Multivariate time series classification (MTSC) requires a model capable of capturing both localized temporal patterns and long-range dependencies while effectively modeling complex inter-variable relationships. Existing convolutional neural network (CNN)-based capsule models suffer from limited receptive fields, constraining their ability to model long-range dependencies, while transformer-based capsule models rely solely on self-attention, which, despite its effectiveness in capturing global features, struggles with preserving local structures and efficiently processing long sequences. To overcome these limitations, we propose KACapMamba, a Knowledge-Aware State-Space Capsule Network, which integrates three attentive Mamba blocks with a routing layer to achieve hierarchical temporal modeling. Unlike conventional transformer-based methods that primarily depend on self-attention for global dependency modeling, each attentive Mamba block in KACapMamba fuses 1-dimensional CNNs, self-attention, state-space module (SSM), and mutual cross-attention, enabling a more structured and adaptive feature representation. Self-attention ensures effective long-range dependency modeling, while SSM provides a recurrent-state mechanism, inherently better suited for sequential processing compared to purely attention-based architectures, thereby enhancing temporal continuity and long-term pattern retention. Notably, mutual cross-attention addresses the limitations of traditional fusion strategies such as element-wise addition or multiplication, which lack the capacity to selectively enhance relevant features. By dynamically reweighting interactions between features, mutual cross-attention enables more expressive, context-aware representations, leading to improved feature disentanglement and inter-variable modeling. Additionally, the routing layer further enhances hierarchical feature disentanglement by refining capsule activations, reinforcing structural coherence and feature selectivity. Experiments conducted across the UEA benchmark archive demonstrate that KACapMamba consistently achieves the highest ‘win’/‘tie’/‘lose’/‘best’ ratios when evaluated against 10 leading transformer and Mamba architectures under both$Accuracy$and$F_{1}$metrics. Moreover, in comparison with 22 state-of-the-art MTSC models, it again secures the most favorable performance profile, demonstrating a clear and statistically supported advantage across both evaluation measures.

In recent years, uncrewed aerial vehicles (UAVs) have become increasingly prevalent for collecting environmental data from various wireless sensors. However, existing research on employing UAVs to collect data from wireless sensors has often ignored the heterogeneous requirements of sensors. In this paper, we investigate joint deployment, user association, and power allocation for data collection in the UAV-assisted wireless sensor network to accommodate the heterogeneous requirements of sensors, where a novel satisfaction function is designed for three types of sensors, including sensors with delay requirements, sensors with energy consumption requirements, and sensors with both delay and energy consumption requirements. Leveraging the satisfaction function, we formulate the optimization problem aimed at jointly optimizing the positions of UAVs, the association between sensors and UAVs, and the power allocation of sensors to maximize overall satisfaction of sensors. In order to effectively address the considered problem, we decompose it into two subproblems, i.e., joint UAV deployment and user association subproblem, and transmission power allocation subproblem. An enhanced human evolutionary algorithm is developed to tackle the joint UAV deployment and user association subproblem, and the Lagrange dual method and gradient descent method are employed to solve the transmission power allocation subproblem. The suboptimal solution is achieved by iteratively addressing the two subproblems until convergence of the proposed enhanced Lagrange and gradient descent-based human evolutionary optimization algorithm is attained. Extensive simulations demonstrate the effectiveness of the proposed algorithm in enhancing overall satisfaction of sensors, underscoring its significant advantages in managing heterogeneous network environments.

This paper studies the joint optimization of multi-objective dependent task scheduling, resource allocation, and service caching in an aerial-ground integrated mobile edge computing system that includes multiple uncrewed aerial vehicles (UAVs). These UAVs, in coordination with a high-altitude platform, work together to process numerous dependent tasks collected by the UAVs. The optimization problem involves two conflicting objectives that need to be minimized simultaneously: the average execution delay of all dependent tasks and the average energy consumption of all UAVs. The conflict between the two objectives makes the problem quite challenging. Recently, some multi-objective approaches, such as multi-objective evolutionary algorithms (MOEAs), have been introduced to address dependent task scheduling. However, these approaches often suffer from premature convergence and tend to fall into local optima. To address these issues, we propose a modified MOEA based on decomposition that incorporates two performance-improving strategies. The first one is a probability-based neighborhood search strategy that selects two individuals to update neighborhood individuals based on the neighborhoods and external population, thereby improving population updating efficiency. The second one is a dynamic voltage and frequency scaling-based energy reduction strategy that further enhances the quality of solutions by adjusting the computing frequencies. Experimental results verify that the proposed algorithm obtains a number of outstanding nondominated solutions and achieves a better balance between objectives compared with several algorithms.

