Numerous existing deep learning models for time-series classification (TSC) tend to overlook the intricate interplay between higher-and lower-level semantic information. While the focus is often on extracting higher-level semantics from lower-level sources, the reciprocal influence of lower-level information on higher levels is undervalued. To address this, we propose an ensemble transitive bidirectional decoupled self-distillation (ETBiDecSD) method for TSC. ETBiDecSD enhances the robustness of higher-level semantic information using an average feature ensemble (AFE) method to amalgamate the output from each level. Simultaneously, the integrated features are transmitted to each lower level through a directional decoupled distillation (DD) structure. Additionally, to promote deep interaction between higher-and lower-level semantic information, ETBiDecSD introduces a transitive bidirectional DD (TBDD) structure, facilitating the transfer of target-class and nontarget-class knowledge between higher and lower levels. Experimental results demonstrate that whether a fully convolutional network (FCN) with four convolutional blocks or InceptionTime with four Inception blocks is used as the baseline, ETBiDecSD outperforms a quantity of well-established self-distillation algorithms across 85 widely used UCR2018 datasets, as evidenced by the metrics “win”/“tie”/“lose” and avg. rank, which are derived from accuracy andF1-scores. Notably, when compared to a nonself-distillation FCN, ETBiDecSD achieves “win”/“tie”/“lose” results of 64/4/17 in terms of accuracy and 65/4/16 in terms ofF1-score. Similarly, in comparison to a nonself-distillation InceptionTime, ETBiDecSD attains “win”/“tie”/“lose” results of 60/12/13 for accuracy and 57/12/16 forF1-score.

This paper studies a distributed semantic communication (SC) problem for multivariate time series forecasting tasks in edge environments, with heterogeneous clients considered. At the client side, a semantic encoder is composed of a number of federated blocks and this number is subject to local resource availability. Each federated block consists of a patch-wise attention module (PAM) and a federated adapter, extracting semantic information for efficient transmission across wireless channels. Based on the federated adapters, this paper proposes an SC-oriented heterogeneous federated learning architecture, named SC-FedAda. SC-FedAda adopts self-distillation to facilitate cross-client and cross-layer knowledge sharing, enabling efficient collaborative inference. At the edge server, semantic signals are fed into a channel decoder and then a semantic decoder. The semantic decoder consists of a PAM and a fully connected network for forecasting tasks. Simulation results demonstrate that SC-FedAda outperforms four state-of-the-art federated learning-based structures under three types of wireless channels, i.e. SC-FedAda achieves much lower forecasting loss on three widely-used time series datasets, particularly in low signal-to-noise ratio scenarios.

Multi-user semantic communication (MUSC) has emerged as a promising paradigm for future 6G networks and applications, where massive clients (e.g., mobile devices) collaboratively construct a global semantic decoder without sharing their local data. However, due to the lack of direct access to clients’ data, MUSC is vulnerable to data poisoning attacks (DPAs), wherein malicious participants send updates derived from poisoned training samples. Current defense techniques against DPAs are designed for traditional networks and are not directly applicable to MUSC. In this paper, we propose an effective attack-defense game framework, denoted as DPAD-MUSC, tailored to defend against DPAs during image transmission for MUSC. First, we determine each attack-type's optimal attack policy based on reinforcement learning, with the aim of strengthening the attack while avoiding detection. To generate adversarial samples accordingly, we devise an adversarial samples generator (ADV-Generator) based on conditional generative adversarial network (CGAN). Then, we introduce an attack defender (DPA-Defender) to detect data poisoning attacks and exclude poisoned samples from the target model's learning process, with the adversarial samples generated under the guidance of the optimal attack policy to enhance the detector's robustness. Simulation results demonstrate that the DPAD-MUSC can find optimal attack policies that cause a greater accuracy drop in the target model while maintaining a higher evasion rate. The ADV-Generator can generate effective adversarial samples and the DPA-Defender outperforms five state-of-the-art methods on three widely used image datasets under additive white Gaussian noise (AWGN) channel in terms of Top-1 accuracy.

