Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions

Self-balancing Incremental Broad Learning System with privacy protection

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Volatile tin oxide memristor for neuromorphic computing

The rise of neuromorphic systems has addressed the shortcomings of current computing architectures, especially regarding energy efficiency and scalability. These systems use cutting-edge technologies such as Pt/SnOx/TiN memristors, which efficiently mimic synaptic behavior and provide potential solutions to modern computing challenges. Moreover, their unipolar resistive switching ability enables precise modulation of the synaptic weights, facilitating energy-efficient parallel processing that is similar to biological synapses. Additionally, memristors' spike-rate-dependent plasticity enhances the adaptability of neural circuits, offering promising applications in intelligent computing. Integrating memristors into edge computing architectures further highlights their importance in tackling the security and efficiency issues associated with conventional cloud computing models.

Metadata-Private Resource Allocation in Edge Computing Withstands Semi-Malicious Edge Nodes

Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme.

Baqasah A, Alroobaea R, Almadhor A, Avelino Sampedro G, Kryvinska N. Blockchain-enabled infrastructural security solution for serverless consortium fog and edge computing

The robust development of the blockchain distributed ledger, the Internet of Things (IoT), and fog computing-enabled connected devices and nodes has changed our lifestyle nowadays. Due to this, the increased rate of device sales and utilization increases the demand for edge computing technology with collaborative procedures. However, there is a well-established paradigm designed to optimize various distinct quality-of-service requirements, including bandwidth, latency, transmission power, delay, duty cycle, throughput, response, and edge sense, and bring computation and data storage closer to the devices and edges, along with ledger security and privacy during transmission. In this article, we present a systematic review of blockchain Hyperledger enabling fog and edge computing, which integrates as an outsourcing computation over the serverless consortium network environment. The main objective of this article is to classify recently published articles and survey reports on the current status in the domain of edge distributed computing and outsourcing computation, such as fog and edge. In addition, we proposed a blockchain-Hyperledger Sawtooth-enabled serverless edge-based distributed outsourcing computation architecture. This theoretical architecture-based solution delivers robust data security in terms of integrity, transparency, provenance, and privacy-protected preservation in the immutable storage to store the outsourcing computational ledgers. This article also highlights the changes between the proposed taxonomy and the current system based on distinct parameters, such as system security and privacy. Finally, a few open research issues and limitations with promising future directions are listed for future research work.

Prescribed Performance Bipartite Consensus Control for Stochastic Nonlinear Multiagent Systems Under Event-Triggered Strategy

In this article, the event-triggered bipartite consensus problem for stochastic nonlinear multiagent systems (MASs) with unknown dead-zone input under the prescribed performance is studied. To surmount the influence of the dead-zone input, the dead-zone model is transformed into a linear term and a disturbance term. Meanwhile, the prescribed tracking performance is realized by developing a speed function, which means that all tracking errors of MASs can converge to a predefined set in a given finite time. Moreover, the unknown nonlinear dynamics are approximated by fuzzy-logic systems. By combining the dynamic surface approach and the Lyapunov stability theory, we design an adaptive event-triggered control algorithm, such that the bipartite consensus problem of stochastic nonlinear MASs can be achieved, and all signals are semiglobally uniformly ultimately bounded in probability of the closed-loop systems. Finally, simulation examples are proposed to verify the feasibility of the algorithm.

Adaptive Neural-Network-Based Fault-Tolerant Control for a Flexible String With Composite Disturbance Observer and Input Constraints

We propose an adaptive neural-network-based fault-tolerant control scheme for a flexible string considering the input constraint, actuator gain fault, and external disturbances. First, we utilize a radial basis function neural network to compensate for the actuator gain fault. In addition, an observer is used to handle composite disturbances, including unknown approximation errors and boundary disturbances. Then, an auxiliary system eliminates the effect of the input constraint. By integrating the composite disturbance observer and auxiliary system, adaptive fault-tolerant boundary control is achieved for an uncertain flexible string. Under rigorous Lyapunov stability analysis, the vibration scope of the flexible string is guaranteed to remain within a small compact set. Numerical simulations verify the high control performance of the proposed control scheme.

ALIS: Learning Affective Causality Behind Daily Activities From a Wearable Life-Log System

Human emotions and behaviors are reciprocal components that shape each other in everyday life. While the past research on each element has made use of various physiological sensors in many ways, their interactive relationship in the context of daily life has not yet been explored. In this work, we present a wearable affective life-log system (ALIS) that is robust as well as easy to use in daily life to accurately detect emotional changes and determine the cause-and-effect relationship between emotions and emotional situations in users' lives. The proposed system records how a user feels in certain situations during long-term activities using physiological sensors. Based on the long-term monitoring, the system analyzes how the contexts of the user's life affect his/her emotional changes and builds causal structures between emotions and observable behaviors in daily situations. Furthermore, we demonstrate that the proposed system enables us to build causal structures to find individual sources of mental relief suited to negative situations in school life.

Asymmetric Cooperation Control of Dual-Arm Exoskeletons Using Human Collaborative Manipulation Models

The exoskeleton is mainly used by subjects who suffer muscle injury to enhance motor ability in the daily life environment. Previous research seldom considers extending human collaboration skills to human-robot collaborations. In this article, two models, that is: 1) the following the better model and 2) the interpersonal goal integration model, are designed to facilitate the human-human collaborative manipulation in tracking a moving target. Integrated with dual-arm exoskeletons, these two models can enable the robot to successfully perform target tracking with two human partners. Specifically, the manipulation workspace of the human-exoskeleton system is divided into a human region and a robot region. In the human region, the human acts as the leader during cooperation, while, in the robot region, the robot takes the leading role. A novel region-based Barrier Lyapunov function (BLF) is then designed to handle the change of leader roles between the human and the robot and ensures the operation within the constrained human and robot regions when driving the dual-arm exoskeleton to track the moving target. The designed adaptive controller ensures the convergence of tracking errors in the presence of region switches. Experiments are performed on the dual-arm robotic exoskeleton for the subject with muscle damage or some degree of motor dysfunctions to evaluate the proposed controller in tracking a moving target, and the experimental results demonstrate the effectiveness of the developed control.

Research on user satisfaction of sports and healthcare integration system in post-pandemic era

After COVID-19, some initiatives such as Healthy China, and Smart Living have been widely mentioned. This study explored the factors influencing user satisfaction in sports and healthcare integration services to help system builders and interaction designers better seek opportunities and directions for systems construction. Based on grounded theory method, conducted semi-structured interviews with people who have home exercise needs, and then summarised the influencing factors after coding the raw information level by level. It applied the user experience honeycomb to classify potential variables, used principal component analysis (PCA) to extract representative evaluation indicators as observed variables, and followed the construction of a theoretical model of the satisfaction factors. The structural equation model (SEM) was validated and analyzed to prove its scientific validity and reasonableness. Research showed that the core factors affecting the user experience of sports and healthcare integration system include usefulness, interactivity, usability, credibility, and findability, all of which have a positive and significant impact on user satisfaction. According to the results of empirical analysis, A multidimensional design strategy for sports and healthcare integration system is proposed to provide a reference for improving user satisfaction.

Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry

In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining; specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt & Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.