Edge slicing is envisioned to support connected autonomous vehicle (CAV) applications with diverse key performance indicator (KPI) requirements by splitting the shared physical infrastructure into several virtual networks. Unfortunately, existing provisioning approaches struggle to accommodate the spatiotemporal dynamics of CAV traffic, leading to significant violated KPIs or soared resource usage. In this paper, we introduce the transient sharing mechanism among edge slices to obtain reused gains without generating harmful performance interference, in which a slice is allowed to access to the under-utilized reserved resources of other slices but may experience interruptions at any time. Considering the heterogeneity and uncertainty of transient resources, we further develop a two-timescale provisioning scheme. Specifically, slices proactively make reservation decisions based on multi-armed bandit architectures at the beginning of large timescales, while hinging on cost-incentive auction mechanisms selectively preempt transient resources in terms of real-time application demands at each small timescale. With extensive experiments based on real traffic traces, we demonstrate that the proposed scheme can improve 10.43% resource utilization and make slices reduce 42.92% cost than state-of-the-art works, which verifies its high assurance and adaptability.

With the explosive growth of multimedia data storage, multimodal learning is an inevitable trend for Information Extraction (IE). However, the noise and irrelevance of web- crawled samples cause adverse degradation in each modality. Additionally, previous researches inadequately address the above issue, and the potential of cross-modal fusion remains underexplored. We propose a strengthened alignment module, using a generative text augmentation submodule to reduce noise contamination and emphasize incorporating visual features into texts. We further propose a fusion adapter utilizing a soft-prompt structure for profound fusion. To activate logical capabilities, we apply prompts with a multi-turn dialogue. For the Multimodal Relation Extraction task over the MNRE dataset, our method exceeds the previous SOTA model with a 7% increase in F1-score. It has superior generalization for other multimodal IE tasks, achieving SOTA on Named Entity Recognition over Twitter-15/17 datasets and on Event Extraction over M2E2dataset.

Within the Internet of Things (IoT) for traffic monitoring, the employment of autonomous aerial vehicles (AAVs) as relays for collecting and transmitting real-time data from traffic sensors to base stations has proven a promising approach. In UAV-assisted Data Time-Sensitive IoT (DTIoT), the Age of Information is a crucial metric assessing data freshness, measuring the elapsed time from traffic sensors to the base station. Optimizing flight trajectories of multiple UAVs to minimize AoI while adhering to energy constraints poses a significant challenge. Current research often employs deep reinforcement learning for UAV trajectory control. Nevertheless, managing multi-agent continuous trajectories in intricate DTIoT network environments faces obstacles due to sparse rewards, thus impeding the training of deep neural network-based control policies using traditional DRL techniques. In this paper, we propose a network-oriented hierarchical reinforcement learning (NO-HRL) to control the UAVs’ flight trajectory in DTIoT networks for minimizing the AoI. We devise a control policy leveraging a two-tier hierarchical DRL framework, with the upper tier determining the target and the lower tier executing it. We also introduce a decoupled sequential training approach to efficiently train the mutually dependent two-tier DRL network of NO-HRL. Experimental results demonstrate that our method excels in optimizing AoI for DTIoT compared to other baselines.

In the context of disaster warning and monitoring within the Internet of Things (IoT), the utilization of unmanned aerial vehicles (UAVs) as relays to gather time-sensitive data from disaster monitoring sensors and transmit it to the base station (BS) has emerged as a highly promising application. In UAV-assisted Data Time-Sensitive IoT (DTIoT), the Age of Information (AoI) serves as a critical performance metric that quantifies the timeliness of data collection, specifically referring to the duration it takes for data to travel from the sensor to the BS. Controlling the flight trajectories of multiple UAVs to minimize AoI is a challenge under energy constraints. Existing work typically uses deep reinforcement learning (DRL) algorithms to address UAV trajectory control problems. However, the task of controlling multi-agent continuous trajectories in complex DTIoT network states is hindered by sparse rewards, posing challenges in training deep neural network-based control policies using standard DRL methods. In this paper, we propose a network-oriented hierarchical reinforcement learning (NO-HRL) algorithm to control the UAVs’ flight trajectory in DTIoT networks for minimizing the AoI. We design the control policy based on a two-layer hierarchical DRL, where the upper layer selects the target and the lower layer executes it. We further propose a decoupled sequential training scheme for effectively training the mutually coupled two-layer DRL network of NO-HRL. The experiment results show that our algorithm outperforms other baselines in AoI optimization for DTIoT.

