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.

In advanced vehicular networks, Roadside Unit (RSU)-based amplify-and-forward (AF) relay and passive Reconfigurable Intelligent Surface (RIS) are two potential helpers to enhance the vehicle-to-vehicle (V2V) communications when the direct link experiences poor quality. This paper presents a comprehensive comparison of the two enhancement modes from the outage performance perspective. In the presence of both direct link and enhanced link, the analytical expressions of the outage probability (OP) for the V2V communication under the two enhancement modes are derived respectively. Moreover, considering the co-channel interference caused by relay/RIS, the OP of the neighbouring vehicle-to-infrastructure (V2I) communication is also derived. Additional analysis compares the diversity order and the strength of interference created by the V2V communication under the two enhancement modes. Further discussions are presented on the effect of the channel estimation error and phase quantization error under the RIS mode. Finally, the pros and cons of the two enhancement modes are demonstrated by both the analytical and numerical results.

The combination of edge computing (EC) and blockchain can enhance task processing while ensuring security and credibility. To maximize system performance and avoid resource waste in task offloading, it is essential to match the resource allocation of the task computing and the blockchain consensus process. However, the existing works treated the above two processes as two independent processes and optimized them separately and ignored the above matching problem. In this article, we propose a resource management scheme for blockchain-assisted EC networks consisting of multiple devices, multiple base stations equipped with edge servers, a cloud server, and a network controller deployed on the edge layer. To minimize the total task processing delay and energy consumption of the devices, we formulate a joint task processing problem incorporating task scheduling, transmit power control, and computing resource allocation. To match the computing delay and consensus delay of each task, we balance the computing resources allocated for the two processes. We design a deep reinforcement learning (DRL) algorithm that utilizes the twin-delayed deep deterministic policy gradient (TD3) technology embedded with a fast numerical method, which effectively reduces the training complexity of the DRL model. Extensive experiments are conducted by varying four crucial parameters. The superiority of our scheme is demonstrated in comparison with three other reference schemes. The performance of our scheme is about 18.3%–24.1% higher than that of other schemes.

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.

In an edge-cloud cooperative computing network, the task offloading performance can be further improved by the edge-cloud and edge-edge cooperation, in which the tasks can be offloaded from an edge server to the cloud server or another edge server. Such edge-cloud cooperative task offloading can jointly utilize the resources of all the edge servers and the cloud server. This paper proposes a collaborative service placement, task scheduling, computing resource allocation, and transmission rate allocation scheme for a multi-task and multi-service scenario with edge-cloud cooperation. The objective of our optimization problem is to minimize the total task processing delay while guaranteeing long-term task queuing stability. Considering the high complexity of the original optimization problem, we transform the problem into a deterministic problem for each time slot based on the Lyapunov optimization. Then, we design an iterative algorithm to obtain the whole solution to the problem efficiently based on a hybrid method using multiple numerical techniques. Further, considering the inherent difference in the optimization periods of the service placement, resource allocation, and task scheduling sub-problems, we design a multi-timescale algorithm to solve the sub-problems with different optimization periods. The complexity of the proposed algorithms is analyzed, and extensive simulations are conducted by varying multiple crucial parameters. The superiority of our scheme is demonstrated in comparison with 4 other schemes.

Multiaccess edge computing (MEC) is a promising approach to enhancing IoT devices running AI-based services. Especially, the edge–cloud architecture acts as a strong supporter of the resource-limited IoT devices. How to optimize the system resources efficiently to improve the service performance is the key issue in this scenario. Motivated by this, in this article, we focus on a multi-base station (BS) and multiservice edge–cloud-assisted IoT environment, where both the BSs (with edge servers deployed) and the cloud can assist the IoT devices to process multitype deep learning (DL) tasks via task offloading. DNN partition mechanism and both the communication and computing resources allocation are utilized to enable a collaborative optimization to minimize the processing delay of all the DL tasks in the system. Due to the mixed-integer nonlinear programming (MINLP) characteristic of our optimization problem, we propose an algorithm that decomposes the original problem into two subproblems, solves them separately, and then obtains the near-optimal solution efficiently. Extensive simulations are conducted by varying five different crucial parameters. The superiority of our scheme is demonstrated in comparisons with several other schemes proposed by existing works. Our scheme can achieve a notable 28.3% delay reduction on average.

