In this paper, a robust Bayesian learning-based optimization algorithm is proposed to improve the performance of power allocation in wireless communications, addressing the problem of deep learning in wireless communications, which is unable to effectively quantify uncertainty. Specifically, by analyzing model uncertainty and aleatoric uncertainty in wireless communications, and combining model ensemble and power-constrained loss methods, a variational autoencoder power allocation algorithm (VAPA) is designed to reduce the impact of uncertainty on the optimization process significantly. Experimental results show that VAPA can effectively deal with model mismatches and outliers, significantly reducing the average power output while maintaining the communication rate. This study provides a new solution to the optimization problem in wireless communications that can facilitate the use of deep learning in communication scenarios with high reliability requirements.

Time series forecasting is a fundamental task in many domains, such as finance, energy, and intelligent systems. It is increasingly important in modern computing environments, including cloud computing and distributed resource management. However, real-world time series often exhibit complex temporal dependencies, high volatility, and multi-scale nonlinear patterns, making accurate forecasting challenging. To address these issues, this work proposes SVBTformer, a novel and effective forecasting model that enhances the Transformer-based Informer architecture with structured temporal learning modules. Specifically, SVBTformer integrates Savitzky–Golay (SG) filtering for noise reduction and signal smoothing, followed by Variational Mode Decomposition (VMD) to extract multi-resolution temporal components. Then, an improved Informer network called BTformer is employed to enhance the modeling capability for time series and strengthen the extraction of temporal dependencies. This work extensively experiments on publicly available benchmarks spanning multiple domains, including the ETT dataset for electric power demand, foreign exchange rates, and meteorological measurements. The results demonstrate that SVBTformer consistently outperforms state-of-the-art models, such as Informer and Autoformer, across most evaluation metrics, delivering superior accuracy and robustness. These gains underscore SVBTformer’s strong generalization capability and suitability for deployment in various real-world time series applications.

For the optimal power allocation problem in interference networks, traditional methods exhibit high computational complexity, while deep learning-based methods often underutilize domain knowledge. To address these challenges, we propose a novel approach termed Weighted Minimum Mean Square Error (WMMSE) full unfolding-based graph neural networks (FUWMMSE). This method integrates key modeling elements from traditional approaches with data-driven components of deep learning, employing the concept of deep unfolding-based methods. Specifically, by unfolding the iterative WMMSE method, optimizing hyperparameters, designing a graph neural networks (GNNs) network structure, augmenting modeling elements with GNNs, and leveraging the strengths of both model-based and data-based methods, our proposed neural network architecture is formulated. This design aims to overcome the limitations of insufficient domain knowledge utilization, complex computational structures, and poor generalization abilities observed in current unfolding-type methods. Theoretical analysis demonstrates that our method possesses the desirable properties of permutation equivariance, ensuring both interpretability and generalizability. Extensive numerical experiments validate that the FUWMMSE method has better performance and generalization capability than other unfolding class methods while maintaining a fast convergence speed.

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm calledGeneticSimulated-annealing-basedParticle swarm optimization withAuto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs.

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy.

Efficient and accurate identification of network anomalies is significant to network security systems. It is highly challenging to detect abnormal behaviors in the increasing network data accurately. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have unsatisfying detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. In this work, based on the improvement of variational autoencoders, stacked sparse shrink variational autoencoders (S3VAEs) are designed. In addition, anUnbalancedXGBoost classifier based onGenetic simulated annealing particle swarm optimization (UXG) is proposed. Finally, the feature extractor of S3VAEs is combined with the UXG classifier, and the anomaly detection model is obtained. Experimental results based on four real-life data sets demonstrate that the proposed anomaly detection model achieves higher classification accuracy and F1 than several state-of-the-art algorithms.

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios.

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem.

