In the future, the real world will convert to a smart world around 2025. One could predict that there will be a changeover from 4G LTE to 5G NR. In pandemic conditions, 4G LTE has been found to provide good online support, such as accessing the Internet for education, administration, banking, official works, etc., anywhere in the real world. But there are some limitations, such as operating machines in industries, and driving vehicles on the road with the help of the Internet. These facilities will be provided by 5G NR as there is a large difference between 4G LTE and 5G NR. In 4G LTE, only Mobile Broad Band (MBB) is present, but in 5G NR, there are three terms, i.e., Enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low Latency Communication (URLLC) and massive Machine Type Communication (mMTC). As a result, the city will convert into a smart city. It is possible by applying intelligence in various technologies. Applying intelligence will lead to the improvement of smartness in the environment, mobility, building, home, administration, health, education, etc. The smartness of the item includes the utilization of the Internet in various devices, which means the Internet of Things (IoT). In previous times, humans communicate with humans, but in IoT, a human will communicate with the device. In the future, it will be realized using NXP Semiconductors. NXP semiconductors manufactured various chips, which should be beneficial for the formation of smart cities. In the near future, facilities will be increased in a more massive manner than the present time. By 2030, the goal will have been fully attained, and IoT will have evolved into the Internet of Everything (IoE), meaning that everything will be made possible by the Internet. Device-to-device communication will be a possibility in IoE side-by-side. This outlines how 5G to 6G will change. 

Smart computing environments have evolved with the dawn of the Internet of Things, incorporating pervasive or ubiquitous computing. Besides using sensors and smart devices, the main objective has been to make these environments intelligent by utilizing different kinds of artificial intelligent methods and algorithms. Making a system intelligent requires inclusion and implementation of various tools and technologies to facilitate artificial intelligence. This chapter focuses on identifying the most prominent enabling technologies in making smart computing environments intelligent. The ten foremost intelligence-enabling technologies – predictive analysis, deep learning, artificial neural network, big data analytics, intelligent edge, human-computer interaction, computer vision, explainable artificial intelligence, natural language processing and robotics have been discussed in this chapter. 

Cities across the globe are installing sensors, actuators and other devices, to become safer, greener, sustainable, and efficient with the hope of improving the urban interests of people. Sensing and collection of records are at the heart of any smart infrastructure, which can display itself and act on its own intelligently. Using sensors to screen public infrastructures, including bridges, roads, and homes, presents cognizance that enables more efficient use of resources based on the facts amassed by those sensors. As smart sensors, actuators, etc., play a critical role in the smart infrastructure, this chapter explores the smart sensors and actuators in IoT-enabled smart cities. As the domain of smart cities is emerging in the present days with a huge number of research opportunities for the researchers, also data collection and sensing play their role at the heart of the infrastructure. This chapter will critically explore the role and importance of Smart sensors and actuators and their applications, challenges, and opportunities, followed by various future trends in the domain of the smart city.

The innovative concept of smart cities drives economic growth and provides a standard of life through better services to the citizens. The incorporation of advanced technologies in smart cities projects smart outcomes that can be provided to the citizens. In the growth of smart cities, various public sectors, such as education, health care, and communication are adopting recent technologies such as IoE, Cloud/Fog computing, and Big data. These existing technologies integrate various IoE-based networks to manage various activities of smart cities. Many smart city projects are being implemented in different countries. Although various challenges are being faced, still, recent technologies are extensively being deployed in various projects of smart cities. To provide solutions to smart cities many standards have been implemented globally. These standards provide various solutions and concrete guidelines for the proper functioning of smart cities. The concept of smart cities is also facing some challenges in their planning, design, and implementation. Security and privacy of the IoE systems is one of the major challenges faced in the projects of smart cities. The main objectives of smart cities are to provide smart solutions to humans for their daily life problems and to create a sustainable environment. Researchers are developing various models of smart cities that can be implemented in real life to support the sustainable development of smart cities. This chapter explores numerous IoE applications which are also concerned with smart cities. The chapter discusses existing technologies that have a great contribution to the development of various prominent areas of smart cities. The chapter identifies and categorizes several challenges that are being faced by the stakeholders and officials in the construction of smart cities. At the end of the chapter, some research directions have also been discussed that can be helpful in the implementation of IoE-based applications and their deployment in smart cities.

