As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQC) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This paper verifies the potential of QRL, which will be further realized by implementing quantum multi-agent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multi-robot control and coordination in smart factory applications. However, the extension is not straightforward due to the non-stationarity of classical MARL. To cope with this, the centralized training and decentralized execution (CTDE) QMARL framework is proposed under the Internet connection. A smart factory environment with the Internet of Things (IoT)-based multiple agents is used to show the efficacy of the proposed algorithm. The simulation corroborates that the proposed QMARL-based autonomous multi-robot control and coordination performs better than the other frameworks. 

/pdf/quantum-multiagent-actor-critic-neural-networks-for-internet-3fcl84t6.pdf

Quantum Multiagent Actor–Critic Neural Networks for Internet-Connected Multirobot Coordination in Smart Factory Management

Advanced Machine Learning (ML) algorithms can be applied using Edge Computing (EC) to detect anomalies, which is the basis of Artificial Intelligence of Things (AIoT). EC has emerged as a solution for processing and analysing information on IoT devices. This field aims to allow the implementation of Machine/Deep Learning (DL) models on MicroController Units (MCUs). Integrating anomaly detection analysis on Internet of Things (IoT) devices produces clear benefits as it ensures the use of accurate data from the initial stage. However, this process poses a challenge due to the unique characteristics of IoT. This article presents a Systematic Literature Mapping of scientific research on the application of anomaly detection techniques in EC using MCUs. A total of 18 papers published over the period 2021-2023 were selected from a total of 162 in four databases of scientific papers. The results of this paper provide a comprehensive overview of anomaly detection using TinyML and MCUs. The main contributions of this survey are the fact that it aims to: a) study techniques for anomaly detection in ML/DL and validation metrics used in the AIoT; b) analyse data used in the estimation of models; c) show how ML is applied in EC using hardware or software; d) investigate the main microcontrollers, types of power supply, and communication technology; and e) develop a taxonomy of ML/DL algorithms used to detect anomalies in TinyML. Finally, the benefits and challenges of this kind of TinyML analysis are described. 

Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping

Sustainable connectivity between an Augmented Intelligence of Things (AIoT) system and terminal devices can provide a strong guarantee for an enterprise's continuity of production. To ensure sustainable data interaction in emergency situations such as damage to communication infrastructure facilities in a postdisaster period, a pyramidal air–space–ground network model is proposed in this paper, and its transmission performance is investigated. The space network, constructed of geostationary (GEO) satellites and medium and low Earth orbit (MEO and LEO) satellites, is heterogeneously fused with the terrestrial AIoT network to ensure around-the-clock network service for enterprise production. Then, bandwidth–delay product (BDP) analysis is performed for this heterogeneous network. The proposed scheme improves the data transmission efficiency of the network model by mitigating the impact of a long round-trip time (RTT) and improving the congestion window optimization in the case of data loss. The proposed scheme increases the transmission rate in the network model by a factor of 3.72 compared to the traditional transmission scheme in the case of a 5-hop terrestrial AIoT network and has a 3.94-fold advantage in download time. 

Sustainable Cross-Regional Transmission Control for the Industrial Augmented Intelligence of Things

Ensuring correct trace links between different types of artifacts (requirements, architecture, or code) is crucial for compliance in safety-critical domains, for consistency checking, or change impact assessment. The point in time when trace links are created, however, (i.e., immediately during development or weeks/months later) has a significant impact on its quality. Assessing quality thus relies on obtaining a historical view on artifacts and their trace links at a certain point in the past which provides valuable insights on when, how, and by whom, trace links were created. This work presents TimeTracer, a tool that allows engineers to go back in time - not just to view the history of artifacts but also the history of trace links associated with these artifacts. TimeTracer allows easy integration with different development support tools such as Jira; and it stores artifacts, traces, and changes thereof in a unified artifact model.