Resource usage prediction in cloud data centers is critically important. It can improve providers’ service quality and avoid resource wastage and insufficiency. However, the time series of resource usage in cloud environments is characterized by multidimensional, nonlinear, and high-volatility characteristics. Achieving high-accuracy prediction for time series with such characteristics is necessary but difficult. Traditional prediction methods based on regression algorithms and recurrent neural networks cannot effectively extract nonlinear features from data sets. Besides, many deep learning models suffer from gradient explosion or gradient vanishing during the training stage. Current commonly used prediction methods fail to uncover some vital information about the frequency domain features in the time series. To resolve these challenges, we design a Forecasting method based on the Integration of a Savitzky–Golay (SG) filter, a frequency enhanced decomposed transformer (FEDformer) model, and a frequency-enhanced channel attention mechanism (FECAM), named FISFA. It adopts the SG filter to reduce noise and smooth sequences in the raw sequences of resources. Then, we develop a hybrid transformer-based model integrating FEDformer and the FECAM, effectively capturing the frequency domain patterns. Besides, a meta-heuristic optimization algorithm, i.e., genetic simulated annealing-based particle swarm optimizer, is proposed to optimize key hyperparameters of FISFA. Then, FISFA predicts the future needs for multidimensional resources in highly fluctuating traces in real-life cloud environments. Experimental results demonstrate that FISFA achieves higher accuracy and performs more efficient prediction than several benchmark forecasting methods with realistic data sets collected from Alibaba and Google cluster traces. FISFA improves the prediction accuracy on average by 32.14%, 25.49%, and 27.71% over vanilla long short-term memory, transformer, and Informer methods, respectively.

In emerging applications of the Internet of Things, wireless sensor networks (WSNs) are often utilized to gather, track, and monitor data in remote areas with limited communication infrastructure. Since the majority of WSNs employ sensors powered by batteries, maintaining energy efficiency and conservation is crucial for ensuring their sustained operations over time. This work designs an Energy-efficient Unmanned aerial vehicle (UAV)-assisted hierarchical architecture of WSNs (EUW). EUW supports fast transmission of data collected from WSNs to a cloud server. Based on this architecture, this work first formulates a joint optimization problem for cluster head selection, time slot allocation, and UAV path planning to minimize the weighted sum of energy consumption of WSNs and that of a UAV. Then, a hybrid meta-heuristic algorithm named knowledge transfer-based particle swarm optimization (KTPSO) is designed, which utilizes previous optimization results to increase the convergence speed and find better results. Finally, numerical analysis and evaluation results are shown to demonstrate the efficiency of KTPSO and the proposed UAV-assisted architecture of hierarchical WSNs.

Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs’ caching and computation capacities while meeting MUs’ latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP’s system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

To deal with the increasing penetration of uncertainties caused by renewable generations and uncertain loads, this paper proposes a novel robust optimization model for the optimal power flow (OPF) problem of power systems. In the model proposed, the generation and the network topology are co-optimized, and then the proposed robust optimal power flow with transmission switching (ROPF_TS) model is converted to be a two-stage mixed-integer optimization model based on the duality theory. Based on Benders decomposition, the model is decomposed into a master problem and a slave problem, the master problem corresponds to the resolution of the deterministic optimal power flow with transmission switching problem under base-point mode, the slave problem corresponds to the robust optimal power flow with the given network topology, and then the feasible domain of the master problem is restrained by the Benders cuts developed by the slave problem. Finally, Example analyses are conducted to verify the validity of the proposed model and its solution methodology.

This paper proposes a novel optimization model for optimal reactive power flow concerning wind power generation, which considers generator voltage regulation characteristics. Synchronous generators (SGs) static excitation error control characteristics and doubly fed induction generator(DFIG) wind farm reactive power (var)-voltage control characteristics are considered. An extra inner potential node is introduced to reflect the excitation limits. Complementarity constraints are applied to represent the wind farm var-voltage control. Power fluctuation caused by the variability and uncertainty of wind generation are considered based on scenario analysis. Nonlinear primal-dual interior point algorithm is used to get the optimal strategies. Case study demonstrates effectiveness of the proposed approach.

With the spread of Internet of Thing end device, the usage of multiple devices with Internet connectivity is increasing. However the methodology that can effectively interconnect the data or integrate the services being executed on individual things is scarce. One way to solve this problem is converting the things into the flexible virtual objects. In this paper, a method to generate an IoT node into a programmable object form is introduced. In addition, Instance Hosting System and its structure that can service the objects as micro instances are provided. Functionality of the proposed system has been confirmed via realization of Instance Hosting Gateway in an Access Point environment. Through this research, the method on how to effectively and efficiently manage number of IoT nodes is suggested.

The Smart u-Learning System is designed to provide an interactive and social learning environment that accommodates emerging devices. This demonstration will show how teachers and students can use its interactivity and social features in live lecture situations.