Federated Learning (FL) is a critical enabler for intelligent Internet of Things (IoT) systems, allowing collaborative model training across heterogeneous devices while preserving data privacy. However, FL performance degrades significantly under non-Independent and Identically Distributed (non-IID) data due to weight divergence, and existing mitigation methods often introduce substantial overhead unsuitable for resource-constrained IoT deployments. We identify that weight divergence in non-IID FL stems primarily from insufficient cross-device knowledge exchange before aggregation—a contributing factor that has been underexplored in existing literature—and propose FedCAD, a communication-efficient FL framework that directly addresses this root cause. The core innovation is delayed cross-group aggregation, which strategically postpones global model updates until each model copy has been sequentially trained across all device groups, ensuring comprehensive knowledge integration. This mechanism is supported by dual-feature device clustering that creates meaningful group structures and fairness-aware adaptive selection that ensures representative participation—forming a causally linked pipeline where clustering provides structure, selection provides quality, and delayed aggregation provides the mechanism for thorough knowledge exchange. Extensive experiments on five benchmark datasets across 13 heterogeneous scenarios demonstrate that FedCAD consistently outperforms ten state-of-the-art methods with negligible additional communication overhead.

Neurological dysfunction encompasses a variety of diseases resulting from neural damage. Accurate assessment of neurological function is critical for diagnosis and the development of effective treatment plans. A significant number of patients with neurological disorders exhibit ocular abnormalities. Analyzing ocular status through eye movement capture plays a pivotal role in understanding various neurological dysfunctions. However, current methods of analyzing ocular status for neurological function assessments lack precision and objectivity, often relying heavily on physicians’ subjective judgment. This work proposes the Ocular-enhanced Face Keypoints Network (OFKNet), a facial keypoint detection model based on deep convolutional neural networks. OFKNet employs ConvNeXt as its backbone network and introduces a multi-scale input enhancement strategy. Additionally, a region enhancement module based on MobileNetV3 is designed to optimize features in the canthus area. Multiscale feature fusion and channel weighting are achieved through an improved Path Aggregation Network and Squeeze-and-Excitation modules. To validate OFKNet’s accuracy, we compared it with state-of-the-art models, including MediaPipe FaceLandmarker, InsightFace, Dlib68, and Dlib81, using a patient dataset we collected. Experimental results demonstrate that OFKNet outperforms existing models, particularly in calibration accuracy around the eyes. By monitoring eye movements in real-time, OFKNet ensures high-precision extraction of key points in each frame, accurately reflecting changes in patients’ ocular movements.

Accurate estimated time of arrival (ETA) prediction for long vehicular trips remains challenging in intelligent transportation systems (ITS) due to heterogeneous traffic patterns and limited local data availability. While federated learning (FL) addresses privacy concerns by decentralizing data training, traditional FL frameworks often struggle with high computational costs and poor adaptability to multi-task scenarios. To overcome these limitations, this paper proposes a Lightweight Multi-task Federated Learning (LMFL) framework for efficient and privacy-preserving ETA prediction. LMFL integrates a novel SE-CIFG, combining a Squeeze-Excitation (SE) attention module to prioritize critical spatio-temporal features and a Coupled Input and Forget Gate (CIFG) to simplify long-term traffic dependency modeling. Additionally, LMFL employs a Federated Gradient Compression Algorithm (FedGCA) to reduce communication overhead between edge and cloud using adaptive thresholding and sparse tensor encoding. Real-world traffic simulation dataset demonstrates that LMFL achieves significantly higher predictive accuracy compared to existing methods, achieving an average 17.1% improvement in prediction precision while reducing training time by 4.1%.

