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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.

A 360-degree view of the different dimensions of this revolution is presented in this book, including the various industries transforming industrial manufacturing, the security and challenges ahead and the far-reaching implications for society and the economy. The main objective of this edited book is to cover the impact that the new advanced society has on several platforms such as smart manufacturing systems, where artificial intelligence can be integrated with existing systems to make them smart, new business models and strategies, where anything and everything is possible through the internet and cloud, smart food chain systems, where food products can be delivered to any corner of the world at any time and in any situation, smart transport systems in which robots and self-driven cars are taking the lead, advances in security systems to assure people of their privacy and safety, and smart healthcare systems, where biochips can be incorporated into the human body to predict deadly diseases at early stages. Finally, it can be understood that the social reformation of Society 5.0 will lead to a society where every person leads an active and healthy life.

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 the low-power, low-cost sensors and aerial devices to sense (soil, temperature, salinity, water, light, insects, pests) and exchange data/images for monitoring and controlling crops.

 – Chapter 10 builds a classification model that classifies whether a customer is going to buy a car with specific features. This research work consisted of four ML models and an analysis of their results. These classifying models were Gaussian Naïve Bayes, decision tree, Karnough Nearest Neighbors and neural networks. The author also attempted to find the best hyperparameter value to obtain the best result from these models. These results are used to compare the accuracies of every model and decide the best model for use in real-time prediction. Here, the author was predicting whether a customer was going to buy a car or not buy a car with particular features available in it. Hence, for this prediction the best accuracy we got was 97.4%, which was given by the decision tree classifier. Also, the neural network had about the same prediction accuracy. Therefore, this ML model can be used by a firm to determine whether or not a new car with specific features will sell well or by a customer wanting to know whether a particular car will be bought by other customers as well.

 – Chapter 11 examines the use of AI and ML in political campaigns. It is divided into three sections—the first section explores internet penetration and the influence of social media on the Indian Lok Sabha election; the second section explores the forms of deepfake and automated social media bots and their use during the election campaign; and the final section explores the future of AI and ML in the election campaign in India.

 – Chapter 12 attempts to explain the impact of segment routing (SR) in software-defined networking (SDN). For this, the authors implemented three algorithms known as multi-objective particle swarm optimization (MOPSO), advanced MOPSO (A-MOSPO) and minimum interference routing algorithm (MIRA) on a Waxman network topology created randomly having 100 nodes. For performance evaluation, MATLAB and parameters such as throughput, link utilization, and delay were taken as the key parameters for evaluating the above protocols in an SDN environment.

 – Chapter 13 discusses the symptoms of COVID-19, precautionary measures against it, ways of spreading the corona-virus, and technologies used to fight it. Also discussed is the impact of COVID-19 on business, financial markets, supply-side and demand-side economics, and international trade on the Indian economy.

 – Chapter 14 discusses the convolutional neural network (CNN) used for detecting skin cancer and compares the accuracy of the model by applying a vast dataset by varying the parameters, such as number of layers, activation functions, etc., to find the best suitable parameters for CNN to design the classifier that could give the best accuracy while classifying the images of the seven types of skin cancer.

 – Chapter 15 presents the hybrid outcome of the firefly algorithm (FA) and artificial potential field (APF) algorithm for humanoid control, which is preferred in the present study for navigational tasks.

 – Chapter 16 proposes a system that considers the student’s academic and behavioral characteristics. The data collected can help faculty members gain a better understanding of a student’s level of knowledge and personality. Based on the information collected, students are grouped into clusters using k-means clustering and a suitable partner is selected for group activities using Irving’s algorithm to enable active learning.

 – Chapter 17 discusses how the workload prediction in cloud environments improves proper utilization of resources so that service level agreement remains at a stable level. Hence, the particle swarm optimization (PSO)-based hybrid wavelet weighted k-nearest neighbors (PHWkNN) algorithm is proposed to predict workload in the cloud data center.

 – Chapter 18 includes a survey for predicting bankruptcy, in which it was concluded that preprocessed datasets have a better prediction outcome and that ensemble models are more powerful for bankruptcy prediction as compared to the single models.

 – Chapter 19 aims to provide a comprehensive review of the research done with respect to the application of AI and ML in the agriculture domain and the key strategies adopted by leading companies like Deere & Company (John Deere, US), Microsoft Corporation, Descartes Labs, ec2ce (Spain), etc., in the agricultural market. The chapter also discusses the current scenario and emerging trends of AI and ML in the Indian agriculture sector. Next, it demonstrates how the application of these technologies has bright prospects in Indian agriculture and can impact the agricultural market in the long term, and how the technological support will boost the agricultural economy by creating new opportunities in agriculture’s operational environments. Finally, it studies the barriers in the application of AI and ML in the Indian context.

The topics presented in each chapter are unique to this book and are based on the unpublished work of the contributing authors. In editing this book, we attempted to bring into discussion all the new trends and experiments for creating an advanced society. We believe this book is ready to serve as a reference for larger audiences such as system architects, practitioners, developers and researchers.

Sandeep Kumar Panda ICFAI Foundation for Higher Education (IFHE), Deemed to be University,Hyderabad, Telangana, India January 2022