Unsupervised anomaly detection in streaming time series is challenging due to evolving data distributions and the lack of labeled anomalies, where concept drift can significantly degrade detection accuracy during long-term operation. Most existing adaptive methods rely on uniform update strategies or continuous incremental learning. Such designs can be slow to react to abrupt distribution shifts and are prone to overwriting previously learned regimes when patterns recur. This paper proposes ADAPTS (Adaptive Drift-Aware Pool-based framework for Time Series), an unsupervised and drift-aware framework for adaptive anomaly detection in non-stationary data streams. ADAPTS integrates statistical drift detection, explicit drift-type classification, drift-specific model adaptation, and a bounded model pool for knowledge reuse within a unified system. The framework distinguishes sudden, incremental, and recurrent concept drift and aligns adaptation strategies accordingly through retraining, controlled fine-tuning, or selective model reuse. Experiments on benchmark datasets show that ADAPTS can keep stable and reliable detection results under different types of drift. Experimental results show that ADAPTS achieves competitive performance across multiple datasets, with performance improvements observed under several drift scenarios, including up to 0.11 absolute gain in AUC on challenging scenarios such as the NAB dataset, while maintaining improved long-term stability under diverse drift conditions.

There is an increasing interest in privacy preserving human activity recognition (HAR) and tracking technologies in smart environments. The millimetre wave (mmWave) radar has attracted a lot of attention due to its privacy preserving and non-intrusive sensing characteristics. However, mmWave radar can be costly and the collected data may not work well with existing HAR algorithms. In this study, a cost-effective and privacy preserving HAR system is proposed requiring only two mmWave radars. The system consists of a preprocessing module with background removal and data augmentation, a clustering based human detection module, and a human activity classification. The system has been implemented and evaluated in an emulated office environment. The system is capable of accommodating different types of classification algorithms and has been evaluated against several popular classification algorithms. The experimental results demonstrate the proposed system architecture can achieve an accuracy of 93.12 % relying on only two mmWave radars, outperforming other benchmark methods.

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users’ efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computational and communication efficiency, and model accuracy, compared with other four state-of-the-art methods for next-generation networking security in modern distributed learning systems.

Human Activity Recognition (HAR) is essential for intelligent applications requiring contextual awareness. However, HAR models often face challenges due to data distribution disparities between training and real-world scenarios, particularly across different users. To address this, we propose Diffusion-based Noise-centered Adversarial Learning Domain Adaptation (DNA-DA), a novel framework integrating generative diffusion modeling with adversarial learning for robust cross-user HAR. DNA-DA leverages a tailored network architecture with specialized constraints to align feature distributions across user domains, embedding activity and domain information into noise during diffusion to enhance adaptation. Through adversarial learning, it transforms forward diffusion and reverse denoising into domain alignment phases, enabling robust activity classification. Evaluated on the OPPT, PAMAP2, and DSADS datasets, DNA-DA achieves a 4.0% average accuracy improvement over state-of-the-art methods, with noise-based denoising enhancing data quality. By effectively mitigating distribution mismatches, DNA-DA offers a scalable solution for real-time, user-adaptive HAR systems.

Mānuka trees in riparian plantings along lake banks can enhance water quality and ecosystem resilience. This study utilizes multisource remote sensing data from an experimental Mānuka plot in the Lake Waikare catchment to assess their role in mitigating pollution and climate change effects. Soil moisture sensors were deployed in both Mānuka and non-Mānuka areas to compare soil moisture, water retention and soil loss. The proposed soil moisture prediction model achieved high accuracy R² value of 0.88. Results showed that Mānuka riparian areas had 53% lower soil moisture than non-riparian areas, and vegetation indices (VI) exhibited significant differences between plots. Furthermore, the riparian Mānuka plot reduced soil loss by 65% compared to non-riparian areas. These findings highlight the potential of Mānuka trees in riparian zones to enhance soil stability, reduce erosion, and support ecosystem resilience.

Recently, machine/deep learning techniques are achieving remarkable success in a variety of intelligent control and management systems, promising to change the future of artificial intelligence (AI) scenarios. However, they still suffer from some intractable difficulty or limitations for model training, such as the out-of-distribution (OOD) issue, in modern smart manufacturing or intelligent transportation systems (ITSs). In this study, we newly design and introduce a deep generative model framework, which seamlessly incorporates the information theoretic learning (ITL) and causal representation learning (CRL) in a dual-generative adversarial network (Dual-GAN) architecture, aiming to enhance the robust OOD generalization in modern machine learning (ML) paradigms. In particular, an ITL- and CRL-enhanced Dual-GAN (ITCRL-DGAN) model is presented, which includes an autoencoder with CRL (AE-CRL) structure to aid the dual-adversarial training with causality-inspired feature representations and a Dual-GAN structure to improve the data augmentation in both feature and data levels. Following a newly designed feature separation strategy, a causal graph is built and improved based on the information theory, which can enhance the causally related factors among the separated core features and further enrich the feature representation with the counterfactual features via interventions based on the refined causal relationships. The ITL is incorporated to improve the extraction of low-dimensional feature representations and learn the optimized causal representations based on the idea of "information flow." A dual-adversarial training mechanism is then developed, which not only enables the generator to expand the boundary of feature distribution in accordance with the optimized feature representation from AE-CRL, but also allows the discriminator to further verify and improve the quality of the augmented data for OOD generalization. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning efficiency and classification performance of our proposed model for robust OOD generalization in modern smart applications compared with three baseline methods.