Federated learning (FL), propelled by advancements in artificial intelligence and edge computing, is increasingly employed in privacy-sensitive intelligent transportation systems (ITS). However, accurately predicting the estimated time of arrival (ETA) for long vehicular trips spanning multiple regions remains challenging due to heterogeneous traffic patterns and insufficient local data. Although single-region or centralized solutions offer higher accuracy, the former struggles to address complex intra-regional dynamics, while the latter raises significant privacy concerns by aggregating large volumes of mobility data from various regional authorities by a single giant entity(e.g., Google or Alibaba). To address these challenges, we introduce a novel Multi-Region Federated Learning (MRFL) framework to collect traffic data at each region-specific base station (BS) to predict the ETA of vehicles without sharing the collected data among traffic BSs, ensuring privacy and alleviating local data scarcity. Experimental evaluations on SUMO datasets demonstrate that MRFL significantly outperforms single-region learning in prediction accuracy and convergence speed, highlighting the efficacy of MRFL in enhancing ETA prediction in diverse traffic scenarios and offering a promising avenue for future advancements in ITS.

Given the forthcoming emergence of 6G communication models, the integration of terrestrial and nonterrestrial infrastructures is receiving increasing attention due to its widespread reach and broadcasting/multicast functions. The utilization of edge computing in space-related applications is appealing. However, the issue of positioning satellite edge servers and deploying services has yet to be resolved. Besides, existing studies mainly concentrate on energy consumption and latency problems, often neglecting the user mobility and potential privacy leakage issues in a mobile edge computing (MEC) environment. Yet it is crucial to optimize computation offloading and resource allocation for satellite-terrestrial edge computing networks. This work designs an innovative architecture for collaborative computation among multiple mobile devices and MEC servers deployed in ground stations and satellites. Based on this architecture, we formulate a nonlinear integer optimization problem to minimize the total system energy consumption. The model integrates several complex real-life nonlinear constraints, including operator cost, edge servers’ computing capacity, storage capacity, resource and latency, and privacy ones. To tackle the problem, this work proposes an advanced hybrid algorithm named a slime mold algorithm with genetic operations and individual updates of grey wolf optimizer (SMG2). SMG2 optimizes user mobility and privacy protection while optimizing server and service placement to minimize total energy consumption. Simulation experiments demonstrate that SMG2 reduces energy consumption drastically over the state of the art.

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. 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 to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption.

Edge computing offers a groundbreaking architecture for supplying computing, storage, and networking resources to propel the Internet of Things forward. By situating them at the network's edge, this model makes computational power more accessible to users. If tasks are executed entirely at the edge, energy and resource constraints of edge nodes may lead to poor performance. Therefore, it is widely recognized that offloading certain tasks to cloud data centers (CDCs), which possess abundant execution resources, is advantageous. However, implementing CDCs is not widespread and lacks flexibility in isolated regions. This presents challenges and high costs for reliably completing tasks quickly. Consequently, employing more adaptable unmanned aerial vehicles (UAVs) as CDCs in specific scenarios is crucial. The work presents the idea of mobile edge computing supported by the UAV. By considering the needs of user services, we enhance the energy efficiency of the UAV by optimizing their trajectories, transmission power, and computational load distribution. Furthermore, the work introduces an improved algorithm called GeneticSimulated-annealing-based Particle Swarm Optimizer (GSPSO) to optimize the energy efficiency of the UAV. Experimental simulations show that regarding the energy efficiency of the UAV, GSPSO exhibits superior search efficiency, surpassing genetic algorithm, simulated annealing, and particle swarm optimization by 7.39%, 15.03%, and 27.93%, respectively.

