The New Advanced Society. Группа авторов

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      Dedication

       Dedicated to my sisters Susmita, Sujata, Bhaina Sukanta, my nephew Surya Datta, my wife Itishree (Leena), my son Jay Jagdish (Omm), my late father Jaya Gopal Panda and late mother Pranati Panda.

       Sandeep Kumar Panda

      Preface

      The primary goal of an advanced society, also known as Society 5.0, is to create a human-centric society in which economic progress and social problems are balanced by implementing a system that integrates cyberspace and physical space. That is, a society which aims to create a better social and economic model by adapting the technological innovations of Industry 4.0. Society 5.0 is a huge societal transformation plan that visualizes a “super-smart society.” It is a follow-up to Industry 4.0, where “information” was the predominant factor but cross-sectional knowledge sharing was not adequate, making cooperation among different sectors difficult. Also, finding the information needed from among information overflow is a tedious task, thereby limiting the scope of actions due to various factors like lack of skills, different abilities of those doing the work, etc. In Industry 4.0, data is accessed from the cloud and operations like searching, retrieving and analyzing data happen over the internet, with the burden of analysis being carried by humans. Whereas, in Society 5.0, people, systems and things will all be connected and the vast amounts of data from sensors will be collected in real time, accumulated and analyzed using artificial intelligence (AI), and the resultant analyses fed back to humans in different forms. Society 5.0 balances economic advancements with the resolution of social problems by incorporating the latest technological advancements like big data, AI, the internet of things (IoT) and robotics in all industrial and societal activities. Of course, Industry 4.0 will be a major component of Society 5.0, but is not the only component—it is also about citizens, organizations, stakeholders, academia and so on. In short, using the technological advancements to provide solutions to better the lives of humans is what an advanced society all about. Some salient features of an advanced society are problem-solving and value-adding, bringing out divergent abilities, decentralization, resilience, sustainability and environmental harmony.

      Included in this book are the fundamentals of Society 5.0, artificial intelligence, and the industrial internet of things, featuring their working principles and application in different sectors. In order to meet these objectives, accomplished writers in the field have contributed the 19 chapters summarized below.

       – Chapter 1 discusses areas of management, cybercrime in the financial sector, human depression, and school/college closures. Moreover, the constraints posed by returning migrant workers and the remedial measures devised to overcome them, and how to build a new advanced society in a post-COVID-19 era are also discussed.

       – Chapter 2 elaborates on the clients, architects, contractors, material suppliers, etc., in the construction industry. The complex supply chain of globally manufactured construction products has to be managed for the sake of meeting quality requirements and customer satisfaction. But, the lack of accountability in the construction industry sometimes leads to various types of errors, delays, and even accidents at some stages. This chapter introduces the key to ending these disputes with the help of Corda, a distributed ledger platform for permissioned networks inspired by blockchain technology. This helps in maintaining transparency among the actors involved in this industry, thus avoiding any miscommunication.

       – Chapter 3 analyzes the identity and access management challenges in the IoT, followed by a proposal of a cloud identity management model for the IoT using distributed ledger technology.

       – Chapter 4 elucidates the development of an efficient deep neural network (DNN) with a reduced number of parameters to make it real-time implementable. The architecture was implemented on German traffic sign recognition benchmark (GTSRB) dataset. Four variations of neural network architectures—feedforward neural network (FFNN), radial basis function neural network (RBNN), convolutional neural network (CNN), and recurrent neural network (RNN)—are designed. The various hyperparameters of the architectures—batch size, number of epochs, momentum, initial learning rate—are tuned to achieve the best results.

       – Chapter 5 deals with the basic aspects of honeypots, their importance in modern networks, types of honeypots, their level of interaction, and their advantages and disadvantages. Furthermore, this chapter also discusses how honeypots enhance the security architecture of a network.

       – Chapter 6 provides an in depth review of the necessity for security in IoT platforms and applications of the industrial internet of things (IIoT). Over the past decade, cyberattacks have mostly occurred on IoT devices; therefore, cybersecurity is introduced to deal with these cyberattacks. Furthermore, one of the chief attack modes in the IIoT are botnets and denial-of-service attacks. These attacks happen in several ways, and once they have occurred it is hard to predict and stop them. This chapter highlights many suggestions from diverse authors, which are detailed in tabular form.

       – Chapter 7 proposes an efficient navigation controller embedding hybrid Jaya-DE algorithm to obtain the optimum path of an individual robot. The efficiency of the proposed navigation controller was evaluated through simulation. The outcomes of the simulation revealed the efficacy of the suggested controller in monitoring the robots towards achieving a safe and optimal path. The strength of the suggested controller was further verified with a similar problem framework. The potency of the proposed controller can be seen from the outcome in resolving the navigation of mobile robots as compared to its competitor.

       – Chapter 8 discusses a study conducted for diagnosing Parkinson’s disease using different machine learning (ML) algorithms for categorization and severity prediction through the measure of 16 voice and eight kinematic features accomplished with various archives. The dataset included 40 people with Parkinson’s disease and healthy patients generated with the help of spiral drawings and voice readings. Of the various ML algorithms used for estimating, the highest accuracy (94.87%) was demonstrated by ANN, while Naïve Bayes was the least precise (71.79%). The work also predicted a severity score by suggesting some scientific measures with a prototype dataset.

       – Chapter 9 discusses the challenges faced in the development of a multi-sensor classification system and their possible solutions. Smart agriculture in rural areas can largely benefit from


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