Time series data widely exist in public services, industrial environments, and military applications. Traditionally, the transmission of a huge volume of data for analytic tasks poses challenges, particularly in mobile environments with limited computing and communication resources. Semantic communication emerges as a solution for intelligently extracting various features from source data and efficiently transmitting task-related information to receivers, thereby reducing bandwidth consumption significantly. In this paper, we introduce a novel federated semantic communication system tailored for forecasting-oriented time series transmission tasks. The correlation of source data collected from terminal devices is mined and the corresponding semantic information is transmitted to an edge server for collaborative inference. To optimize the semantic analysis process, we devise a deep decomposition block at the transmitter side, decomposing time series into trend and multiple period components. This reduces noise interference from wireless channels, enhancing the overall transmission quality. For effective training and collaborative inference, we propose a Federated Mixture of period Routers (FedMoR) architecture. Within each channel encoder, period routers are divided into private and public ones. Private routers extract specialized features from individually collected data, mitigating accuracy degradation. Public routers share knowledge across all transmitters, enhancing temporal analysis robustness. Simulation results demonstrate that the proposed system outperforms two traditional technique-based and two semantic communication-based baselines under three common channels. The system achieves low mean square errors on five widely-used real-world time series forecasting datasets, particularly in the low signal-to-noise ratio regime.

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e.g., classification. Hence, a model’s representation learning ability is critical for enhancing its performance. This article proposes a densely knowledge-aware network (DKN) for MTSC. The DKN’s feature extractor consists of a residual multihead convolutional network (ResMulti) and a transformer-based network (Trans), called ResMulti-Trans. ResMulti has five residual multihead blocks for capturing the local patterns of data while Trans has three transformer blocks for extracting the global patterns of data. Besides, to enable dense mutual supervision between lower-and higher-level semantic information, this article adapts densely dual self-distillation (DDSD) for mining rich regularizations and relationships hidden in the data. Experimental results show that compared with 5 state-of-the-art self-distillation variants, the proposed DDSD obtains 13/4/13 in terms of “win”/“tie”/“lose” and gains the lowest-AVG_rank score. In particular, compared with pure ResMulti-Trans, DKN results in 20/1/9 regarding win/tie/lose. Last but not least, DKN overweighs 18 existing MTSC algorithms on 10 UEA2018 datasets and achieves the lowest-AVG_rank score.

This paper proposes a task-oriented semantic communication system for electrocardiogram (ECG) signal classification, called ECG-SC-DARTS. Based on deep learning, this system adopts the differentiable neural architecture search (DARTS) to automatically design the neural architecture of the semantic encoder under various channels. This paper improves the performance of the original DARTS by introducing a new recurrent neural network (RNN) cell with residual structure and a noise adding scheme for skip-connections. The RNN cell enhances the temporal semantic information extraction ability while the added noise reduces the risk of performance collapse caused by skip-connections. Experimental results demonstrate that ECGSC-DARTS generates appropriate neural architectures for the semantic encoder under AWGN, Rayleigh and Rician channels and these architectures outperform a number of baseline models, such as the original DARTS, fully convolutional network, multi-layer perception, and ResNet, regarding F1-score. Moreover, ECGSC-DARTS is more reliable than the traditional communication system in harsh channel environment.

We present a many-to-one distributed semantic communication system for multivariate time series classification. The system adopts a federated learning-based architecture to achieve low-redundancy collaborative inference, where an unsupervised auxiliary task is designed to coordinate the feature vectors at different dimensions between semantic encoders and the classifier. For each transmitter, we design a scale-adaptive semantic encoder by applying weighted sum to a number of predefined convolutional layers. The scale-adaptive semantic encoder can extract multi-scale features from time series following various distributions. A dynamic channel encoder is developed to adapt to the scale-adaptive semantic encoder, converting semantic features to complex symbols appropriate for wireless transmission. For the receiver, we apply the same scale-adaptive structure to the semantic decoder to extract multi-scale semantic features from all transmitters for accurate classification. Simulation results show that the proposed distributed semantic communication system outperforms two baseline systems under AWGN, Rician, and Rayleigh channels and achieves excellent Top-1 accuracy performance on three UEA2018 datasets, especially when the signal-to-noise ratio is low.

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.