Many new IoT applications have emerged with the fast evolution of 5G and the Internet of Things (IoT). These applications place higher demands on network energy consumption and processing capabilities. Mobile edge computing (MEC) significantly enhances execution efficiency, while energy harvesting (EH) modules further augment the operational features of IoT devices. However, existing studies mainly concentrate on energy consumption and latency problems, often neglecting issues about user mobility and potential privacy leakage within the MEC environment. Therefore, optimizing computation offloading and resource allocation for MEC-enabled IoT networks is essential. This work proposes an innovative architecture with EH for collaborative computing between multiple mobile devices (MDs) and MEC servers. To tackle the problem, this work also proposes an advanced hybrid algorithm named Self-adaptive Bat Optimizer with Genetic operations and individual update of Grey wolf optimizer (SBG2). With SBG2, this work aims to minimize the energy consumption of MDs while providing user mobility and privacy protection. Simulation experiments show that SBG2 reduces energy consumption by 79.15%, 93.20%, and 89.58%, respectively, compared to the other three typical algorithms.

Multi-objective optimization algorithms are essential for addressing real-world challenges characterized by conflicting objectives. Although conventional algorithms are effective in exploring solution spaces and generating non-dominated solutions, solution quality and dynamic adaptability of true Pareto fronts need to be improved. This work proposes a multi-objective algorithm that integrates Non-dominated sorting genetic algorithm II (NSGA-II) and Multi-Objective Reinforcement Learning (N-MORL). N-MORL consists of two parts including upstream and downstream components. In the upstream component, this work improves the Variance-stabilized Multi-objective Proximal Policy Optimization (VMPPO) for enhanced convergence stability by adjusting its iteration mechanism. Additionally, this work optimizes variance networks and action sampling to balance exploration and exploitation, which improves experience sampling efficiency. This work adopts high-quality solution sets yielded by MORL as the initial solution set for downstream NSGA-II, guiding the exploration space and increasing the solution number. High-quality initial solutions significantly accelerate the iterative convergence speed of N-MORL. N-MORL provides the quality and the number of solutions, better covering or approaching the true Pareto front. Experimental results with five benchmark multi-objective functions demonstrate that N-MORL outperforms the other three multi-objective evolutionary algorithms regarding high-quality solutions with the same iterations.

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%.

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.

Hybrid cloud-edge systems combine the advantages of cloud computing and mobile edge computing (MEC) to achieve flexible integration and fluidity of data between the cloud and the edge. To address dynamic and stochastic loads caused by mobile users (MUs) and time-varying tasks, MEC network operators need to continuously migrate installed services among edge servers, significantly increasing network maintenance costs. Existing studies often overlook the service migration cost resulting from MU mobility. Therefore, we present a joint optimization scheme focusing on minimizing the operational cost of hybrid cloud-edge systems while considering the dynamic service migration cost induced by MUs. With the rapid development of 5G/6G technologies, many MUs require connectivity to edge nodes (ENs) or cloud data centers (CDCs) for processing. Minimizing the operational cost of hybrid cloud-edge systems while considering many heterogeneous decision variables is a challenge. To solve this complex high-dimensional mixed-integer nonlinear problem, we develop a novel deep learning-based evolutionary algorithm called autoencoder-based multiswarm gray wolf optimizer based on genetic learning (AMGG). Experimental results with real data demonstrate that AMGG achieves lower system cost by 49.69% while strictly meeting task latency requirements of MUs compared with state-of-the-art algorithms.

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system.

Autonomous driving poses high demands on computing and communication resources. Vehicular edge computing is presented to offload real-time computing tasks from connected and automated vehicles (CAVs) to high-performance edge servers. However, it brings additional communication overhead due to limited bandwidth, and increases delay of tasks. To solve it, this work first proposes an offloading architecture including multiple CAVs, roadside units and cloud. We minimize the total cost of a hybrid system by jointly considering task offloading ratios, and allocation of communication and computing resources. Furthermore, a mixed integer non-linear program is formulated and solved by a novel meta-heuristic algorithm called Self-adaptive Gray Wolf Optimizer with Genetic Operations (SGWOGO). SGWOGO achieves joint optimization of computation offloading among CAVs, roadside units and cloud, and allocation of their resources. Finally, real-life data-driven simulation results demonstrate that SGWOGO achieves lower cost in fewer iterations compared with its several state-of-the-art peers.