Due to the high dependence of social development on electricity, the failure of energy system equipment often leads to inestimable losses. The use of Internet of Things (IoT) technology to collect real-time data from energy devices and artificial intelligence (AI) technology to prognostic faults in the devices becomes essential to achieve power system security. Although real-time data contains rich descriptions of relevant equipment faults and solutions, the complex data structure and severe imbalance of condition monitoring (CM) data may lead to poor performance of AI models. In this study, we propose a multitask correlation constrained topology learning model for smart prognostic and health management in IoT. In particular, the proposed model mainly includes a feature extraction module, a multitask topology network (MTTN) module, and a class balance loss (CBL) algorithm module. First, the Bi-LSTM is employed to extract word collocation features and numerical features of the data, focusing on the important semantic features in complex structured data. Second, the MTTN is constructed to efficiently utilize the topological dependency between multiple tasks. Finally, a CBL loss function is applied to enhance the focus on the minority classes. Experiment and evaluation results demonstrate that our model has superior learning efficiency and prediction performance, especially on extremely imbalanced data, which can correctly predict the faulty equipment, the fault cause and the fault severity level, providing a rapid and precise reference for advance repair or replacement of energy system equipment in IoT environments.

With the flourish of digital technologies and rapid development of 5G and beyond networks, Metaverse has become an increasingly hotly discussed topic, which offers users with multiple roles for diversified experience interacting with virtual services. How to capture and model users’ multi-platform or cross-space data/behaviors become essential to enrich people with more realistic and immersed experience in Metaverse-enabled smart applications over 5G and beyond networks. In this study, we propose a Personalized Federated Learning with Model-Contrastive Learning (PFL-MCL) framework, which may efficiently enhance the communication and interaction in human-centric Metaverse environments by making use of the large-scale, heterogeneous, and multi-modal Metaverse data. Differing from the conventional Federated Learning (FL) architecture, a multi-center aggregation structure to learn multiple global models based on the changes of dynamically updated local model weights, is developed in global, while a hierarchical neural network structure which includes a personalized module and a federated module to tackle both issues on data heterogeneity and model heterogeneity, is designed in local, so as to enhance the performance of PFL with unique characteristics of Metaverse data. In particular, a two-stage iterative clustering algorithm with a more precise initialization is developed to facilitate the personalized global aggregation with dynamically updated multiple aggregation centers. A personalized multi-modal fusion network is constructed to greatly reduce the computational cost and feature dimensions from the high-dimensional heterogeneous inputs for more efficient cross-modal fusion, based on a hierarchical shift-window attention mechanism and a newly designed bridge attention mechanism. A MCL scheme is then incorporated to speed up the model convergence with less communication overload between the local federated module and global model, while an embedding layer which effectively enables the delivered global model to better adapt to the local personality in each client is further integrated. Compared with five baseline methods, experiment and evaluation results based on two different real-world datasets demonstrate the excellent performance of our proposed PFL-MCL model in a fine-grain personalized training strategy, toward more efficient communication and networking among human-centric Metaverse enabled smart applications.

In sensor-based Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed (i.i.d.). Contrarily, practical implementations often challenge this notion, manifesting data distribution discrepancies, especially in scenarios such as cross-user HAR. Domain adaptation is the promising approach to address these challenges inherent in cross-user HAR tasks. However, a clear gap in domain adaptation techniques is the neglect of the temporal dependency relation embedded within time series data during the phase of aligning data distributions. Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method. This novel method uniquely recognizes and integrates temporal dependency relations during the domain adaptation process. By synergizing the capabilities of generative models with the Temporal Relation Attention mechanism, our method improves the classification performance in cross-user HAR. The evaluation has been conducted on three public sensor-based HAR datasets targeting daily living activity and sports fitness activity scenarios to demonstrate the efficacy of the proposed DGDATA method.

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics—including affective learning, reinforcement learning, and transfer learning—are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand.

