With 6G communication technology advancing, the demand for Radio Environment Maps (REMs) has increased due to their critical role in network optimization, resource management, and signal coverage in complex environments. However, due to the high cost of sensing, only a small number of discrete radio sampling results can be obtained, which limits their application to specific tasks. To address this problem, we propose a novel method for constructing fine-grained REMs in complex environments based on a generative diffusion model. This method leverages the spatial correlations between sparse data points while incorporating conditional information based on global spatial correlations and geographic relationships. The goal is to construct fine-grained radio environment maps from sparse coarse-grained data. Experimental results demonstrate that our model performs significantly well, achieving an absolute error of only 2.79 dB even when the sampling rate is as low as 10%.

Aquatic information monitoring is crucial for the sustainable management of water environments. Conventional interpolation methods commonly hinge on assumptions of spatial proximity or temporal similarity. However, they often fall short of capturing the intricate spatiotemporal correlations present in water quality sequences, affecting our understanding of the spatial patterns of regional water quality conditions. In this study, we propose a framework for river basin information fine-grained sensing based on deep learning, which includes a global sensing model (SGM) and a static deployment model. Inside the SGM, we adopt a multidimensional convolutional neural network (CNN) to extract spatiotemporal features and an attention mechanism to fuse these features, to infer water quality variable information on unmonitored points. Since the inference outcomes could be affected by the locations of the sensors, to minimize the inference error of the SGM, the static deployment model was designed to aid the deployment of sensors into strategic locations of a river basin to obtain optimum spatial-temporal data samples. The research results not only revealed the spatial distribution patterns of total nitrogen (TN) concentrations but also showed that the proposed method could yield a better inference performance compared to traditional interpolation methods.

As cloud computing revolutionizes various fields, the demand for scalable and flexible computing resources has grown significantly. Applications in large-scale engineering simulations, artificial intelligence, and data analysis require substantial computational power and memory, placing pressure on traditional systems. With its heterogeneous resource pools, cloud computing offers a promising solution by enabling the dynamic allocation of diverse, distributed resources. These resource pools facilitate parallel task execution, accelerate computations, and enhance system flexibility. However, efficiently managing these resources remains a complex, NP-hard challenge due to the vast search space, resource fragmentation, and the need for dynamic adjustments. In this paper, we first develop a novel multi-task flow representation model using a deep graph neural network (GNN) and a resource pool representation model based on a convolutional neural network (CNN). These models describe the dependencies among multiple tasks, resource requirements, and resource distribution in the multi-DAG job arrival scenario. Then, we design a dynamic resource allocation strategy model based on deep reinforcement learning (DRL) to reduce job processing time. Finally, we compare the performance of our proposed method with the other eight baseline algorithms using test datasets of various scales and under different arrival modes consisting of Montage, CyberShake, Broadband, Epigenomics, LIGO, VGG 16-SVD, and Edge Detection datasets.

The issue of efficient scheduling and deployment of urban public resources has become increasingly important with the development of technological innovations and the mobility of societies. The arbitrary usage behavior of users causes the unbalanced distribution of resources and makes it difficult for users to get adequate resources in some places but redundant resources in others. Therefore, designing an efficient scheduling policy for public resources becomes crucial to promoting resource utilization and customer satisfaction. In this article, we propose a novel scheduling system for public resources that aligns with the actual value-driven scheduling strategy and take the bike-sharing system as an example. Then, we design a deep reinforcement learning algorithm named two action layer proximal policy optimization (TALPPO) to generate an effective sharing-bike scheduling strategy under realistic constraints, which could help enterprises to make better management and operation decisions. Finally, we compare the proposed algorithm with the other ten baseline models and provide extensive experimental results on two data sets called Mobike (dockless) and Citi Bike (docked) to evaluate the performance of our proposed approach.

With the development of intelligent transport systems in the Internet of Things (IoT) smart cities, the bike-sharing system provides an environment-friendly choice for short-distance commuting, and it is employed extensively in major cities around the world. However, the issue of sharing bikes imbalance in various bike-sharing stations (BSS) constantly exists. Therefore, planning an effective route for rebalancing the bike-sharing system becomes a crucial task. In this article, based on a novel rebalancing problem of bike-sharing systems, which is to maximize the total allocated bikes at different stations under the constrained scheduling resources, we propose a meta-learning algorithm named ALRL to effectively allocate the sharing bikes under realistic constraints. Experimental results on real data sets and case studies demonstrate the effectiveness of our proposed approach which is better than the traditional methods.

For significant characteristics such as high efficiency and environmental protection, electric vehicles (EVs) have become a new technology trend in the intelligent transportation system (ITS). Data like real time traffic situation and charge point occupation in ITS shows significant real-time characteristics. Distributed storage system, an effective technology to ensure reliable sharing of dynamic data, is first adopted to storage allocation for delay-sensitive data in this article. In order to improve the recovery probability of delay-sensitive data within its timeliness, we establish an access queuing delay model based on the characteristics of sensitive data. Then, we find the optimal storage allocation strategy across distributed storage nodes for data with different delay threshold in terms of the recovery probability. The analysis shows that for data with low real-time performance and delay sensitivity, the maximal symmetric allocation is more excellent. For real-time data with low delay threshold, the minimal allocation is better under the condition of limited storage budget.

