Cloud Material Handling Systems: Conceptual Model and Cloud-Based Scheduling of Handling Activities

Since the past few years, the vehicular network has gained significant attention because of its powerful potential applications such as traffic management, surveillance, and safety. The modern vehicles are equipped with smart sensors, actuators, and efficient communication devices such as GPS and embedded hardware. The vehicular network potential application outcomes are achieved by using vehicles onboard computational, communication, and storage capabilities with the help of cloud computing. Thus, the aim of the vehicular cloud network is to improve the traditional transportation system. The smart vehicle is equipped with smart devices such as computer on wheels, GPS devices, collision radars, and intelligent radio transceivers. The Internet of Things (IoT) and cloud computing have provided a solution to handle the increasing traffic congestion and vehicular safety. The cloud-based vehicular IoT network uses a number of software services which include sensor service, cloud service, and platform service. These services, when interacting with each other, provide a basic architecture to build traffic control and cloud-based vehicular data processing system. The IoT-based vehicular cloud network allows automobile manufacturers to innovate smart features into the vehicles with low cost, which also increases their market competitiveness. Automobile companies are utilizing cloud services from different cloud providers to support various service-level agreements. Since more and more vehicles are equipped with sensors that can access the Internet, vehicular services are combined with different cloud services to map, encapsulate, and aggregate the vehicular data to form the vehicular network platform. With the increasingly growing data in the cloud-based vehicular network, there are fundamental engineering challenges such as big data collection, data analysis for traffic management, real-time decision-making, and the ability to understand each other’s application formats and service-level agreement templates. The vehicular network has combined various technologies to handle these issues, such as machine learning, artificial intelligence, database management, and data mining. Similarly, cloud interoperability issues arise to support heterogeneous programming interfaces, programming languages, data models, and operating systems in an efficient and reliable manner. With the advancement in the mobile communication system, the vehicular cloud network can facilitate the scalable system with a reduced cost, efficient routing, resource sharing, and monitoring in a secure and efficient manner. The IoT-based vehicular cloud network is a complex system of interconnected sensors and communication tools and cloud platform. We can divide such a system into a number of subsystems and dimensions which include traffic management, data information processing, and service routing. The cloud-based vehicular network layered these services into cloud computing three distinct dimensions that include software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). In this chapter, we study the advances in the use of IoT in the transportation system via a cloud platform for developing an efficient vehicular cloud network. Thus, IoT and cloud computing in the automotive domain are studied. Similarly, the effectiveness of the vehicular network depends on its ability to handle large heterogeneous sensors and heterogeneous cloud platforms. Hence, the interoperability challenges at various cloud service levels and global standard issues are discussed. We also provided an analysis of how machine learning and blockchain can be applied to IoT-based vehicular cloud networks for self-learning and security mechanism, respectively.

Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The entire digitalized manufacturing systems operate through the Internet, and hence, cybersecurity threats have become a problem area for manufacturing companies. The impacts can be very serious because cyber-attacks can penetrate operations carried out in the physical infrastructure, causing explosions, crashes, collisions, and other incidents. This research is a thematic literature review of the deterrence to such attacks by protecting IoT devices by employing provenance blockchain and artificial intelligence. The literature review was conducted on four themes: cloud manufacturing design, cybersecurity risks to the IoT, provenance blockchains for IoT security, and artificial intelligence for IoT security. These four themes of the literature review were critically analyzed to visualize a framework in which provenance blockchain and artificial intelligence can be integrated to offer a more effective solution for protecting IoT devices used in cloud manufacturing from cybersecurity threats. The findings of this study can provide an informative framework.

Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new manufacturing paradigm – cloud manufacturing (CMfg) – for solving the bottlenecks in the informatisation development and manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, manufacturing cloud service and manufacturing cloud are studied, and the constructing method for manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented.