The article proposes an extended Morlet wavelet-based FIR (et-MW-FIR) which can be piled on top of enhanced MW-FIR (e-MW-FIR) to further improve the overall estimation accuracy. Moreover, by integrating the extended algorithm block, the wavelet parameter selection range is enlarged. In this case, as one application of the proposed method, the article also introduces a fully compliant P-class estimator by varying the parameters rather than using a threshold parameter which may lead to nonconvergence in practice. The new estimators with two sets of parameters are compared with the conventional and enhanced estimators under IEC/IEEE 60255-118-1. Additionally, some extensive simulations, field data, and hardware implementation are also conducted to evaluate the performance of the proposed block and estimator.

The proliferation of advanced embedded and communication technologies has facilitated the possibility of modern Intelligent Transportation System (ITS). The hierarchical nature of such large-scale and distributed systems brings obvious challenges in creating a scalable and sustainable computing environment, and hence the development and application of edge intelligence become critical. Federated learning (FL), as an emerging distributed machine learning paradigm, aims to offer secure knowledge sharing and effective learning across multiple devices. However, conventional FL may fall into trouble when facing large-scale and network-agnostic systems with fast moving devices and changing network attributes. In this study, we propose an Adaptive Segmentation enhanced Asynchronous Federated Learning (AS-AFL) model, aiming to improve the learning efficiency and reliability in sustainable ITS via a decentralized fashion. Specifically, a meta-learning based adaptive segmentation scheme is designed to automatically separate the client nodes (e.g., vehicles) into multiple edge groups according to their homogeneous attributes. An integrated aggregation mechanism is then developed to realize the horizontal FL among a group of similar client nodes via the so-called intra-group synchronous aggregation, while allowing the vertical FL across different groups via the so-called inter-group asynchronous aggregation. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning and communication performance of our proposed model, compared with several conventional FL schemes in a distributed ITS application scenario.

The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services. It becomes essential to ensure secure communications while exchanging highly sensitive IoT data efficiently, which leads to high demands for lightweight models or algorithms with limited computation capability provided by individual IoT devices. In this study, a graph representation learning model, which seamlessly incorporates graph neural network (GNN) and knowledge distillation (KD) techniques, named reconstructed graph with global-local distillation (RG-GLD), is designed to realize the lightweight anomaly detection across IoT communication networks. In particular, a new graph network reconstruction strategy, which treats data communications as nodes in a directed graph while edges are then connected according to two specifically defined rules, is devised and applied to facilitate the graph representation learning in secure and efficient IoT communications. Both the structural and traffic features are then extracted from the graph data and flow data respectively, based on the graph attention network (GAT) and multilayer perceptron (MLP) techniques. These can benefit the GNN-based KD process in accordance with the more effective feature fusion and representation, considering both structural and data levels across the dynamic IoT networks. Furthermore, a lightweight local subgraph preservation mechanism improved by the graph attention mechanism and downsampling scheme to better utilize the topological information, and a so-called global information alignment defined based on the self-attention mechanism to effectively preserve the global information, are developed and incorporated in a refined graph attention based KD scheme. Compared with four different baseline methods, experiments and evaluations conducted based on two public datasets demonstrate the usefulness and effectiveness of our proposed model in improving the efficiency of knowledge transfer with higher classification accuracy but lower computational load, which can be deployed for lightweight anomaly detection in sustainable IoT computing environments.

Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic, which is playing an increasingly indispensable role in modern intelligent applications. These applications are known for their real-time requirements under limited network and computing resources, thus it becomes a highly demanding task to transform and compute tremendous amount of raw data in a cloud center. The edge–cloud computing infrastructure allows a large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an end–edge–cloud-based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying a data security constraint. In this study, a deep-reinforcement-learning-enhanced two-stage scheduling (DRL-TSS) model is proposed to address the NP-hard problem in terms of operation complexity in end–edge–cloud Internet of Things systems, which is able to allocate computing resources within an edge-enabled infrastructure to ensure computing task to be completed with minimum cost. A presorting scheme based on Johnson’s rule is developed and applied to preprocess the two-stage tasks on multiple executors, and a DRL mechanism is developed to minimize the overall makespan based on a newly designed instant reward that takes into account the maximal utilization of each executor in edge-enabled two-stage scheduling. The performance of our method is evaluated and compared with three existing scheduling techniques, and experimental results demonstrate the ability of our proposed algorithm in achieving better learning efficiency and scheduling performance with a 1.1-approximation to the targeted optimal IoE applications.