Electric vehicles (EVs) are viewed as an attractive option to reduce carbon emission and fuel consumption, but the popularization of EVs has been hindered by the cruising range limitation and the inconvenient charging process. In public charging stations, EVs usually spend a lot of time on queuing especially during peak hours of charging. Therefore, building an effective charging planning system has become a crucial task to reduce the total charging time for EVs. In this paper, we first introduce EVs charging scheduling problem and prove the NP-hardness of the problem. Then, we formalize the scheduling problem of EV charging as a Markov Decision Process and propose deep reinforcement learning algorithms to address it. The objective of the proposed algorithms is to minimize the total charging time of EVs and maximal reduction in the origin-destination distance. Finally, we experiment on real-world data and compare with two baseline algorithms to demonstrate the effectiveness of our approach. It shows that the proposed algorithms can significantly reduce the charging time of EVs compared to EST and NNCR algorithms.

In terms of preventing traffic accidents, improving traffic efficiency and ensuring personal safety, the research on vehicular ad hoc networks (VANETs) is of great significance. Message authentication is an important security foundation for VANETs. With the rapid growth of the number of access terminals, the existing computing power of the VANETs will not be able to meet the fast message verification service load of large-scale dynamic networks. This article proposes a novel distributed collaborative authentication method. By selecting a reasonable number of assistance verification terminals in the VANETs system and cooperating with the roadside unit (RSU) to jointly undertake the task of network message verification, the purpose is to reduce the verification delay and achieve fast message verification. The simulation results show that in a large-scale connected vehicle with a large number of system terminals, the system message verification delay of our scheme is shortened to one tenth of the centralized verification system message verification delay.

Bike-sharing systems become more and more popular in the urban transportation system, because of their convenience in recent years. However, due to the high daily usage and lack of effective maintenance, the number of bikes in good condition decreases significantly, and vast piles of broken bikes appear in many big cities. As a result, it is more difficult for regular users to get a working bike, which causes problems both economically and environmentally. Therefore, building an effective broken bike prediction and recycling model becomes a crucial task to promote cycling behavior. In this paper, we propose a predictive model to detect the broken bikes and recommend an optimal recycling program based on the large scale real-world sharing bike data. We incorporate the realistic constraints to formulate our problem and introduce a flexible objective function to tune the trade-off between the broken probability and recycled numbers of the bikes. Finally, we provide extensive experimental results and case studies to demonstrate the effectiveness of our approach.

In this paper, we study the secrecy communication in two-user MISO interference networks where an external eavesdropper is interested in the messages transmitted by both transmitters. We propose a beamforming design to maximize the achievable secrecy sum rate of the transmitters subject to the individual power constraint at each transmitter. To transform this complex non-convex problem into a convex one, we propose an iterative algorithm based on the constrained concave convex procedure (CCCP) and successive convex approximation (SCA). It is observed that the proposed algorithm converges fast to a stationary point within a few iterations. Furthermore, we also propose a low-complexity null-space beamforming design scheme in which the beamforming vectors have closed-form solutions. Simulation results show the effectiveness of the two proposed schemes in improving the secrecy sum rate performance.

In this letter, we jointly design relay antenna tilting with user scheduling so as to enhance the sum rate performance of a two-hop relay system, where the relay is equipped with a large-scale antenna array and performs zero-forcing processing. Building the fundamental of the joint design, a tight and tractable sum rate approximation is first derived by employing random matrix theory. Then the relay antenna downtilt and the number of active user pairs are jointly optimized to maximize the approximate sum rate. It is noted that the proposed scheme is independent of instantaneous channel state information. Therefore, it enjoys very low implementation complexity while improving the system performance.

We investigate the secrecy beamforming in two-way relay channels (TWRC) with physical layer network coding (PNC). The multi-antenna relay broadcasts the superimposed signal of two user messages with secrecy beamforming after receiving the signals transmitted by the two legitimate users. We first propose a lower bound of the secrecy sum rate to quantify the secrecy performance of the TWRC with PNC. Because the maximization of the lower bound is non-convex under total power constraint, we propose a joint beamforming and power allocation scheme, in which the problem is successively approximated by several convex semidefinite programs. In order to reduce the complexity, we further propose an suboptimal scheme with closed-form solution. Numerical results indicate that the proposed schemes with PNC achieve much better secrecy sum-rate performance than the traditional AF schemes.

This paper reveals symbol extension is capable of improving secrecy performance in the multiple-input single-output (MISO) wiretap channel. We propose a symbol extension scheme jointly with the existing beamforming and artificial noise generation strategy to exploit the time variation of fading channel. After multiplying the data symbol vector by a proper designed square matrix, the data symbol can be extended to multiple time-slots. As a result, without any symbol rate loss, the proposed scheme enhances the secrecy performance in terms of secrecy outage probability. Furthermore, we also analyze the asymptotic secrecy outage probability and derive the achievable diversity order. Both analytical and numerical results show that the proposed scheme can bring more diversity gains into secrecy communication.