Real-Time Monitoring System to Lean Manufacturing

With the advancement of new technologies, the four main pillars of digital transformation, including cloud computing, Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), are simultaneously shaping the future of information technologies. Cloud computing, as a scalable and accessible platform, provides the ability to store and process huge data generated by IoT devices. These changes, together with artificial intelligence and machine learning, which have the ability to analyze complex data and make intelligent decisions, will elevate IoT capabilities to a new level.This article examines the role of cloud computing in the development and evolution of the Internet of Things. Using cloud computing, data generated by devices connected to the Internet of Things is collected, stored, and processed centrally. This process leads to reduced hardware costs, improved scalability, and increased flexibility of systems. In this regard, machine learning allows systems to automatically learn from collected data and make accurate predictions, and artificial intelligence helps in deeper data analysis and enables IoT systems to make optimal decisions that are in line with environmental changes and user needs. Together, these two technologies enable systems to adapt more effectively to changing conditions and optimize their performance. Finally, the article examines the challenges and opportunities arising from the combination of cloud computing, the Internet of Things, artificial intelligence, and machine learning, and provides solutions for better use of these technologies in various industries. These industries include healthcare, transportation, renewable energy, and smart cities. Research results show that the use of these technologies can lead to improved productivity, reduced costs, and increased security in IoT-based systems. This article not only emphasizes the importance of the convergence of these technologies but also points out the need to develop strategies to better exploit them in order to create innovation in different industries.

Synergetic manufacturing systems anchored by cloud computing: A classified review of trends and perspective

Today, in order to survive in the rapid challenging environment of the modern manufacturing era, manufacturers are forced to adopt new technologies, especially for products that are made in small batch production. Automated Guided Vehicles (AGV) has been applied for the automated manufacturing system. In industrial application, manufacturing factory is brought the mobile vehicle to incorporate working with other machine in order to being the automated manufacturing system (Arai et al., 2002).Advanced automated manufacturing systems are widely used in industrial companies where productivity objectives have to be met. These systems often being costly, they must be designed to be as efficient as possible. Here, an automated manufacturing system in a job shop layout considering AGV as a material handling resource is focused. The key issue in manufacturing operations is how to produce high quality products at low costs in such a way that the diversified demand is met. Hence, modern manufacturing companies should become as responsive as possible in order to satisfy customer demands. Material handling accounts for 30–75% of the total cost of a product, and efficient material handling can result in reducing the manufacturing system operations cost by 15–30% (Sule, 1994). These points underscore the importance of material handling costs reduction as a key element in improving the cost structure of a product. The determination of a material handling system involves both the selection of suitable material handling equipment and the assignment of material handling operations to each individual piece of equipment. Hence, material handling system selection can be defined as the selection of material handling equipment to perform material handling operations within a working area considering all aspects of the products to be handled.The material handling system plays a crucial role in automated manufacturing systems. When inadequately designed, the material handling system indeed can adversely affect the overall performance of the system and lead to substantial losses in productivity and competitiveness, and to unacceptably long lead times. Thus, to avoid such pitfalls, material handling system design must be integrated into the overall design of the manufacturing system centering on the selection of machines and the allocation of operations to the machines (Butdee and Suebsomran, 2006; Shirazi et al., 2010).Automated guided vehicle is an intelligent machine that has ‘intelligence’ to determine its motion status according to the environmental conditions systems. AGVs are advanced material handling devices extensively used in automated manufacturing systems (AMS) to transport materials among workstations (Vis, 2006).

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.

High-precision indoor positioning technology is regarded as one of the core components of artificial intelligence (AI) and Internet of Things (IoT) applications. Over the past decades, society has observed a burgeoning demand for indoor location-based services (iLBSs). Concurrently, ongoing technological innovations have been instrumental in establishing more accurate, particularly meter-level indoor positioning systems. In scenarios where the penetration of satellite signals indoors proves problematic, research efforts focused on high-precision intelligent indoor positioning technology have seen a substantial increase. Consequently, a stable assortment of location sources and their respective positioning methods have emerged, characterizing modern technological resilience. This academic composition serves to illuminate the current status of meter-level indoor positioning technologies. An in-depth overview is provided in this paper, segmenting these technologies into distinct types based on specific positioning principles such as geometric relationships, fingerprint matching, incremental estimation, and quantum navigation. The purpose and principles underlying each method are elucidated, followed by a rigorous examination and analysis of their respective technological strides. Subsequently, we encapsulate the unique attributes and strengths of high-precision indoor positioning technology in a concise summary. This thorough investigation aspires to be a catalyst in the progression and refinement of indoor positioning technologies. Lastly, we broach prospective trends, including diversification, intelligence, and popularization, and we speculate on a bright future ripe with opportunities for these technological innovations.

In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage.
 The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations.
 The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field.
 Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).

