Time-series data are vital for preserving earthen ruins and evaluating wind erosion effects. Harsh conditions at these sites often lead to sensor degradation and significant data gaps. To tackle wind speed data imputation for such environments, we introduce a Multi-Scale Conditional Generative Adversarial Network (MSC-GAN) with a Transformer-based generator. This model integrates features across hourly, daily, and weekly scales, combined with real-time wind direction data and random noise. Utilizing the Transformer’s ability to model long-range dependencies and multi-scale information, MSC-GAN adeptly manages complex missing data patterns. We validate MSC-GAN using nearly two years of near-surface wind speed data from the Suoyang City earthen ruins. Our experimental results reveal that MSC-GAN substantially improves imputation accuracy—by 40.2% for short gaps and 6.7% for long gaps—over traditional methods. Code is available at https://github.com/zizhou001/msc-gan.

Federated learning aims to jointly train a general and robust global model through users’ local training models (not local private data). Therefore, a crucial step is how to aggregate local models, which poses a challenge when the data among users is non-independent and identically distributed (non-i.i.d.). This paper proposes a new federated model-pooling algorithm, FedMEDA. From the perspective of model evaluation, this algorithm monitors the model pool, screens out high-quality local models beneficial to the construction of the global model, and combines them to achieve a more robust aggregation. We verify that personalized models can be accurately evaluated through a simple weighted F1-score. Our experiments validate the superior performance of FedMEDA in scenarios where the Dirichlet distribution is used to simulate non-i.i.d. user data. In addition, FedMEDA is compatible with recent work on standardizing user-model training. Only the aggregation strategy needs to be changed while keeping the other parts of the federated learning algorithm unchanged.

Mobile edge computing (MEC) enabled artificial intelligence-generated content (AIGC) has garnered considerable attention. To support AIGC metaverse applications within MEC in Internet of Things (IoT) networks, it is effective to offload computation tasks, particularly those involving neural networks generative in AIGC, from mobile devices to edge clouds. Existing solutions typically assume the availability of a dedicated and powerful edge server for each user with single modal data, which can handle the entire AIGC service offloading. However, the practical availability of such dedicated and powerful servers may be limited, necessitating the utilization of less capable alternatives. Thus, we propose the multimodal parallel offloading AIGC framework which partitions multimodal content and offloads partial diffusion tasks to multiple servers. Our proposed scheme accelerates mobile deep vision multimodal metaverse applications through parallel offloading provided by multiple servers. We further utilize the generative AI scheme to solve offloading problems to adapt the dynamic and available communication and computing resource in wireless IoT network. Our framework proposed a multimodal parallel diffusion offloading scheme with integrating the recurrent region proposal prediction algorithm to optimize communication and computing resources while minimizing delay. Simulation results show that our approach can significantly reduce delay compared to conventional algorithms.

In the domain of federated learning, traditional federated averaging algorithms encounter difficulties in maximizing accuracy in non-IID data circumstances due to the large data volume and the low model efficiency ratio of participants. To address the problem of non-IID data among participants in FL, this paper puts forward a novel greedy aggregation algorithm based on the Fapmodel evaluation, named FedEGA. FedEGA incorporates models from each participant into the global model successively as potential components and builds the global model by evenly distributing multiple model weights that are fine-tuned with various hyperparameter configurations. During the construction process, a dynamic weight parameter mechanism is adopted to balance the accuracy and precision evaluation of the model. By using validation set scores on the central server, FedEGA ranks models in descending order, ensuring that the global model does not perform worse than the best individual model on the retained validation set, thereby alleviating issues caused by client drift and the hindrance to maximizing accuracy due to non-IID data. Experiments on public datasets and real-world data show that our algorithm surpasses classic FL algorithms such as Federated Averaging (FedAvg), Federated Proximal (FedProx) optimization, and Federated Self-Regularization (FedSR) in terms of maximum accuracy and communication efficiency in non-IID scenarios. Additionally, experiments with imbalanced data confirm its stronger robustness and generalization capabilities.

Summary Deep learning has made great achievements in the field of speech recognition. With the popularization of embedded devices such as intelligent speaker and the demand for dialect interaction scenes, it poses great challenges to far‐field speech recognition and dialect language recognition. In order to solve the dialect language recognition of embedded devices in far‐field speech recognition, we propose a deep learning neural network model with multitask learning. First, the audio is passed through the end‐to‐end noise reduction model to improve the effect of audio recognition. Then we define dialect recognition as the main task and dialect area as the auxiliary task, using the multitask learning method to improve the accuracy of dialect classification. The experimental results show that the end‐to‐end noise reduction model can improve the accuracy of audio recognition, and the best effect can be 7.54% higher than the baseline, and the accuracy of dialect language recognition can be improved by about 5% through multi task learning model.

Summary As an important technology in computer vision, video‐based human action recognition has a great commercial value, which has attracted extensive attention in the field of computer vision and pattern recognition in both academia and industry. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human–computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this article, adopting the key frame extraction and multifeature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition.