Compared with terrestrial networks, unmanned aerial vehicles (UAVs) have the characteristics of flexible deployment and strong adaptability, which are an important supplement to intelligent transportation systems (ITS). In this paper, we focus on the multi-UAV network area coverage problem (ACP) which require intelligent UAVs long-term trajectory decisions in the complex and scalable network environment. Multi-agent deep reinforcement learning (DRL) has recently emerged as an effective tool for solving long-term decisions problems. However, since the input dimension of multi-layer perceptron (MLP)-based deep neural network (DNN) is fixed, it is difficult for standard DNN to adapt to a variable number of UAVs and network users. Therefore, we combine Transformer with DRL to meet the scalability of the network and propose a Transformer-based deep multi-agent reinforcement learning (T-MARL) algorithm. Transformer can adapt to variable input dimensions and extract important information from complex network states by attention module. In our research, we find that random initialization of Transformer may cause DRL training failure, so we propose a baseline-assisted pre-training scheme. This scheme can quickly provide an initial policy model for UAVs based on imitation learning, and use the temporal-difference(1) algorithm to initialize policy evaluation network. Finally, based on parameter sharing, T-MARL is applicable to any standard DRL algorithm and supports expansion on networks of different sizes. Experimental results show that T-MARL can make UAVs have cooperative behaviors and perform outstandingly on ACP.

With the advancement of intelligent transportation systems, a series of diversified V2X applications come into being, which have different key performance indicators (KPIs) and transmission features. Moreover, multi-tier computing as a new system-level architecture distributes computing and communication capabilities anywhere between the cloud and the end-user. Unfortunately, the existing network paradigm for V2X services adopts a one-shot allocation of resources ignoring the inherent differences of V2X service. To cope with these problems, three types of refined network slices for V2X services are first proposed to simultaneously support heterogeneous service characteristics without excessively splitting resources. Considering the spatiotemporal correlation between service traffic and physical resources, a jagged slicing in multi-tier dynamic resources, which forms a “slice sandwich” brightly, is realized by a dual timescale intelligent resource management scheme. The inter-slice resource configuration is based on neural bandits with upper confidence bounds at each large-time period, while the exclusive resources are managed elastically by deep Q-learning in terms of the real-time changing network state in the small slot. We developed a simulation environment by Simulation of Urban Mobility (SUMO) including real-world road conditions and traffic models. The experiment results demonstrate that the proposed scheme can effectively guarantee KPIs of V2X services and improve the system revenue compared with benchmark algorithms.

Edge network slicing promises better utilization of network resources by dynamically allocating resources on demand. However, addressing the imbalance between slice resources and user demands becomes challenging when complex user behaviors lead to bursty traffic within the edge network. Hence, we propose a comprehensive dynamic slice strategy with two coupled sub-strategies (i) bursty-sensitive slice resource coordination and (ii) proactive demand resource matching to find an optimal balance. For obtaining stable strategies, the edge network with bursty traffic is formulated as a bi-level Lyapunov optimization problem. Then we propose a resource allocation and request redirection (RA-RR) algorithm with polynomial complexity by introducing deep reinforcement learning to guarantee real-time. Specifically, two agents are trained to solve two sub-strategies, and the Lyapunov drift-plus-penalty function is used as the reward to keep queues stable. RA-RR is responsive to fluctuations in demand and realizes an efficient interaction of coupled decision-making. Moreover, a training method based on alternating optimization is designed to ensure convergence of the RA-RR algorithm. Experiments demonstrate that the proposal can maximize network revenue while ensuring the stability of slice services when edge traffic bursts, and has an average improvement of 20.4% compared with comparisons.

Internet of Things (IoT) software is used to control the distributed hardware of the underlying network and provide a reliable operating platform for various services. In production system, diversity IoT software provides multiple services, flows of different software run in parallel on the same IoT platform. Thus, system-level network parameter configurations may not be suitable for all service needs. In this paper, we focus on the challenge of the personally parameterizing transmission unit size and congestion windows (CWND) in flow control. We propose deeper flow control software model (DeepFC) for finer-grained flow control than traditional algorithms. DeepFC consist of two parts: (i) Since system-level transmission unit size may degrade network performance due to frequent fragmentation, we combine path MTU (PMTU) and deep reinforcement learning (DRL) to predict fine-grained flow-level transmission unit size. (ii) Transmission unit size is related to CWND in flow control. The fine-grained transmission unit size needs fine-grained congestion control solution. In DeepFC, we consider the mutual coupling between transmission unit size and CWND parameter configuration to further improve network performance. Experimental results show that DeepFC can reduce fragmentation by 67.8% compared to the protocols with system-level transmission unit size, flow completion time can be reduced by 20.93%, and throughput can be increased by 19.96% compared to the average of benchmark algorithms.

Network slicing is rapidly prevailing in the edge network, which provides computing, network, and storage resources for various services. When the multiple service providers (SPs) respond to their tenants in parallel, individual decisions on the dynamic and shared edge network may lead to resource conflicts, which affects the delivery of network slicing services. Existing works ignore resource interaction and coordination in the multi-SP scenario, which is not in line with the actual situation. Indeed, the complexity of resource interaction caused by the coexistence of multiple SP policies increases the difficulty to solve the formulated optimization model. In this article, we focus on the multi-SP network slicing deployment in parallel. The coordination of network resources between SPs is designed as an effective multi-agent communication mechanism that is merged into multi-agent deep reinforcement learning (MADRL). To deal with dynamic edge networks, we design the neurons hotplugging learning which realizes scalability without a high cost of model retraining. Experiments on real and random networks demonstrate that the proposed multi-SP network slicing mechanism can successfully learn coordination policies and easily adapt to various network scales. It improves the accepted requests by 7.4%, reduces resource conflicts by 14.5%, and shortens the model convergence time by 83.3%.