/pdf/timetracer-a-tool-for-back-in-time-traceability-replaying-2lah4zkffs.pdf

TimeTracer: a tool for back in time traceability replaying

The Industrial Internet of Things (IIoT) focuses on connecting devices to gather real-time data and manually analyze it to make decisions. The IIoT devices could be equipped with some intelligence that empowers them to automatically create an informed choice and act with the ability for self-correction. This paper presents an innovative and secure approach to using industrial intelligence of things (IIoT 2.0) devices for designing an automated smart factory management system. Firstly, a blockchain-based secure order placement technique is proposed in a smart factory management system to store the production record permanently and avoid third-party interaction. This paper also introduces cooperative autonomous product tracking and maintenance systems using automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) to minimize human interaction inside a smart factory. Moreover, a deep learning model-based automated anomaly detection and prevention approach is demonstrated to take proactive action by the UAV and AGV inside a smart factory. Finally, to address the security issue among IIoT 2.0 devices, a secure data transfer approach based on public-key infrastructure (PKI) is utilized. Result analysis of the proposed system shows that the blockchain network can form the blocks very rapidly using the private blockchain network. Furthermore, PKI implementation confirms that the proposed smart factory management system's IIoT 2.0 devices can swiftly transfer secure data and instructions from one device to another. 

Industrial Intelligence of Things (IIoT 2.0) based Automated Smart Factory Management System using Blockchain

The descending of intelligence from the cloud to the heterogeneous and low-power edge in the Artificial Intelligence-of-Things prevents uploading user-sensitive information to the cloud. It brings an urgent demand for deploying training tasks collaboratively in industrial scenarios to manage data locally. This article proposes an edge-based collaborative training system for the smart factory which harnesses the intelligence of edge devices by balancing the computational and communicational resources and improving system dependability. Two typical scenarios of parts recognition and defect inspection are evaluated as a case study with our system. The feasibility and dependability of the presented system are verified with a platform composed of eight high-performance (Nvidia Jetson Nano) and eight low-performance edge devices (Raspberry Pi 4B). The efficiency under tradeoff between computational resource and network condition constraints in a cluster is tested to simulate real-case performance in smart factory scenarios. Our platform reaches the peak performance of 1167 images/s training efficiency on ResNet32 under a 125 MB/s bandwidth. Experimental results demonstrate that the proposed design can collaboratively perform training tasks with optimized efficiency and provide dependable collaborations for system fault detection and cluster extension.

Edge-Based Collaborative Training System for Artificial Intelligence-of-Things

Under the background of the information explosion, there are numerous knowledge stars in all walks of life on the Internet. Taking java learning as the main case, you can easily find a lot of learning knowledge by searching “Java” in station B; On the one hand, while enjoying the dividends brought by the times, learners also feel a lot of inconvenience on the other hand, such as selecting high-quality learning materials, storing the source of learning materials, and building other people's knowledge into their own knowledge system. In view of the above problems, this paper proposes to develop and design a java knowledge map system with the advantages of mind mapping, which can effectively record the source of high-quality learning materials and the idea of high-quality personal system. The system adopts the development mode of front-end and back-end separation. The back-end and front-end adopt Spring Boot and Vue architecture respectively, integrate the ORM framework MyBatis, and the database adopts Neo4j diagram database and MySQL relational database for storage. The system shows the system of knowledge in the form of map, effectively records learning materials and transmits learning ideas.

Design and Implementation of Java Knowledge Graph Based on Neo4j

In recent years, with the continuous development of information technology, the information revolution has swept the world, which has changed the lifestyle of people at home and abroad. Taking the most basic shopping as an example, great changes have been realized from offline to online, from single to multiple. The development of ecommerce platform is the most rapid. Electronic mall is a commodity trading platform built by e-commerce software. The electronic mall based on the Internet and B2C can effectively integrate resources. This paper, through the analysis and research of Jilin Province characteristic agricultural products, carries out the design and development of Jilin Province characteristic agricultural products ecommerce platform, and makes contributions to social construction and self-realization.

Design and Implementation of E-Commerce Platform for Characteristic Agricultural Products

This paper describes the design of a business rules engine management system based on Drools. Through the visual encapsulation of Drools, users can use a way similar to natural language to describe the originally complex, complicated and professional rules, and through the abstract configuration of business objects and parameter objects, the system can meet the business requirements of different business scenarios. In the whole life cycle management of rules, parameter table and parameter data, the system supports users to control the change of data state through three-level approval mode, so that users can conveniently carry out the configuration and management of various data in the system. At the same time, the system will record each key operation log, in order to facilitate enterprises and institutions to carry out system audit.

Application Research of Business Rules Engine Management System Based on Drools

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Edge-Based Collaborative Training System for Artificial Intelligence-of-Things

Design and Implementation of Java Knowledge Graph Based on Neo4j

Design and Implementation of E-Commerce Platform for Characteristic Agricultural Products

Application Research of Business Rules Engine Management System Based on Drools