On-device deep learning enables mobile devices to perform complex tasks, such as object detection and voice translation, regardless of the network condition. The advanced deep learning model gives an excellent performance, also leads to a heavy burden on resource-limited devices (i.e., mobile devices). To speed up the on-device deep learning. Prior studies focus on developing lightweight network architecture for real-time inference by sacrificing model accuracy. This paper presents ATO-EDGE: adaptive task offloading for deep learning based on edge computing. Considering three optimization goals, energy consumption, accuracy, and latency, ATO-EDGE leverages an offline pre-trained model to select a suitable deep learning model on a specific device to process the given task. We apply our approach to object detection and evaluate it on Jetson TX2, Xilinx ZYNQ 7020, and Raspberry 3B+. The deep learning model candidates contain ten typical object detection models trained on Microsoft COCO 2017 dataset. We obtain, on average, 28.25%, 35.44%, and 0.9 improvements respectively for latency, energy consumption, and mAP (mean average precision) when compared to the SOTA DETR model on the Raspberry Pi.

With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.

Interference management and power transfer can provide a significant improvement over the 5th generation mobile networks (5G) dense Internet of Things (IoT) heterogeneous networks (HetNets). In this paper, we present a novel approach to simultaneously manage inferences at the downlink (DL) and uplink (UL), and to identify opportunities for power transfer and additional UL transmissions integrated with existing protocols and infrastructures for enhanced inter-cell interference coordination (eICIC) protocol in dense IoT HetNets, while considering practical non-linear energy harvesting (EH) model. The design is formulated as the joint optimization of interference aware UL/DL decoupling, airtime resource allocation and energy transfer. The key insight of our algorithm is to translate the original, intractable joint-optimization problem into a problem space where a good approximate solution can be quickly found. We evaluate our scheme through theoretical analysis and simulation. The evaluation shows that our approach improves the system utility by over 20% compared to start-of-the-art in dense IoT HetNets. Compared to alternative schemes, our approach maintains the best user fairness and rate experience and can solve the problem in a fast and scalable way.

The adaptive enhanced inter-cell interference coordination (eICIC) configuration is critical for interference management. This problem is challenging especially from energy efficiency perspective and taking individual user fairness into account. Therefore, we formulate a max-min energy efficiency eICIC configuration problem, i.e., determining the number of almost blank subframes (ABS) and user associates with macro or pico while considering fairness jointly. Since the mixed combinatorial and non-smooth features of the problem, an iterative- distributed algorithm is proposed with using fractional programming and Lagrangian dual theory. Numerical results demonstrate the effectiveness of the proposed algorithm and verify fairness achieved among users, and validate the tradeoff between energy efficiency and fairness for eICIC in HetNets comparing with the existing algorithms.

Web browsing is an activity that billions of mobile users perform on a daily basis. Battery life is a primary concern to many mobile users who often find their phone has died at most inconvenient times. The heterogeneous multi-core architecture is a solution for energy-efficient processing. However, the current mobile web browsers rely on the operating system to exploit the underlying hardware, which has no knowledge of individual web contents and often leads to poor energy efficiency. This paper describes an automatic approach to render mobile web workloads for performance and energy efficiency. It achieves this by developing a machine learning based approach to predict which processor to use to run the web rendering engine and at what frequencies the processors should operate. Our predictor learns offline from a set of training web workloads. The built predictor is then integrated into the browser to predict the optimal processor configuration at runtime, taking into account the web workload characteristics and the optimisation goal: whether it is load time, energy consumption or a trade-off between them. We evaluate our approach on a representative ARM big.LITTLE mobile architecture using the hottest 500 webpages. Our approach achieves 80% of the performance delivered by an ideal predictor. We obtain, on average, 45%, 63.5% and 81% improvement respectively for load time, energy consumption and the energy delay product, when compared to the Linux heterogeneous multi-processing scheduler.

The densification and expansion of heterogeneous cellular networks (HetNets) pose new challenges on interference management and reduction of energy consumption. The 3GPP has proposed enhanced intercell interference coordination (eICIC) by making a macrocell silent in almost blank subframes (ABSs) to mitigate interference for low power base stations (BSs) in HetNets. However, energy efficiency (EE) is very crucial for the deployment of a large number of low power nodes as they consume a lot of energy. In this work, we develop a novel EE-eICIC algorithm to determine the amount of ABSs and user equipment (UE) that should associate with picocells or macrocells from energy efficiency perspective. Due to the nonsmooth and mixed combinatorial features of this formulation, we focus on a suboptimal algorithm design. Using generalized fractional programming and the convex programming theory, we propose an iterative and relaxed-rounding algorithm to solve the problem. Numerical results illustrate that the proposed EE-eICIC algorithm achieves superior performance in comparison with state-of-the-art methods in terms of energy efficiency of both system and user.