The high-speed mobile networks offer great potentials to many future intelligent applications, such as autonomous vehicles in smart transportation systems. Such networks provide the possibility to interconnect mobile devices to achieve fast knowledge sharing for efficient collaborative learning and operations, especially with the help of distributed machine learning, e.g., Federated Learning (FL), and modern digital technologies, e.g., Digital Twin (DT) systems. Typically, FL requires a fixed group of participants that have Independent and Identically Distributed (IID) data for accurate and stable model training, which is highly unlikely in real-world mobile network scenarios. In this paper, in order to facilitate the lightweight model training and real-time processing in high-speed mobile networks, we design and introduce an end-edge-cloud structured three-layer Federated Reinforcement Learning (FRL) framework, incorporated with an edge-cloud structured DT system. A dual-Reinforcement Learning (dual-RL) scheme is devised to support optimizations of client node selection and global aggregation frequency during FL via a cooperative decision-making strategy, which is assisted by a two-layer DT system deployed in the edge-cloud for real-time monitoring of mobile devices and environment changes. A model pruning and federated bidirectional distillation (Bi-distillation) mechanism is then developed locally for the lightweight model training, while a model splitting scheme with a lightweight data augmentation mechanism is developed globally to separately optimize the aggregation weights based on a splitted neural network structure (i.e., the encoder and classifier) in a more targeted manner, which can work together to effectively reduce the overall communication cost and improve the non-IID problem. Experiment and evaluation results compared with three baseline methods using two different real-world datasets demonstrate the usefulness and outstanding performance of our proposed FRL model in communication-efficient model training and non-IID issue alleviation for high-speed mobile network scenarios.

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications.

Safety production surveillance is of great significance to industrial operation management. While augmented intelligence of things is demonstrating tremendous potential in industrial applications, the analyzed information offers lots of benefits to the higher level planning in the enterprise management systems, to further improve the operational efficiency. In this article, a video surveillance system with augmented intelligence of things is considered as a promising solution to enhance the operational efficiency of enterprises. However, the challenge is to process the surveillance video streams as soon as possible without ignoring any emergencies. This issue can be formulated as a two-stage scheduling problem, which is an NP-hard problem that can be integrated with higher level enterprise systems for operational efficiency improvement. An improved Deep Q-Network (DQN) model with a newly designed prioritized replay scheme, named Bi-Dueling DQN with Prioritized Replay, is proposed to solve this two-stage scheduling problem in a smart enterprise management system. A dense reward function based on a concrete state representation is designed to tackle the sparse reward challenge and to speed up the convergence in actual large-scale task scheduling process. A prioritized replay scheme is then developed to improve the sampling efficiency, so as to effectively reduce the training time in deep reinforcement learning for the optimal two-stage scheduling. The experiment results demonstrated that the proposed approach is able to provide an efficient scheduling policy to resolve the two-stage scheduling problem, while at the same time offering insight information to improve the performance of higher level smart enterprise management systems.

Modern manufacturing process is commonly composed of multiple automated devices working together efficiently. Cloud-based manufacturing aims to achieve better efficiency by allowing the collaborative manufacturing across a group of automated robots. Cooperations between multiple robots can accomplish more complicated tasks that is beyond the capability of any individual one. However, it is of critical importance to control robots with different capabilities to work in harmony while ensuring safety and reliability during this process. In this paper, a double constrained containment mechanism is proposed to dispatch heterogeneous robots in a distributed containment control framework for smart manufacturing. Following a three-layer control framework, a cloud decision-making center is designed to realize the cloud-based collaborative manufacturing, which is more cost-effective than other commonly used containment mechanisms that only rely on information exchange among leader-robots. A projection-based containment control scheme is developed, which not only consider nonlinearities induced by position constraints and velocity constraints, but also can tackle dynamically changing communication topologies with uncertain communication delay, to efficiently navigate all follower-robots into a safe working zone formed by leaders. A theoretical stability analysis is conducted to prove the proposed mechanism can ensure all followers enter the target area while their positions and velocities remaining in the corresponding constraint sets. Experiment evaluation results under three application scenarios demonstrate the advantage of our method that can offer a more practical solution to other existing multi-robot containment control for cloud-based smart manufacturing, in considering both position and velocity constraints combined with switching topologies and communication delays.

With the development of the Internet of Things (IoT) and 5G technologies, IoT devices deployed on roads are able to collect a large amount of traffic data at any time. Road networks can be easily constructed into a graph structure with spatial-temporal features, and how to use these spatial-temporal features for dynamic traffic flow forecasting has become a heated issue. Although existing studies bring in the consideration of periodicity to deal with spatial-temporal sequence dependence, the similarity of time-varying relationships among cross-spatial nodes has not been well discussed. In this paper, we propose a Graph Attention Network with Spatial-Temporal Clustering (GAT-STC), which considers the so-called recent-aware features and periodic-aware features, to improve the Graph Neural Network (GNN)-based traffic flow forecasting in Intelligent Transportation System (ITS). Specifically, for the recent-aware feature extraction, a distance-based Graph Attention Network (GAT) is improved and constructed to better utilize the hidden features of neighbor nodes within a reliable distance during the recent time interval, thus can effectively capture the dynamic changes in spatial feature representation. For the periodic-aware feature extraction, a spatial-temporal clustering algorithm, in which both features in terms of nodes’ current traffic states and similar trends in terms of their dynamic changes are taken into account, is developed and applied to achieve better learning efficiency. Experiments using three public traffic datasets demonstrate the higher accuracy and better efficiency of our proposed model for traffic flow forecasting, compared with five baseline methods in ITS.

