The multiple Uncrewed Aerial Vehicles (multi-UAV) collaborative system significantly augments perception capabilities and coverage range of task environments through the establishment of a comprehensive three-dimensional monitoring network, emerging as an indispensable technological cornerstone for future Mobile Crowdsensing (MCS) systems. However, the co-existence of environmental dynamics and device heterogeneity induces non-trivial energy efficiency imbalances across the UAVs, posing a substantial challenge to achieving sustained and efficient multi-UAV exploration. Therefore, we propose an energy-efficient cluster cooperative exploration method that jointly optimizes information sharing and task allocation. To balance communication energy efficiency among UAVs, we introduce an energy-efficient hierarchical information sharing mechanism that dynamically adjusts relay nodes based on real-time attributes of UAVs. In order to improve the utilization of resources, a multi-UAV cooperative task allocation model was developed using cooperative game. It has also been proven that a fair task allocation strategy exists, which is acceptable to all UAVs. Furthermore, the approximate Shapley value of every UAV is calculated using the improved Monte Carlo sampling method combined with incremental update mechanism to ensure fair task allocation. Experimental results demonstrate that the maximum enhancement of task completion ratio is 12%, 26%, and 10%, respectively, at task-critical thresholds for systems utilizing 5, 10, and 15 UAVs. Moreover, the proposed method demonstrated superior performance in energy consumption ratio, synergy, and energy consumption difference compared to benchmarks.

The regional edge cloud data centers support numerous latency-sensitive applications, including autonomous driving, Augmented Reality/Virtual Reality (AR/VR), smart grids. However, dynamic workloads often trigger spurious Virtual Machine (VM) migrations that degrade real-time service guarantees. To address this challenge, we propose a lightweight, proactive VM scheduling framework based on a hierarchical structure (HLFVM). By combining logical region partitioning with low-complexity migration algorithms, it enables rapid localized migration decisions. First, by leveraging the Enhanced Harris Hawk Optimization (EHHO) to optimize the parameters of the Long Short Term Memory (LSTM) model, we propose a Load Forecast method based on the EHHO-LSTM (LFEL) model. This algorithm accurately predicts multiple resource loads on PMs and reduces the lag in migration decision-making. Then, we propose the zone-aware LFEL-based VM Migration (LFVM) algorithm, which includes PM status classification and migration selection mechanism. The migration selection mechanism chooses the VM destinations according to the cost function to expedite the migration decision. Numerous experiments have shown that the execution time of the LFVM algorithm is reduced by at least 70.4% compared to traditional algorithms, while VM migration time is improved by 5.7%. Concurrently, it achieves superior control over energy consumption and enhances resource utilization.

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection$({\mathrm { AP}}_{s})$is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion.

Artificial Intelligence of Things (AIoT) utilizes a combination of computing, storage, and networking resources to provide highly reliable and low-latency information services to the industrial production processes. However, with the increasing integration of numerous smart terminals into real-time sensing, autonomous decision-making, and precision manufacturing execution systems, the current task scheduling pattern appears to be insufficient to meet the latency requirements of computationally intensive tasks. To address the above challenge, this paper presents a collaborative edge-terminal task offloading scheme. First, the Task Backlog and Multi-slot Scheduling (TBMS) problem is converted from a long-term offloading problem to a single timeslot scheduling problem by Lyapunov optimization. Then, to simplify the problem, the single timeslot problem is decomposed into three subproblems: the local resource allocation problem, the server resource allocation problem, and the indicator weight selection problem. The two resource allocation problems are proved to be convex, which have been solved by using the Bisection method and the Karush-Kuhn-Tucker (KKT) method, respectively. For the indicator weight selection problem, we proposed the enhanced jumping spider optimization algorithm that integrates the elite opposition-based learning strategy. Extensive experiments show that the proposed algorithm can alleviate the computing pressure of the terminal device. Compared with the traditional methods, the offload system cost is effectively reduced by at least 58.8% and the average execution success rate is increased by at least 6%.

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a Mixed Integer Nonlinear Programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

In industrial cyber–physical systems (ICPS), real-time condition monitoring of wear-prone components of steel rolling production equipment is a key scenario for predictive maintenance. Machine vision-based crack detection can quickly identify critical damage and prevent unplanned downtime. However, the harsh working environment poses difficulties for data collection, a large amount of noise tends to contaminate surface crack images, and complex surface crack morphology affects the recognition accuracy. The real-time and accuracy performance of traditional crack detection algorithms are hard to meet the requirement of industrial applications. To tackle this challenge, a high-precision surface crack detection architecture for rolling steel production equipment based on image semantic segmentation is proposed. First, a coordinate attention-deep convolution generative adversarial networks (CA-DCGANs)-based data augmentation method is proposed to augment the original data set with high quality. Second, a crack detection model based on multiscale learning efficient spatial pyramid network (MLESPNetV2) is proposed. It effectively improves detection accuracy to obtain semantic information strongly correlated with crack using multiscale modeling and attention mechanism. Third, A semi-supervised learning method based on multiscale learning efficient spatial pyramid-generative adversarial network (MLESP-GAN) is proposed to solve the problem of insufficient labeled data and unstable training process. Finally, extensive experimental results on KolektorSDD and CAS-Crack data sets demonstrate that the proposed MLESPNetV2 significantly improves accuracy and real-time performance compared with the benchmark model. It is therefore suitable for deployment in industrial sites for real-time health monitoring of industrial equipment.