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called CapMatch. CapMatch gracefully hybridizes supervised learning and unsupervised learning to extract rich representations from input data. In unsupervised learning, CapMatch leverages the pseudolabeling, contrastive learning (CL), and feature-based KD techniques to construct similarity learning on lower and higher level semantic information extracted from two augmentation versions of the data, "weak" and "timecut," to recognize the relationships among the obtained features of classes in the unlabeled data. CapMatch combines the outputs of the weak- and timecut-augmented models to form pseudolabeling and thus CL. Meanwhile, CapMatch uses the feature-based KD to transfer knowledge from the intermediate layers of the weak-augmented model to those of the timecut-augmented model. To effectively capture both local and global patterns of HAR data, we design a capsule transformer network consisting of four capsule-based transformer blocks and one routing layer. Experimental results show that compared with a number of state-of-the-art semi-supervised and supervised algorithms, the proposed CapMatch achieves decent performance on three commonly used HAR datasets, namely, HAPT, WISDM, and UCI_HAR. With only 10% of data labeled, CapMatch achieves values of higher than 85.00% on these datasets, outperforming 14 semi-supervised algorithms. When the proportion of labeled data reaches 30%, CapMatch obtains values of no lower than 88.00% on the datasets above, which is better than several classical supervised algorithms, e.g., decision tree and -nearest neighbor (KNN).

Network function virtualization (NFV) enables the provision of different quality of service (QoS) levels through service function chains (SFCs), where NFV outsources big data tasks of end users to nearby edge servers. In multi-access edge computing (MEC), its dynamic and uncertainty nature poses great challenges to the SFC placement problem, which requires optimizing multiple potentially-conflicting objectives, such as network latency and load balancing. Moreover, user preferences may vary along with time, adding another layer of complexity to the problem. To address the problem above, we propose a novel distributed deep reinforcement learning (DRL) architecture based on a spatio-temporal encoder (STE), denoted as DDRL-STE. DDRL-STE is featured with equal-weight pre-training and transformer-based STE. Experimental results show that DDRL-STE outperforms three state-of-the-art DRL algorithms regarding latency and load balancing under three well-known network topologies, exhibiting its excellent potential in exploration and generalization.

This paper investigates the problem of energy-efficient trajectory optimization with wireless charging (ETWC) in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system. A UAV is dispatched to collect computation tasks from specific ground smart devices (GSDs) within its coverage while transmitting energy to the other GSDs. In addition, a high-altitude platform with a laser beam is deployed in the stratosphere to charge the UAV, so as to maintain its flight mission. The ETWC problem is characterized by multi-objective optimization, aiming to maximize both the energy efficiency of the UAV and the number of tasks collected via optimizing the UAV's flight trajectories. The conflict between the two objectives in the problem makes it quite challenging. Recently, some single-objective reinforcement learning (SORL) algorithms have been introduced to address the aforementioned problem. Nevertheless, these SORLs adopt linear scalarization to define the user utility, thus ignoring the conflict between objectives. Furthermore, in dynamic MEC scenarios, the relative importance assigned to each objective may vary over time, posing significant challenges for conventional SORLs. To solve the challenge, we first build a multi-objective Markov decision process that has a vectorial reward mechanism. There is a corresponding relationship between each component of the reward and one of the two objectives. Then, we propose a new trace-based experience replay scheme to modify sample efficiency and reduce replay buffer bias, resulting in a modified multi-objective reinforcement learning algorithm. The experiment results validate that the proposed algorithm can obtain better adaptability to dynamic preferences and a more favorable balance between objectives compared with several algorithms.

This paper studies the age of information (AoI) and energy tradeoff (AET) problem in an aerial-ground collaborative mobile edge computing system, where a high-altitude platform and an unmanned aerial vehicle (UAV) work together to offer computing services for ground devices (GDs). The AET problem is formulated as a multi-objective optimization problem (MOP) that aims at simultaneously minimizing the total AoI of GDs and total energy consumption of the UAV by optimizing its flight paths and task offloading ratios. Addressing the AET problem poses a significant challenge due to the inherent conflict between the two objectives. The existing methods cannot well address the MOP because they adopt the linear combination to transform an MOP into a single-objective optimization problem using fixed weights (i.e., preferences), ignoring the conflict between objectives. Moreover, user preferences may change over time in dynamic MEC systems. To overcome these challenges, we first build a multi-objective Markov decision process model with a vectorial reward for the AET problem. There are one-to-one relationships between each component of the reward and one of the two objectives. Then, we propose a multi-objective learning algorithm based on proximal policy optimization (PPO), which primarily comprises a training phase and an evolutionary phase. The former adopts multi-objective PPO to iteratively optimize multiple learning individuals, aiming to obtain a nondominated policy set. The latter employs a genetic operator to further improve the quality of each policy in the set. Specifically, the crossover and mutation operators operate at the parameter level of policy networks, avoiding stagnation and premature convergence. The experiment results validate that the proposed approach obtains a set of excellent nondominated policies and a favorable balance between objectives. Moreover, the proposed approach achieves improvements of at least 39.8%, 2.1%, and 15.3% regarding AoI, energy consumption, and cost compared with several algorithms.