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance.

This paper presents DSF-IoT, a novel distributed approach enabling a future decentralised Internet of Things (IoT). DSF-IoT is designed to address key challenges in the IoT, providing a novel secure and efficient specification for the description of IoT services and their data, as well as novel mechanisms for consistent dynamic and contextually relevant service discovery and interaction. Distributed technologies provide an alternative to existing centralised and firmly capitalised models for service provision, removing the requirements for trusted vendor infrastructure and simplifying the deployment of IoT devices, while end-to-end trust, authenticity, and confidentiality ensure user interactions and data are secure-by-default. DSF-IoT is demonstrated through the development of a network of demonstration devices, and evaluated against existing frameworks for IoT service specification and discovery.

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

Modern Internet-of-Things (IoT) applications are heavily data driven and often require reliable data streams to achieve high-quality data mining. The concept of edge computing is introduced to reduce data latency and communication bandwidth between the cloud server and IoT edge devices. However, inefficient routing that may cause transmission failure or unnecessary data (re)transmission is still a key obstacle to obtain good and reliable data mining results. In this article, network coding combined with opportunistic routing is used to improve energy efficiency in wireless IoT infrastructure, considering the existence of link correlation. Studies have shown that packet receptions on wireless links are correlated, which is completely contrary to the assumption of link independence used in existing routing mechanisms. This assumption causes estimation errors in the calculation of expected number of transmissions for forwarders, which further affects the selection of forwarder set, and ultimately affects the performance of the protocol. We propose an intrasession network coding mechanism based on the mining of link correlation. A novel smart routing method is proposed to accurately estimate the number of transmissions required by forwarders, together with an algorithm for selecting a forwarder set with more optimal number of transmissions. Simulation results demonstrate that the proposed mechanism can achieve fewer transmissions and offer more energy-efficient communications for wireless edge IoT applications.

Along with the popularity of the Internet of Things (IoT) techniques with several computational paradigms, such as cloud and edge computing, microservice has been viewed as a promising architecture in large-scale application design and deployment. Due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training. In addition, most of the machine-learning-based intrusion detection methods are insufficient when dealing with imbalanced dataset under limited computing resources. In this article, we propose an optimized intra/inter-class-structure-based variational few-shot learning (OICS-VFSL) model to overcome a specific out-of-distribution problem in imbalanced learning, and to improve the microservice-oriented intrusion detection in distributed IoT systems. Following a newly designed VFSL framework, an intra/inter-class optimization scheme is developed using reconstructed feature embeddings, in which the intra-class distance is optimized based on the approximation during a variation Bayesian process, while the inter-class distance is optimized based on the maximization of similarities during a feature concatenation process. An intelligent intrusion detection algorithm is, then, introduced to improve the multiclass classification via a nonlinear neural network. Evaluation experiments are conducted using two public datasets to demonstrate the effectiveness of our proposed model, especially in detecting novel attacks with extremely imbalanced data, compared with four baseline methods.

Smart manufacturing aims to support highly customizable production processes. Therefore, the associated machine intelligence needs to be quickly adaptable to new products, processes, and applications with limited training data while preserving data privacy. In this article, a new federated transfer learning framework, federated transfer learning for cross-domain prediction, is proposed to address the challenges of data scarcity and data privacy faced by most machine learning approaches in modern smart manufacturing with cross-domain applications. The framework architecture consists of a central server and several groups of smart devices, where each group handles a different application. The existing applications can share their knowledge through the central server as base models, while new applications can convert a base model to their target-domain models with limited application-specific data using a transfer learning technique. Meanwhile, the federated learning scheme is deployed within a group to further enhance the accuracy of the application-specific model. The integrated framework allows model sharing across the central server and different smart devices without exposing any raw data and, hence, protects the data privacy. Two public datasets, COCO and PETS2009, which represent the source and target applications, are employed for evaluations. The simulation results show that the proposed method outperforms two state-of-the-art machine learning approaches by achieving better learning efficiency and accuracy.

Efficient architectures and implementations of median filters have been well investigated in the past. In this article, we focus on median filters for very big scientific applications with very large windows and an infinite stream of data, inspired by big data needs in the Square Kilometre Array (SKA) pulsar search engine, but transferable to other big data domains. We propose a novel approach for very large rectangular windows on an FPGA accelerator device able to support the processing of infinite streams of data. OpenCL is used for rapid parameter sweeping and design space exploration based on a pipelined model of the system. Evaluation on a host/accelerator system with an Arria 10 device surpassed 64 million values processed per second considered for the SKA real time requirement, achieving 83.4M value/s while reading from/writing to disk. These results are compared with a state-of-the-art software implementation only achieving 41M value/s for over twice the total system energy cost.