With the rapid development of cloud computing, the Internet of Things (IoT) and other new generation information technologies, a new service-oriented networked intelligent manufacturing mode—cloud manufacturing (CMfg), is emerged. In the CMfg paradigm, cloud manufacturing platform service innovation is an effective way to improve the service function, service quality and service efficiency of cloud manufacturing platforms. However, the service innovation of cloud manufacturing platforms is complex system engineering, which needs to consider many participants with independent interests simultaneously. If one party’s behaviour strategy changes, the whole system will be affected. In order to adapt the decision-making management of manufacturing service innovation in cloud manufacturing system, to provide the effective decision-making support for service innovation of cloud manufacturing system in uncertain environment, to explore the interaction mechanism and equilibrium strategy of various stakeholders in the evolution process of cloud manufacturing system. This research constructs a tripartite evolutionary game model among participants based on evolutionary game theory, Analyses the influence of participants’ strategy choices, The Cloud manufacturing platform’s service quality coefficients and users’ preference Coefficients on the equilibrium state stability of cloud manufacturing systems, and uses MATLAB software for numerical simulation. The results show that the evolutionary stability strategy of cloud manufacturing enterprises plays a decisive role in the service innovation of the cloud manufacturing platform. When cloud manufacturing enterprises choose a noncooperation strategy, the evolutionary stability strategy of the cloud manufacturing platform is innovation. When cloud manufacturing enterprises choose a cooperation strategy, the evolutionary stability strategy of the cloud manufacturing platform is affected by the platform’s service quality coefficient and the user’s preference coefficient, and both factors can promote the evolution of cloud manufacturing platform service innovation. The platform’s service quality coefficient has a threshold value, which depends on the relative size of benefits, costs and user’s preference coefficient generated by the service innovation of the platform. Therefore, in the cloud manufacturing mode, the cloud manufacturing platform must rely on different enterprises to provide their own innovation resources to realize the service innovation of the cloud manufacturing platform, so the cloud manufacturing platform should focus on introducing a group of high-quality cloud manufacturing enterprises for cooperation. In addition, users’ strong demand for high-quality services can provide sufficient impetus for cloud manufacturing enterprises to choose cooperation and cloud manufacturing platforms to choose innovation, which can not only encourage cloud manufacturing platforms to provide personalized services for users but also avoid the extra waste of resources.

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

The Internet of Things (IoT) plays an indispensable role in the field of agriculture. IoT is a sophisticated automation and data analysis structure that makes use of sensing, networking, big data, cloud computing, machine learning, artificial intelligence, and other emerging technologies that can keep surprising the whole research, business, marketing, and financial areas. The implementation and usage of IoT in agricultural applications can increase productivity and profitability. The evolution of smart sensors and smart devices along with current lightweight communication protocols enhanced the feasibility of interconnecting agricultural things to monitor agriculture fields with full automation and it is called as Internet of Agriculture Things (IoAT). It is the group of agriculture devices or sensors and applications that merge with agriculture cloud platforms. An agriculture device enabled with Wi-Fi facilitates machine-to-machine communications and also facilitates the employment of Wireless Sensor Networks. The proposed IoAT architecture gives an insight into complete IoT fundamental concepts, environmental and deployment sensors, hardware platforms, wireless communication technologies, IoT cloud platforms, and machine learning techniques; different layers are associated with IoAT and its applications.

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber–physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber–physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.

With the rapid development of Internet of Things (IoT) technology, location-based services have been widely applied in the construction of smart cities. Satellite-based location services have been utilized in outdoor environments, but they are not suitable for indoor technology due to the absence of global positioning system (GPS) signal. Therefore, many indoor localization technologies and systems have emerged by utilizing many other signals. In particular, fingerprinting localization has recently garnered attention because its promising performance. In this work, we aim to study recent indoor localization technologies and systems based on various fingerprints, which use machine learning and intelligent algorithms. We also present the architecture of intelligent localization. The development of indoor localization technology should have the ability of self-adaptation and self-learning in the future. And the architecture shows how to make localization become more “smart” by advanced techniques. The state-of-the-art localization systems’ working principles are summarized and compared in terms of their localization accuracy, latency, energy consumption, complexity, and robustness. We also discuss the challenges of existing indoor localization technologies, potential solutions to these challenges, and possible improvement measures.