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.

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.

In multi-access edge computing (MEC) networks, network slicing enables the MEC network service provider (MEC-NSP) to provide customizable MEC services for user devices (UDs) with diverse QoS (Quality of Service) demands. In MEC network slicing, slice pricing and network resource allocation for slices are two core problems, which have not been jointly considered by existing works. To this end, we propose a two-stage slice pricing scheme to achieve balanced slice pricing and optimal network resource allocation. The goal of our scheme is to reduce the resource costs of the MEC-NSP and ensure its profit while meeting different user QoS requirements. At the first stage, we jointly optimize the computing, cache and communication resource allocation for all the slices by using problem decomposition. Then, we formulate a slice pricing problem based the Stackelberg game, prove the Nash equilibrium existence of the problem, and design an iterative algorithm based on the optimal response function. Extensive simulations are conducted in 4 scenarios, where our scheme is compared with 4 reference schemes. The simulation results demonstrate the superiority of our scheme in all the scenarios. The profit of the MEC-NSP optimized by our scheme is 17.64%-24.39% higher than those by the comparative works.

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.

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.

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 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.

With the remarkable development of Wi-Fi network, network security has become a key concern over the years. In order to face the increasing number of wireless network intrusion activities, an effective intrusion detection system is necessary. In this paper, a deep learning approach based on ladder network which self-learns the features necessary to detect network anomalies and perform attack classification accurately was proposed. And using focal loss as a loss function to enhance the discriminative ability of the model to classify difficult samples. In experiments on Aegean Wi-Fi Intrusion Dataset (AWID) public data-set, the network records was classified into 4 types: normal record, injection attack, impersonation attack, flooding attack. This paper achieved the classification accuracies of these four types of records are 99.77%, 82.79%, 89.32%, 73.41% respectively, and achieved an overall accuracy of 98.54%.

Event matching is vital in multi-dimensional content-based publish/subscribe services, which are widely employed for data dissemination in various scenarios. Existing mechanisms suffer from performance degradation in high-dynamic large-scale systems. To this end, we present GEM (Geometrical Event Matching), an analytic geometrical approach to fast event matching. GEM offers a very high event matching speed, and it also has low costs for subscription insertion/deletion operations and memory usage. In GEM, subscriptions are organized efficiently by a triangle-like index structure. A graph partitioning matching method and a selection matching method are jointly used for single-dimensional matching (SDM). Optimized by a decision algorithm for each incoming event, the event matching process is carried out in a pipeline consisting the SDM for each dimension. The search space shrinks continuously as the process goes, so that the event matching performance is promoted adaptively. A cache method is also designed to boost the first SDM in the pipeline. We implement extensive experiments to evaluate the performance of GEM in comparison with 3 state-of-the-art reference algorithms (TAMA, H-TREE and REIN). The results show that, the event matching time, subscription insertion/deletion time and memory consumption of GEM is on average 53.9%, 42.3%/49.5% and 31.8% lower than the best in other 3 algorithms, respectively. The event matching time of GEM is reduced efficiently via the cache method. The superiority of GEM appears more significantly as system scale and dynamic grow, and its performance also maintains in a high stability.

In this paper, we first give an overview of security architecture of mobile WiMAX network. Then, we investigate man-in-the-middle attacks and Denial of Service (DoS) attacks toward 802.16e-based Mobile WiMAX network. We find the initial network procedure is not effectively secured that makes Man-in-the-middle and Dos attacks possible. In addition, we find the resource saving and handover procedure is not secured enough to resist DoS attacks. Focusing on these two kinds of attacks, we propose Secure Initial Network Entry Protocol (SINEP) based on Diffie-Hellman (DH) key exchange protocol to enhance the security level during network initial. We modify DH key exchange protocol to fit it into mobile WiMAX network as well as to eliminate existing weakness in original DH key exchange protocol.