The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterogeneous big data environments. A hierarchical hybrid network (HHN) model is constructed to describe multitype relationships among different entities, and a series of measures are defined to quantify the internal correlations within one specific layer or external correlations between different layers. An intelligent router based on deep reinforcement learning framework is designed to generate optimal actions to route across the HHN. An improved random walk with the restart-based algorithm is then developed with the intelligent router, based on the hierarchical influence across network associated with multiple correlations. An intelligent recommendation mechanism is finally designed and applied to support users' collaboration works in scholarly big data environments. Experiments based on DBLP and ResearchGate data show the practicability and usefulness of our model and method.

Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep Q-network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.

Dynamic reconfigurability and adaptability are crucial features of the future manufacturing systems that must be supported by adequate software technologies. Currently, they are typically achieved as add-ons to existing software tools and run-time systems, which are not based on any formal foundation such as formal model of computation (MoC). This paper presents the new programming paradigm of service oriented SystemJ (SOSJ), which targets dynamic distributed software systems suited for future manufacturing applications. SOSJ is built on a merger and the synergies of two programming concepts of service oriented architecture, to support dynamic software system composition, and SystemJ programming language based on a formal MoC, which targets correct by construction design of static distributed software systems. The resulting programming paradigm allows the design and implementation of dynamic distributed software systems.

This article introduces distributed service framework (DSF), a distributed service framework supporting development, and deployment of trustworthy and privacy-preserving distributed Internet of Things (IoT) and Industrial IoT (IIoT) applications. DSF provides a common protocol and infrastructure for secure service specification, registration, discovery, publishing, and subscription, over insecure public networks, and introduces mechanisms for service replication, supporting dynamic scalability of services, and delegation, allowing use on embedded and resource-constrained devices. DSF is an open protocol, with an open-source implementation including supporting library for application development, a command-line client for user interaction, and a daemon that manages services and data. DSF is qualitatively evaluated against existing approaches to developing distributed applications and common technologies used in the IIoT context and demonstrated through the development of an IIoT scenario.

In this paper, a new solution based on wireless sensor network is introduced to provide low cost and low energy monitoring for underground pipelines. We propose an adaptive duty-cycle MAC protocol based on Wake-on-Radio called WoR-MAC. Simulation experiments have been conducted to validate the outstanding performance compared with other duty-cycle based energy-efficient MAC protocols in terms of energy consumption and average end-to-end latency.

Recently, health social media have engaged more and more people to share their personal feelings, opinions, and experience in the context of health informatics, which has drawn increasing attention from both academia and industry. In this paper, we focus on the behavioral influence analysis based on heterogeneous health data generated in social media environments. An integrated deep neural network (DNN)-based learning model is designed to analyze and describe the latent behavioral influence hidden across multiple modalities, in which a convolutional neural network (CNN)-based framework is used to extract the time-series features within a certain social context. The learned features based on cross-modality influence analysis are then trained in a SoftMax classifier, which can result in a restructured representation of high-level features for online physician rating and classification in a data-driven way. Finally, two algorithms within two representative application scenarios are developed to provide patients with personalized recommendations in health social media environments. Experiments using the real world data demonstrate the effectiveness of our proposed model and method.

The heterogeneity of execution platforms and operating software in manufacturing machines and robots, as well as various sensors and actuators, creates challenges for integration into larger systems. Existing approaches make use of different types of middleware to mitigate the challenges of designing interoperable systems. However, middleware can significantly impede modular design and composition of software systems that are dynamic in nature. This paper elaborates upon those challenges and proposes using an approach called service-oriented SystemJ (SOSJ), based on the system-level programming language SystemJ enhanced with service oriented features. This approach allows developers to design dynamic software systems while adopting and incorporating legacy solutions. The approach is demonstrated on the integration of an industrial automation system, incorporating the use of multiple modular mechatronics stations and service robotics systems, represented by robot operating system-enabled Baxter robots. The proposed approach offers a simple service interface based on abstract objects for integrating robots and automation machines in the SOSJ world, without the need to modify the underlying mechatronics or robotics systems.

In an age of increasing camera-based surveillance, there is also an increase in privacy concerns being voiced by the surveilled. Camera surveillance systems are expanding beyond law enforcement and public safety applications towards "video analytics" scenarios that act as a source of data for business and planning decisions. It is necessary for us to consider how perceptions of privacy may change under different conditions, so that appropriate safeguards can be designed for the relevant systems. Through an on-line survey, we determine that privacy concerns are not universal, and that they are heavily dependent on the specific context being considered. We find a wide distribution of perception between the respondents, and identify some of the underlying factors that lead to differences in opinion between different contexts.

