Digital twin (DT) is an emerging concept that is gaining attention in various industries It refers to the ability to clone a physical object (PO) into a software counterpart The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry It takes into account related proposals emerging in other fields, namely augmented and virtual reality (eg, avatars), multiagent systems, and virtualization This survey thereby allows for the identification of an extensive set of DT features that point to the “softwarization” of POs To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities Four application scenarios illustrate how the DT concept can be used and how some industries are applying it The scenarios also lead to a generic DT architectural model This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process

/pdf/digital-twin-in-the-iot-context-a-survey-on-technical-4dp6h32olt.pdf

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

/pdf/digital-twin-based-sustainable-intelligent-manufacturing-a-32gjodfp5s.pdf

Digital twin-based sustainable intelligent manufacturing: a review

The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector.

https://www.mdpi.com/1424-8220/20/22/6458/pdf

Survey on Security Threats in Agricultural IoT and Smart Farming.

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

Urban traffic flow prediction techniques: A review

This paper evaluates the different dimensions of Big Data in various fields of applications with the Volume, Variety and frequency of generations of huge data. Also this paper focuses on the areas where large volume of data is being used for the growth and progress of the organizations, in which the data received currently, must ensure that it attains the uniqueness which will be taken by the different stakeholders of the organizations in their respective environments.

Investigations on big data features research challenges and applications

In this paper a unique approach towards decision making process and better quality care in Pati healthcare applications is developed by using the Machine Learning (ML) concepts as an alternative mode for identifying the characteristics and patient satisfaction (PS) in the proposed healthcare system. Extracting the information from raw data using some algorithmic approach is known as Data mining. In this a ML approach is used in determining the patient satisfaction in health care sector. Applied regression models to determine the patient satisfaction and also correlation methods is identified as an important attribute to be considered in determining the better quality of health care application models. The data set is taken based on the opinion on three types of data such as a) patient opinion towards hospital care b) nurse opinion towards workplace and c) Administrative aspects of healthcare. Our findings revealed high accuracy in Regression (88%), that helps in concluding by considering the administrative and workplace attributes related to patient satisfaction. The results are validated using traditional statistical methods like binomial correlation and linear regression.

Reliable Machine Learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care

IoT – a new proliferation in the technological advancement, changed the way object is perceived and used. It enables connecting smart objects to the internet and aims to develop new promising future to Intelligent Transportation System (ITS). ITS uses techniques such as wireless communication, computational technologies, GPS, and sensor technologies to provide smart and quick services to users and to be better informed and make safer, more coordinated, and 'smarter' use of transportation medium. As number of objects connected to ITS application increases, the amount of data generated also increases and they are send to cloud for data analysis and knowledge discovery. However, sending and retrieving of data across cloud is less useful due to delay latency and others. An alternative to cloud is fog (edge) model that overcomes the weakness of cloud by analyzing and discovering knowledge at the edge. However, the fog computing model has limited computational capability. For an IoT enabled Intelligent Transportation System with enormous number of objects connected, neither cloud nor fog computing model addresses the issues alone. This paper focuses on presenting an IoT hybrid model for Intelligent Transportation System (ITS). We also address the effectiveness of the model by discussing use case scenarios.

IoT Hybrid Computing Model for Intelligent Transportation System (ITS)

Digital twin will gain new insight in sports domain. It will not only help to connect and monitor sportsperson virtually in a lab but also help to simulate actions, movements in the ground and play shots which recommend coach and trainer to redefine the game strategy on the fly or even half time or break during the match.

Reliable Digital Twin for Connected Footballer

The number of connected things or devices will be 125 billion by 2030 as per IHS market survey It indirectly translates that large number of users will be accessing these devices for collecting, controlling, monitoring and building edge analytics The device management policy and data policy on accessing data from different resources needs proper governance and control The data security will help these users to assure regulatory compliance, security, privacy and protection This paper is a centralized data security management across different layers of Internet of Things along with rule builder Mainstream of this study focusses on compliance standard, identity management, data management and policy engine and audit reports The specific regulatory compliance, security standards or firmware or hardware security and vulnerabilities at IoT layer has not been captured in this paper

Centralized Reliability and Security Management of Data in Internet of Things (IoT) with Rule Builder

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