In this digital world integrating smart city concepts, a smart city is a technically advanced metropolitan region that collects data using various electronic technologies, voice recognition methods, and sensor devices. It would not be wrong to say that this smart city is based on the Internet of things (IoT) and artificial intelligence (AI). The IoT and AI are closely related. IoT systems generate big data, and data is at the heart of AI and machine learning. Simultaneously, as the number of linked devices and sensors expands, the importance of smart technologies is also growing faster in this domain. These technologies enable contextual understanding and allow smart devices to solve our problems. Today, the applications of computational intelligence in IoT products and smart devices used in smart cities vary. AI can be leveraged to drive efficiency and improve human living quality for the smart cities of tomorrow. NLP gives the powers to AI tools that recognize and respond in natural Language. This chapter focused on a definite area of AI called Natural Language Processing, which helps and enhances human living in smart cities. There are many use cases where this AI technology makes sense for smart cities. Computerized healthcare services assist policymakers in implementing smart cities in becoming brighter, using opinion mining and permission to remodel a house. These use cases are achieved and discuss various applications, scopes, techniques, advantages, disadvantages, and future scope of NLP of AI in Smart Cities. 

Intelligent transportation system (ITS) is a promising technology to enhance driving safety and efficiency within smart cities. It involves public transportation management, infrastructure control and road safety. Its main purpose is to avoid risks and accidents, reduce traffic congestion and ensure safety for road users. Vehicular ad hoc networks (VANET) are core components of ITS where wireless communications between vehicles, as well as between vehicles and infrastructure, are possible to allow exchanging road, traffic or infotainment information. VANET is vulnerable to several security attacks that may compromise the driver’s safety. Using misbehavior detection approaches and information analysis demonstrated promising results in securing VANET. In this context, Machine Learning techniques proved their efficiency in detecting attacks and misbehavior, especially zero-day attacks. The goal of this chapter is twofold. First, we intend to analyze the security issue in VANET by reviewing the most important vulnerabilities and proposed countermeasures. In the second part, we introduce a comprehensive Machine Learning framework to design a VANET IDS. We used the framework to evaluate the performances of several Machine Learning techniques to detect position attacks using the VeReMi security dataset. Experimental results prove that KNN, Decision Tree and Random Forest outperform Logistic Regression, SVM and Gaussian Naïve Bayes in terms of Accuracy, F-measure, Precision and Recall. 

Positioning applications using GPS, A-GPS, and other technologies are now commonly found in most held hand smart devices. The advents in applications/tools such as Google Maps have made outdoor positioning and guidance much easier. Compared to outdoor positioning, Indoor positioning has always been more challenging. Indoor positioning faces an uphill task of proper Position fixing due to an array of issues that are otherwise absent in the outdoor environment. In this chapter, through trilateration, we have devised an application that takes the help of Wi-Fi signals and does Position fixing in an indoor environment. Indoor localization and positioning is still a challenging topic in Wireless Sensor Networks, and also it is vital because of its effects on monitoring, power consumption, etc., for our work distance calculation of an object using the proposed path loss model, and Trilateration method is implemented to calculate the unknown Position of a device under the environment. It collects all the Wi-Fi signals and finds the exact matches with the database to calculate the user's actual Position on the map. It reduces the complexity of computing the distance of different access points from the user and reduces error. The tool was found to be quite promising in detecting the Position of the host device. Future work that can be extended from this work can include work with a path loss model, Multi-Sensor Fusion, to the inclusion of pattern recognition.

