FAST TCP system could converge quickly to the equilibrium point simultaneously.