Machine learning (ML) plays a key role in Intelligent Industrial Internet of Things (IIoT) applications. Processing of the computation-intensive ML tasks can be largely enhanced by applying edge computing (EC) to traditional cloud-based schemes. System optimizations in the existing works always ignore the inference accuracy of ML models with different complexities, and their impacts on error task inference. In this article, we propose a joint task offloading and resource allocation scheme for accuracy-aware machine-learning-based IIoT applications in an edge–cloud-based network architecture. We aim at minimizing the long-term average system cost affected by the task offloading, computing resource allocation, and inference accuracy of the ML models deployed on the sensors, edge server, and cloud server. The Lyapunov optimization technique is applied to convert the long-term stochastic optimization problem into a short-term deterministic problem. An optimal algorithm based on the general Benders decomposition (GBD) technology and a heuristic algorithm based on proportional computing resource allocation and task offloading strategy comparison are proposed to efficiently solve the problem, respectively. The performance of our scheme is proved by theoretical analysis and evaluated by extensive simulations conducted in multiple scenarios. Simulation results demonstrate the effectiveness and superiority of our two algorithms in comparison with several other schemes proposed by the existing works.

Mobile edge computing (MEC) shows prominent application prospects in the Industrial Internet of Things (IIoT) by allowing resource-restricted IIoT mobile devices (MDs) to offload their tasks to geographical proximity edge clouds. An efficient incentive mechanism should be designed jointly addressing resource allocation and pricing to incentivize MDs (i.e., buyers) and edge clouds (i.e., sellers) to participate in offloading service trading. This article aims to solve the social welfare maximization problem of a personalized MEC computation offloading service market where each edge cloud can allocate different computing and wireless resources to each MD according to the MDs’ delay and energy consumption constraints, and each MD submits bids to edge clouds differently based on the resource allocation of the edge clouds. We propose a truthful combinatorial auction (TCA) mechanism which involves three phases of resource allocation, buyer-seller matching, and payment determination. It should be highlighted that our proposed buyer-seller matching algorithm combines optimal matching and heuristic matching, so it greatly improves the auction effect while ensuring computational efficiency. Considerable theoretical analysis and experimental results prove that the performance of the proposed TCA mechanism is significantly superior to that of other auction mechanisms while holding the desirable properties.

Joint task inference, which fully utilizes end edge cloud cooperation, can effectively enhance the performance of deep neural network (DNN) inference services in the industrial internet of things (IIoT) applications. In this paper, we propose a novel joint resource management scheme for a multi task and multi service scenario consisting of multiple sensors, a cloud server, and a base station equipped with an edge server . A time slotted system model is proposed, incorporating DNN deployment, data size control, task offloading, computing resource allocation, and wireless channel allocation. Among them, the DNN deployment is to deploy proper DNNs on the edge server under its total resource constraint, and the data size control is to make trade off between task inference accuracy and task transmission delay through changing task da ta size. Our goal is to minimize the total cost including total task processing delay and total error inference penalty while guaranteeing long term task queue stability and all task inference accuracy requirements. Leveraging the Lyapunov optimization, we first transform the optimization problem into a deterministic problem for each time slot. Then, a deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm is designed to provide the near optimal solution. We further desi gn a fast numerical method for the data size control sub problem to reduce the training complexity of the DRL model, and design a penalty mechanism to prevent frequent optimizations of DNN deployment. Extensive experiments are conducted by varying differen t crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other schemes.

In an internet of vehicle (IoV) scenario, vehicular edge computing (VEC) exploits the computing capabilities of the vehicles and roadside unit (RSU) to enhance the task processing capabilities of the vehicles. Resource management is essential to the performance improvement of the VEC system. In this paper, we propose a joint task offloading and resource allocation scheme to minimize the total task processing delay of all the vehicles through task scheduling, channel allocation, and computing resource allocation for the vehicles and RSU. Different from the existing works, our scheme: 1) considers task diversity by profiling the tasks of the vehicles by multiple attributes including data size, computation amount, delay tolerance, and task type; 2) considers vehicle classification by dividing the vehicles into 4 sets according to whether they have task offloading requirements or provide task processing services; 3) considers task processing flexibility by deciding for each vehicle to process its tasks locally, to offload the tasks to the RSU via V2I (Vehicle to Infrastructure) connections, or to the other vehicles via V2V (Vehicle to Vehicle) connections. An algorithm based on the Generalized Benders Decomposition (GBD) and Reformulation Linearization (RL) methods is designed to optimally solve the optimization problem. A heuristic algorithm is also designed to provide the sub-optimal solution with low computational complexity. We analyze the convergence and complexity of the proposed algorithms and conduct extensive simulations in 6 scenarios. The simulation results demonstrate the superiority of our scheme in comparison with 4 other schemes.