Ahstract- This paper introduces Yet Another Wireless Network Simulator (YAWNS), a simulator supporting the development and automated validation of complex heterogeneous systems based on wireless sensor and actuator networks (WSANs). YAWNS allows simulation of heterogeneous systems through the use of a novel Virtual Radio Interface (VRI) abstraction that mimics the operation of physical radio hardware over a language independent simulation protocol. This simplifies system integration and decouples application languages and hardware from the simulation process, allowing evaluation and interoperability testing when using different languages, operating systems, applications and protocol implementations. The simulator is based on an extensible discrete-time engine coupled with a low-fidelity emulated medium allowing intuitive description of scenarios as well as simulation of complex dynamic and multiband environments with approximate real-world parity. A simple configuration language allows the definition of scenarios as well as events to update or validate the state of the simulation, allowing automated alteration of scenarios and evaluation of applications.

Large scale median filtering algorithms are investigated in the context of the Square Kilometre Array (SKA) pulsar search; a signal processing pipeline estimated to require more than 10POps on 60PB of search data collected per day. Real time performance is needed for rectangular median windows of 63 frequency channels across 1023 time steps, requiring at least 64 million values to be calculated and output per second. This paper proposes an algorithmic approach for large scale median filtering based on existing techniques, providing improvements for the heterogeneous system used and utilising a high-end FPGA accelerator. Taking advantage of OpenCL for rapid parameter sweeping, the design space was explored to find the best algorithmic approach. The evaluation results are promising and show output of up to 99.3 million values per second on an Arria-10 FPGA, coming close to the limits set by resources and bandwidth. These results are set into relation with GPU and CPU implementations for the same algorithm, taking advantage of the OpenCL portability, achieving up to 16.8 and 9.1Mvalue/s respectively.

High-level approaches to hardware development can expedite the design process, allowing for rapid design space exploration. However, in order to generate optimised solutions expert intervention is often still required. This work seeks to explore the relationship between high-level descriptions and the resulting hardware architecture. This aims to reduce the barrier to entry for software developers (without hardware expertise) to produce optimised hardware designs through application of classical loop optimisation techniques. An algorithm from the Square Kilometre Array (SKA) is chosen to demonstrate the effects of such changes in a real world, real-time application requiring high throughput and low power consumption, taking a systematic approach in order to achieve an optimised result. A systolic array design is also discussed and compared with the software style changes. The Intel FPGA SDK for OpenCL (AOCL) Offline Compiler (AOC) is used here for verification and synthesis of the designs being examined, targeting an Arria-10 FPGA accelerator.

For video analytics and surveillance applications, person re-identification across multiple camera views remains an open problem. The challenge of being able to determine that two images of people are the same person based solely on their appearance can be difficult, even for human observers. Many recent re-identification methods use deep learning with supervised learning to discriminate between the identity classes. However, the requisite training data is generally not available in real-world scenarios. In this paper, we compare a number of fast classification methods for the purposes of re-identification, taking extracted and pre-processed feature vectors and classifying them into identity classes, focusing on one-shot and unsupervised learning algorithms. We present two novel one-shot learning methods, including Sequential K-means, a computationally efficient algorithm with competitive accuracy. We demonstrate this on an indoor person tracking dataset, and discuss parameter tuning in order to further improve the accuracy of the algorithm.

One of the key challenges in modern manufacturing systems is how to dynamically reconfigure software behaviors that govern machines to reflect changes in physical manufacturing process without completely resetting the entire manufacturing operation. The existing software solutions used to describe software behaviors in manufacturing systems are typically not based on formal semantics and model of computation and have limited capabilities in handling dynamic adaptation/reconfiguration. This paper presents the Service-Oriented SystemJ (SOSJ) framework that supports a new programming paradigm for designing dynamic distributed manufacturing systems. SOSJ combines the system-level language SystemJ and service-oriented architecture (SOA) paradigm to take advantages of both SystemJ's correct-by-construction formal semantics and SOA's dynamic features, respectively. The paper describes the concepts and functionalities of SOSJ, which enable dynamic reconfiguration of a typical manufacturing system. Performance benchmarks are run to compare the capabilities of SOSJ to a multiagent system framework JADE.

In this paper, we propose using an approach based on the system-level programming language SystemJ extended with service oriented features, called SOSJ, to design dynamic interoperable software systems. The approach abstracts and integrates the worlds of automation and robotics systems by using a simple service interface based on abstract objects within SOSJ. We demonstrate our approach in a real-life automated bottling system scenario that uses multiple FESTO modular stations operating in SOSJ and integrating them with two Baxter robots operating in ROS without the need for any modification of the underlying mechatronics or robotics systems.

This paper presents new changes and improvements in the architecture of a programming paradigm Service Oriented SystemJ (SOSJ) to tackle large distributed systems. The programming paradigm makes use of the synergy of two programming concepts of Service Oriented Architecture (SOA) suitable for dynamic system composition and formal GALS (Globally Asynchronous Locally Synchronous) language SystemJ amenable for designing safe static concurrent distributed systems. The use of SOSJ framework based on the new architecture is demonstrated in an industrial manufacturing example and initial benchmarks that evaluate the framework's performance in large distributed systems are shown.

