The geological environment remote sensing interpretation task imposes higher demands on deep learning models' computational efficiency and deployment performance. As an efficient lightweight strategy, channel pruning has been widely applied in computer vision. However, when applied to geological remote sensing tasks, existing pruning methods struggle to balance interpretation accuracy and computational cost effectively, mainly due to the complexity of geological environment data and the constraints of resource-limited deployment. Moreover, although reinforcement learning-based pruning strategies have been proposed to overcome these limitations, they often suffer from limited exploration capability and unstable training performance. To address these issues, we propose a dynamic pruning method (SACRL) incorporating the maximum entropy mechanism, which enhances the explorability and stability of the pruning strategy by introducing an entropy regularity term in the reinforcement learning objective function. Experiments demonstrate that, compared to conventional pruning methods such as AMC and Taylor, SACRL demonstrates better performance in terms of overall classification accuracy (OA), mean Intersection over Union (mIoU), and mean Average Precision (mAP). Furthermore, deploying the pruned model on resourcelimited devices, such as the Jetson AGX Orin, further validates its adaptability in real-world applications and highlights its potential in remote sensing interpretation of geological environments.

Efficient processing and sharing of geological environmental data are crucial for sustainable development and informed decision-making. However, current analysis methods struggle with low efficiency and resource utilization, especially in complex computational tasks. This paper proposes a parallel processing framework based on container orchestration that systematically improves the efficiency of geological environment data analysis by integrating container technology and complex task processing optimization strategies. Leveraging containerized processing, we established a standardized packaging and deployment mechanism for geological environmental data analysis algorithms, enabling flexible encapsulating and management of multiple models. In addition, we proposed a complex task decomposition method for pipeline parallelism and realized multicontainer collaborative geological environment data processing in a distributed environment based on container orchestration. For enhancing the efficiency of complex task processing purposes, this paper proposed a task scheduling optimization strategy based on the dynamic merging of directed acyclic graph, which improves resource utilization and processing speed through task merging. Experimental results demonstrate that the proposed framework enhances processing efficiency by over 50% in typical geological environmental data analysis scenarios, while improving resource utilization by$\mathbf{4 8 \% - 6 9 \%}$. It exhibits strong reliability and scalability, offering technical support for intelligent analysis and service sharing of geological environmental data.

The Internet of Vehicles (IoV) significantly enhances the capabilities for road information collection and processing by enabling real-time connectivity between vehicles, infrastructure, and cloud systems. Leveraging these technological advantages, multi-vehicle collaborative real-time crack detection is expected to become a crucial method to guarantee the health and safety of infrastructures. Due to different vehicles being equipped with various types of sensors, the collected data are heterogeneous, and the limited computational resources of onboard units obstacle the efficient data processing and effective crack detection in infrastructures. To address these challenges, this study proposes a novel Distributed Edge Computing for Crack detection (DECCD), vehicle serve as edge nodes that locally collect and analyze data. The central node continuously aggregates and processes data from multiple edge nodes to train a robust model. This model is periodically refined and then distributed to edge nodes, where it is further training to detect cracks. A multi-scene dataset, called CrackMS, is constructed by integrating multi-scene datasets of different modalities, and the data are enhanced by Deep Convolutional Generative Adversarial Network (DCGAN) to simulate the complexity of crack data acquired by vehicles. A crack detection model, called the Multi-Scene Auxiliary Prediction Network (MSA-Net), which includes an AUX module and a Scene module is proposed to optimize feature extraction and processing of scene changes. Then a lightweight student model with similar performance is trained by knowledge distillation. Experimental results show that the proposed model, while maintaining a lightweight design, achieves a significant improvement in detection accuracy compared to baseline models.