In this paper, we investigate the joint unmanned aerial vehicle (UAV) deployment and resource allocation problem to minimize the latency of multi-UAV-assisted computation offloading in air-ground integrated millimeter-wave (mmWave) networks, in which UAVs have both computing and relaying capabilities, thereby providing more opportunities for ground user equipments (UEs) to access the moble edge computing (MEC) servers with rich computing resources. Moreover, the study also takes into account the dynamic interference experienced by UEs due to different uploading completion times during the computation offloading process. To efficiently address the considered non-convex problem, we split it into four subproblems, i.e., UAV deployment, MEC server selection, computation resource and task ratio allocation, and power allocation subproblems, and solve them iteratively. Specifically, the first one is solved by three-dimensional-strategy iterative weekly acyclic game, the second one is addressed by Markov Approximation approach in which the third one is solved by the interior point method at each iteration, and the last one is solved by whale optimization algorithm (WOA). Finally, extensive simulations are provided to demonstrate the effectiveness of the proposed approach, and results have shown the approach can effectively mitigate the effect of blockage on mmWave transmissions and reduce the total latency of all UEs, particularly in scenarios where the communication bandwidth is limited or data volumes of tasks are large.

This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV has two roles to play: MEC server or wireless relay. The UAV makes task offloading decisions online, in which the collected tasks can be executed locally on the UAV or offloaded to the BS for remote processing. The TCTO problem involves multi-objective optimization as its objectives are to minimize the task delay and the UAV's energy consumption, and maximize the number of tasks collected by the UAV, simultaneously. This problem is challenging because the three objectives conflict with each other. The existing reinforcement learning (RL) algorithms, either single-objective RLs or single-policy multi-objective RLs, cannot well address the problem since they cannot output multiple policies for various preferences (i.e. weights) across objectives in a single run. An evolutionary multi-objective RL (EMORL) algorithm is applied to address the TCTO problem. We improve the multi-task multi-objective proximal policy optimization of the original EMORL by retaining all new learning tasks in the offspring population, which can preserve promissing learning tasks. The simulation results demonstrate that the proposed algorithm can obtain more excellent non-dominated policies by striking a balance between the three objectives regarding policy quality, compared with two evolutionary algorithms, two multi-policy RL algorithms, and the original EMORL.

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This article studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.

Over the years, a large number of deep learning algorithms have been developed for time series classification (TSC). These algorithms were usually invented by researchers with prior knowledge and experience. However, it is a critical challenge for beginners to design decent structures to address various TSC problems. To this end, we propose a robust neural temporal search (RNTS) framework for identifying the relationships and features in TSC data, which mainly contains a temporal search network and an attentional LSTM network. To be specific, inspired by the idea of neural architecture search (NAS), the temporal search network automatically transforms its structure for each dataset according to its characteristics, responsible for extracting basic features. The attentional LSTM network is used to explore the complex shapelets and relationships the former may ignore. Experimental results demonstrate that RNTS achieves the best overall performance on 24 standard datasets selected from the UCR 2018 archive, in terms of three measures based on the top-l accuracy, compared with a number of state-of-the-art approaches.

In mobile-edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This article studies the tradeoff between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e., ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed: 1) the problem-specific population initialization scheme uses a latency-based execution location (EL) initialization method to initialize the EL (i.e., either local SMD or MEC server) for each task and 2) the dynamic voltage and frequency scaling-based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and metaheuristics in terms of the convergence and diversity of the obtained nondominated solutions.

This paper studies the problem of how to efficiently minimize network coding resource. A modified particle swarm optimization (PSO) algorithm is proposed to tackle the problem, with the concept of path-relinking (PR) integrated into the evolutionary framework. As an efficient local search heuristic that makes use of problem-specific domain knowledge, PR helps strike a better balance between global exploration and local exploitation for the evolutionary search. Simulation results demonstrate that the proposed algorithm overweighs a number of existing and commonly used evolutionary algorithms (EAs) in terms of the solution quality, convergence, and computational time.