Network slicing is rapidly prevailing in edge cloud, which provides computing, network and storage resources for various services. When the multiple service providers (SPs) respond to their tenants in parallel, individual decisions on the dynamic and shared edge cloud may lead to resource conflicts. The resource conflicts problem can be formulated as a multi-objective constrained optimization model; however, it is challenging to solve it due to the complexity of resource interactions caused by co-existing multi-SP policies. Therefore, we propose a CommDRL scheme based on multi-agent deep reinforcement learning (MADRL) and multi-agent communication to tackle the challenge. CommDRL can coordinate network resources between SPs with less overhead. Moreover, we design the neurons hotplugging learning in CommDRL to deal with dynamic edge cloud, which realizes scalability without a high cost of model retraining. Experiments demonstrate that CommDRL can successfully obtain deployment policies and easily adapt to various network scales. It improves the accepted requests by 7.4%, reduces resource conflicts by 14.5%, and shortens the model convergence time by 83.3%.

Unmanned aerial vehicles (UAVs) can provide flexible network coverage services. UAVs can be applied in a large number of scenarios, such as emergency communication and network access in areas without terrestrial network coverage. However, UAVs are limited to relatively short communication range and restricted energy resources. In extreme conditions such as disasters, there may also be a problem that the communication bandwidth is limited and the UAV cannot communicate with the server with a large amount of information, so a decentralized solution is expected. In addition, the interaction between multiple objectives and multiple UAVs leads to a huge state space, which makes large-scale practical applications difficult. To simplify complex interactions, we modeled the UAV control problem with mean-field game (MFG). We propose a new UAV control method, the mean-field trust region policy optimization (MFTRPO), which uses the MFG method to construct the Hamilton-Jacobi-Bellman/Fokker-Planck-Kolmogorov equation that obtains the optimal solution and solves the difficulties in the practical application through the trust region policy optimization and neural network feature embedding methods. The proposed method: 1) maximizes communication efficiency while ensuring fair communication range and network connectivity; 2) fuses the mean-field theory with deep reinforcement learning techniques; and 3) is scalable and adaptive. We conduct extensive simulations for performance evaluation. The simulation results have shown that MFTRPO significantly and consistently outperforms two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

Aerial Remote Sensing (Aerial RS) image compression & transmission on-board system, not only is the core of Aerial RS supervising, but also the key technology about the security of data obtainment. The requirement of Aerial RS is stricter on the data quality and security. If we transmit the images through some special channels while the images are obtained during the task of Aerial RS, the control center on the ground could acquire the status of the whole Aerial RS system. Besides, it could also backup the images immediately. Image compression & transmission system is the important bridge between the Aerial RS system on-board and the control center. The research group of Aerial RS data processing in Peking University, integrates all the necessary technologies including compression algorithm & hardware, integration of function modules, protocol of data packing & unpacking. The research group has developed an efficient compression method that has been optimized both in software structures and hardware architecture. Considering with the narrow space on the airplane, the research group has designed the compression & transmission function module which is installed on the motherboard of the Aerial RS control system. The compression program is compiled in the Code Composer Studio (CCS) development environment, and then the result file compiled is burned into the DSP chip on the module. This paper also introduces an efficient protocol designed by the research group, which can ensure the accuracy of the transmission. It ensures the control center can rebuild & unpack the packages correctly, and avoid fatal errors caused by some false frames and packages during the transmission. This data compression & transmission system, which has been tested in certain different places in China, has achieved the purpose expected.

Arable land protection has been a global hotspot, because how can we exploit arable-land resources is an important topic for the regional future socio-economic stability and sustainable development in China. Managing and monitoring the change of arable-land on quantity and quality can provide the background and foundation information. The paper starts with the introduction of the necessity to monitor the data of arable land change and set up a system to manage the year on year changes of arable land. Then the authors present the design of the main functions of the system and discuss the system's structure and framework. On the basis of these, databases, including spatial database and attribute database are designed and appropriately organized. At last, the China arable-land change information system (CALCIS) is developed based on geographical information system and remote sensing techniques and then a good example of it is given to show the successful application of the system.

In this paper, we present a novel semi-fragile watermarking scheme based on wavelet packet. The method in the paper includes four parts: first, to produce watermark; second, to scramble watermarking image; third, to embed watermark; last, to inspect and locate tampered marked image. To inspect whether including watermark in an image with the key attained from process of embedding watermarking. If it is a marked image, then extracting watermarking. At last to validate the degree of robustness by compression and noise, to locate tamper by cutting and altering.