In intelligent transportation systems, efficient traffic management and population monitoring is attributed to the real-time scheduling of multiple Transportation Big Data (TBD) flows, which strongly supported risk assessment and control during the COVID-19 pandemic. However, as a complex and heterogeneous network, it is difficult to meet the priority and real-time requirements of multi-modal TBD flow scheduling. To improve the real-time performance of TBD flow scheduling, a scheme for Time-Triggered (TT) flow scheduling and Audio-Video Bridging (AVB) flow scheduling is proposed. First, an online scheduling method for TT flows (RFSD) is proposed, which uses Lion Swarm Optimization (LSO) algorithm for priority assignment and dynamic queues to adjust the scheduling order in real time. It ensures fairness in scheduling and effectively improves the utilization of time slot resources. Furthermore, an online scheduling method for AVB flows (RFSU) is proposed, which uses the Imperialist Competitive Algorithm (ICA) to construct the utility function for evaluating the scheduling value of AVB flows, effectively increasing the throughput of AVB flows. Finally, extensive experiments show that RFSD increases successful scheduling by 22% over the PAS algorithm. Compared to the TTA algorithm, RFSU achieves a 24% reduction in average delay and a 27% reduction in jitter.

The Maritime Internet of Things (MIoTs) provides intelligent information services for marine scientific research, emergency response and environmental monitoring by leveraging its wide coverage and ubiquitous connectivity. However, challenging maritime communication conditions and limited sea-based network resources hinder MIoT from meeting the evolving network quality of service requirements of growing maritime activities. This poses a significant challenge to ensuring real-time and reliable transmission of mixed traffic flows. To address issues such as link contention and transmission delays in software-defined MIoT systems, a deterministic scheduling and highly reliable routing mechanism based on cross-layer design is proposed. First, a deterministic scheduling mechanism for mixed traffic flows is introduced, which effectively reduces transmission delays and improves the schedulability of data flows. Second, a high-reliability, low-latency routing mechanism based on Double Deep Q Network (DDQN) is proposed, which is capable of dynamically screening neighbouring nodes based on real-time link and node states, thus facilitating fast and high-quality path selection. Extensive simulation results show that DSMTF improves flow schedulability by 28% compared to traditional algorithms, while HRLDQ increases the network packet delivery rate by 25.8% and reduces the average end-to-end delay by 23.6%.

With the rapid development of Industry 4.0, the industrial cyber-physical systems (ICPS) are expected to realize the digital sensing, automatic control, and refined management in smart factories. However, limited bandwidth resources and severe industrial interference make it difficult to meet the real-time and ultrahigh reliability in edge computing (EC)-based next-generation industrial automation networks. To tackle these challenges, in this article, we propose a real-time transmission optimization scheme to accelerate EC. First, we establish a hierarchical system model for smart manufacturing and automation scenarios. Then we present a power control optimization method based on noncooperative game to alleviate interference and reduce energy consumption. Finally, we propose a path optimization scheme based on Q-learning for low-latency and ultrahigh reliability transmission requirements. Extensive simulation results reveal that our proposals perform better in terms of transmission delay and packet-loss rate compared with traditional methods, and therefore, contributes to EC deployment in ICPS.

As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems.

As an important index of aero-engine, Remaining Useful Life (RUL) is the key content of prediction. Due to the good generation characteristics of Variational Auto-encoder (VAE) and Generation Adversarial Network (GAN) networks, this paper proposes a Health Index (HI) curve generation method based on VAE-GAN. After that, sensor sequence prediction is carried out through Bidirectional Long Short-Term Memory Network (BLSTM). The two networks are parallel, and then RUL prediction is carried out by synthesizing the data of the two networks. As far as the author knows, this is the first use of VAE-GAN in Prognostics Health Management (PHM). It is verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Finally, the results show that the VAE-GAN network is effective and superior in RUL prediction. At the same time, the proposed parallel network is superior to other RUL prediction methods by generating HI curves.

The demand for improving productivity in manufacturing systems makes the industrial Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In IIoT systems, there is an increasing demand for different types of industrial equipment to exchange stream data with different delays. Communications between massive heterogeneous industrial devices and clouds will cause high latency and require high network bandwidth. The introduction of edge computing in the IIoT can address unacceptable processing latency and reduce the heavy link burden. However, the limited resources in edge computing servers are one of the difficulties in formulating communication scheduling and resource allocation strategies. In this article, we use deep reinforcement learning (DRL) to solve the scheduling problem in edge computing to improve the quality of services provided to users in IIoT applications. First, we propose a hierarchical scheduling model considering the central‐edge computing heterogeneous architecture. Then, according to the model characteristics, a deep intelligent scheduling algorithm (DISA) based on a double deep Q network (DDQN) framework is proposed to make scheduling decisions for communication. We compare DISA with other baseline solutions using various performance metrics. Simulation results show that the proposed algorithm is more effective than other baseline algorithms.

Currently, a number of crowdsourcing‐based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real‐time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software‐defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA) is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.