This paper presents the Service Oriented SystemJ (SOSJ) framework, which combines correct-by-construction language features of GALS (Globally Asynchronous Locally Synchronous) system-level language SystemJ with dynamic reconfiguration features of Service Oriented Architecture (SOA), creating a new programming paradigm suitable for designing dynamic distributed manufacturing systems. The paper demonstrates new concepts introduced by SOSJ which enable dynamic online reconfiguration of typical distributed manufacturing systems. Some performance benchmarks are used to showcase the capability of the SOSJ framework.

Embedded sensors are increasingly seen in modern mobile and wearable devices to support better user activities and vital signs monitoring. While more wireless sensing devices are becoming available, network connectivity and scalability remain the key obstacles in achieving seamless information flow and service provision as envisaged by the vision of the Internet of Things. In this paper, a mobile 6LoWPAN network architecture is proposed by engaging Android-based mobile devices as edge routers to address the connectivity and scalability issues with the wider IP network for pervasive healthcare applications. The 6LoWPAN-based wireless sensor network, together with the Android edge router, form a mobile sub-network that allows seamless information exchange with the Internet and improves service availability for each end user. The proposed architecture includes a customised bare-metal 6LoWPAN protocol designed for ultra low power wireless sensor nodes for long term vital signs monitoring and integrated with Android devices for mobile internet access. The proposed network architecture is demonstrated and validated on a pervasive healthcare monitoring application.

This paper presents an extensible and adaptable platform for distributed applications with mixed criticality based on using state of the art FPGA technology. Although capable of executing programs written in different languages, the platform specifically targets the execution of programs written in Globally Asynchronous Locally Synchronous language SystemJ used in the context of Internet of Industrial Things. The key properties of the prototype platform are accommodation of mixed-criticality processing as well as provision of Internet addressable services. Mixed-criticality execution platform (MCEP) uses multiple processor cores and network interfaces: (1) a dual-core ARM processor with Ethernet for Internet access and processing of non-real -- time application parts and (2) TP-JOP reactive hard real-time processor with customized Controller Area Network (CAN) for real-time and time-critical response processing. This platform has been successfully developed and used in an industrial automation system within the Internet of Industrial Things context.

Connection of wireless sensor and actuator networks (WSANs) to the Internet will enable information flow to these once isolated networks. Distributed Ambient Intelligence (AmI) applications, such as remote monitoring and control services, would become possible if Internet connectivity of WSANs is achieved. However, realization of such applications remains challenging with three major obstacles. First, the homogeneity of embedded sensor and actuator nodes and hence homogeneous networks prevents seamless information flow and service provision. Second, current WSAN technology has limited support for Internet connectivity. Protocol conversion with heavy overhead is required on a more powerful gateway device. Third, there are no common interoperable interfaces between different WSAN standards which has become a barrier to heterogeneous WSAN applications. This paper introduces a design for an integrated IP compatible, heterogeneous WSAN composed of the University of Auckland's AWSAM and Oracle's Sun SPOT nodes. This was achieved through the creation of a Hybrid Router, which physically bridges AWSAM and Sun SPOT communication with customized network abstraction, and an Edge Router, which provides Internet connectivity.

The ever increasing demand for energy has led towards distributed renewable energy generation systems. Such systems depend on a well integrated information and communication infrastructure for interconnection and integration of various energy sources, loads, and environmental sensors to achieve intelligent distributed control and management. This paper describes the design and implementation of an intelligent hybrid communication system for a micro distributed energy generation application. The industrial standard controller area network (CAN) bus was selected to connect various energy sources and loads, due to its resistance to noisy conditions. An application specific communication protocol was designed, based on the CAN protocol, to allow data exchange and control. The proposed communication system was verified using a prototype micro wind generation system with multiple power converters and a central energy management unit. Combined with intelligent control algorithms, which could in future be incorporated in the energy management unit, the system has the ability to perform optimised load balancing and energy usage planning.

The availability of low-power wireless sensor nodes has brought forward the scenarios of the Internet of Things, in which ubiquitous things need to be aware of their context, and at the same time be power efficient and IP-addressable. 6LoWPAN is one of the first protocols to standardise Internet connectivity for wireless sensor networks. The various 6LoWPAN implementations that are currently available are either dependent on the operating systems used on the sensor nodes or only commercially available. The dependency on using operating systems is suitable for larger nodes with more processing power and memory capacity, but is not practical, if not impossible, for power efficient nodes with systems on chip (SoC) solutions such as TI CC430. In this work, a successful implementation of 6LoWPAN/CD (6LoWPAN protocol for constrained devices) on bare metal CC430-based sensor nodes has been designed and implemented. It is based on open source software including the Contiki operating system and TI SimpliciTI protocol stack. The IP connectivity is demonstrated on a CC430-based power efficient wireless sensor node, AWSAM.