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of identifying top-k Socio-Spatial co-engaged Location Selection (SSLS) for users in a social graph, that selects the best set of k locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) spatially and socially relevant to the user and her friends, and (ii) diversified both spatially and socially to maximize the coverage of friends in the socio-spatial space. To address the NP-hard and challenging problem, we first develop an exact solution by designing some pruning strategies, and also develop an approximate solution by deriving relaxed bounds and advanced termination rules. To accelerate the efficiency, we further develop a fast exact approach and a meta-heuristic approximate approach. Finally, extensive experiments are conducted to evaluate the performance of our proposed algorithms against three adapted existing methods using four real-world datasets.

Spatial keyword query has attracted wide-spread academic and industrial concerns due to the popularity of location-based services and Internet of Things. To efficiently support the online query processing, the data owners need to outsource their data to cloud platforms. However, the outsourcing services may raise privacy leaking issues. Moreover, access control, another important security concern, is largely ignored. Therefore, we first propose and formalize the problem of secure boolean spatial keyword query under lightweight access control while guaranteeing the widely acceptedadaptive indistinguishability against chosen keyword attackmodel. Then, we devise a novel hybrid Bloom filter encoding strategy, including a linear embedding and exponent-based transformation schemes and a secure index structure, calledSAGTree. They can maintain both geo-text and access policy information together in a secure way while answering the encrypted queries under access control without decryption. Finally, we present the in-depth security analysis and demonstrate the performance of our proposed algorithms.

With the rapid prosperity of wireless communication, a new era of smart cities has arisen. During the implementation of smart cities, electricity supply plays a profound role and needs to be guaranteed. To do this, a health model with terminal sensors data is usually adopted to enable the security of the transmission line. However, due to the extreme weather condition and the interference of sensors, some transmitted values are missing, which renders the sensors unreliable. Unfortunately, the existing prediction methods have a lower accuracy and there still exists a gap between the real value and the predicted value. In this article, we propose a novel data fusion model, named as KNN-XGBoost, to predict the missing values. Specifically, in this model, it mainly consists of two parts: 1) KNN stage: a KNN model is used to find${K}$nearest labels as new attributes and 2) XGBoost: an XGBoost model is applied to predict missing attribute values. The experimental results have demonstrated that our proposed model can improve the accuracy from 1.11% to 4.05%, 0.55% to 1.84%, and 1.10% to 4.04% than the other methods under the three grid health models, respectively.

The authenticity and integrity of sensed data in the data collection stage is a very critical aspect for the smart city industrial environment. They impact the accuracy of data analysis and the objectivity of making decisions. However, how to identify attack behaviors from environmental interference and establish a secure route to transmit data for resource-constrained terminals are challenging problems. To address these problems, in this article, we propose a trust-based security system (TSS). In TSS, we first design a trust model using binomial distribution for calculating the node's trust value and a third-party recommendation scheme for improving the objectivity of trust value. Then, we propose a trust management scheme for preventing theon–offattack. After that, we design a secure routing protocol, which is used to balance the security, transmission performance, and energy efficiency. Finally, the analytical results of the TSS are evaluated with extensive simulation experiment.

Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients' tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.

Summary PM2.5 time series have the features of non‐stationary and nonlinear. Existing forecasting methods for PM2.5 cannot achieve high accuracy for they have ignored the potential characteristics of PM2.5 time series. Aiming at this problem, a hybrid approach using local mean decomposition and Support Vector Regression (SVR)‐Elman (LSE) is firstly proposed in this paper to analyse 5days ahead PM2.5 concentrations for forecasting in Wuhan, China: (1) the meaningful PF1‐PF5 components are extracted from original PM2.5 time series by local mean decomposition; (2) the first high‐frequency product function is managed by using the SVR model, such that the relationship between PM2.5 and other air quality data can be revealed accurately; (3) the other components are trained by Elman model with the sliding window method. Experimental results show that, compared with multiple linear regression, autoregressive integrated moving average, BP neural network, and SVR models, the proposed hybrid LSE model‐based approach exhibits the best performance in terms of R2, MAE, MAPE, RMSE, while it is applied for forecasting in real datasets. Copyright © 2016 John Wiley & Sons, Ltd.