Energy demand and supply vary from area to area, where an unbalanced load may occur and endanger the system security constraints and cause significant differences in the locational marginal price (LMP) in the power system. With the increasing proportion of local renewable energy (RE) sources in microgrids that are connected to the power grid and the growing number of electric vehicle (EV) charging loads, the imbalance will be further magnified. In this article, we first model the EV charging network as a cyber–physical system (CPS) that is coupled with both the transportation networks and the smart grids. Then, we propose an EV charging station recommendation algorithm. With a proper charging scheduling algorithm deployed, the synergy between the transportation network and the smart grid can be created. The EV charging activity will no longer be a burden for power grids, but a load-balancing tool that can transfer energy between the unbalanced distribution grids. The proposed system model is validated via simulations. The results show that the proposed algorithms can optimize the EV charging behaviors, reduce charging costs, and effectively balance the regional load profiles of the grids.

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.

Enabled by Multi-access Edge Computing (MEC) in a WiFi-cellular heterogeneous network, the tasks of mobile terminals (MTs) can be offloaded via the cellular network to the MEC servers or cloud server, or via the WiFi network to alleviate transmission congestion of the cellular network. The MEC also enables service caching to cache the programs/libraries/databases of the tasks to avoid repeated input data uploading. Existing research works lack joint optimization on the task offloading and service caching for MEC in the WiFi-cellular heterogeneous network. In this paper, a novel resource management scheme for joint task offloading and service caching is proposed to maximize the energy consumption benefits of all the MTs covered by a WiFi-cellular heterogeneous network while guaranteeing the task processing delay tolerance of each MT. We consider the constraints on limited computing and storage resources of the MEC servers equipped on the cellular base station and the WiFi access point, and we also consider cellular channel allocation for the task offloading. We design an iterative algorithm based on the alternating optimization technique to solve the proposed mixed integer nonlinear programming problem efficiently. Extensive simulations are conducted in multiple scenarios by varying different crucial parameters. The numerical results demonstrate that our scheme can largely improve the system performance in all the scenarios, and energy consumption reduction optimized by our scheme is 16.24%-43.09% higher than those by the comparative works.

The widely used Internet of Things (IoT) mobile devices (MDs) require fast processing capability to handle a large volume of computing tasks. Mobile-edge computing (MEC) can augment the capability of IoT MDs through offloading their computing tasks to the MEC-enabled base station (MEC-BS) that covers them. Most of the existing research works only focus on the computation offloading problems for a single MEC-BS. However, the load of a MEC-BS will rise as the increase of the scale of the offloaded tasks, especially during rush hours, and further it will result in deterioration of system performance. In this article, we propose a game-based multitype task offloading scheme among MEC-BSs. The tasks offloaded from IoT MDs can be further offloaded among MEC-BSs to alleviate high-load MEC-BSs. Aiming at balancing the computing delays of the tasks on each MEC-BS, a noncooperative game is formulated to model the computation offloading for the tasks with different types, indicated by computation amount, data size, and delay tolerance. The existence and convergence of the Nash equilibrium of the game are first proved using the variational inequality and regularization techniques. Then, we design a distributed iterative algorithm to efficiently solve the game problem. Simulation results show the fast convergence of our algorithm. The reduction of total computing delay optimized by our scheme can reach 45%–50% on average in multiple scenarios, and the superiority of our scheme is also demonstrated in comparisons with reference schemes.

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.

Content-based sensor search is a core application in the Internet of Things (IoT), where target sensors can be quickly found by predicting the current output of the sensors. Due to the lack of update mechanism, the established prediction model in existing search architectures will gradually become unavailable in the highly dynamic IoT environment. Hence, a dual prediction structure based on edge intelligence is proposed in this article, to maintain the performance of the prediction model with minimum communication cost in long-term prediction. To implement our architecture, a more effective online learning algorithm is proposed to update prediction models online combined with our proposed adaptive window pattern clustering (AWPC) algorithm. Meanwhile, based on the dual prediction scheme (DPS), a mechanism is deployed on edge sensors to achieve transfer decision making in our architecture, where edge computing is performed to achieve selectively reporting data. With the designed architecture, about 76.56% of the communication energy consumption could be saved while achieving a 95.47% average prediction accuracy in continuous long-term prediction.

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.

In this letter, we propose a novel scheme, called Compensational Computation Offloading, which aims at efficiently maximizing the lifetime of edge networks. The main idea of the scheme is to offload the computing tasks of the nodes with low residual energies to the ones with high residual energies. At the same time, the energy consumptions cost by the transmitting and receiving tasks during the computation offloading are considered. IN order to balance the residual energies of the network, we design a high-efficiency scheduling algorithm which runs iteratively to search for near-optimal probabilities of each node offloading its tasks to its neighbor nodes. Finally, we obtain a set of probability values to form the overall scheduling algorithm. Simulation results demonstrate our scheme can efficiently increase the network's lifetime several times under different network topologies. The algorithm is very reliable and can approach approximate equality for each node in an edge network.