 The Internet of Things (IoT) has been massively revolutionizing human lives for the last few decades. The powerful and steady advancements in the field of science and technology have aided this process immensely. As a result, almost all aspects of our lives have grown smarter. Nowadays, life is almost unthinkable in the absence of IoT-enabled smart devices, such as smart televisions, smart computers, smart-phones, smart fitness trackers, etc. Needless to say, all these devices enjoy ever-growing popularity in this era of smart technology. This development is propelled by the existing digital communication backbone – the Internet. The very Internet, over which human communication started just a few decades back, is now being used by each and everything in our surroundings, be it natural or man-made. The advent and inclusion of IoT in recent times have highlighted how trees, crops, fruits, chairs and tables, electrical appliances, and all other objects around us can interact with each other. They are capable of communicating as freely as humans, and based on such communications, these things’ can even behave smartly, individually, and in unison, by making informed decisions in real-time! Given that these things do not possess the gift of life naturally, their ability to express themselves comes from the use of numerous types of sensing devices, also called sensors. The intelligent manufacturing and easy availability of these miniature, cost-effective sensing devices have given a new shape to almost all aspects of our lives. The data regarding the behavior of things, as captured by sensors, is essentially what the things express, and it carries meaning in the particular setting. This data may then be processed and analyzed at the source, transmitted over the internet and processed in a cloud or remote machine. While in some day-to-day applications, the data is used directly for decision-making (for example, in smart electric appliances), in more critical problems, the data needs ample processing and analysis (healthcare, activity recognition, etc.). In the latter case, different mathematical model-based machine learning algorithms are utilized to learn hidden patterns or features in acquired data and extracted features. With the use of a trained learning algorithm called a classifier, the new data is then used for decision-making purposes. The choice of such algorithms is often dependent on the type of sensor data being used and the corresponding application area. Thus, it is seen that IoT-based systems find application in various domains, starting with research and development up to industry, agriculture, defense, etc. In reality, the progress of researchers in different domains leads to smart products that, in turn, make human lives easier. Research in several popular ular verticals, such as Human Activity Recognition, Remote Healthcare, Remote Monitoring, Smart Automation, Smart Agriculture, etc., have yielded many such products. This chapter focuses on the deep-seated relationship between IoT and sensors from the perspective of state-of-the-art research. It offers discussions on the usage of various types of sensing devices, associated data, and their contribution towards solving specific research problems in the respective IoT-based applications. This includes the Video Camera, Inertial Measurement Unit (IMU) Sensors, Ultrasonic Sensors, Electrocardiogram (ECG) Sensors, Passive Infra-Red (PIR) Sensors, Electromyogram (EMG) Sensors, and some commonly used sensing devices for Environmental and Agricultural Smart system development. A pertinent case study is also included to demonstrate the role of sensors in the development of IoT-based systems. This study also highlights how little effort it takes to implement an IoT-based data acquisition system. The different popular application areas are discussed thereafter in terms of some broad categories. This is followed by the description of some of the standard metrics used for evaluation and benchmarking the performance of smart sensing systems. The future of sensing devices has been discussed, followed by the pertinent challenges faced by IoT-enabled smart systems in implementation. Finally, the concluding remarks are offered. The chapter aims to serve as a wholesome source of knowledge, and relevant information to researchers and practitioners who wish to indulge in the development of smart IoT enables sensing systems.

Due to the massive development in the field of Internet technology, data communication and its processing make it easy for people to access and interact with various physical devices around the world. It has led to many buzzwords, such as the Internet of Things, cloud computing, data analysis, etc. It is important to understand the relationship between them. Every day, many devices connect to the internet and share a large amount of data that needs to be processed for future reference. Therefore, the cloud concept plays a big role in storing such a large and fast operation. The use of this type of data depends on the personal needs of the end user. Some users’ devices are the sources of their communication. For some, user data is important to understand customer behavior. For some users, how to manage massive data and devices are important. Similarly, few users pay attention to data to improve their goods and services. In this chapter, we focus on the Internet of Things, cloud computing, and data mining, and try to find the connection between them in terms of users, services, and applications. Furthermore, to get an insight into the data, this chapter consists of an introduction, research methodology, and conclusion.

Cloud Computing has evolved as a next-generation technology and used as an integrated technology solution or feature in many evolving computing areas. One of the areas is in the field of Smart Cities, employing the internet of things or everything. A smart city is similar to a next-generation city, where services are being provided to users just like in the cloud computing domain. The focus of using such a system is to provide smart services to users or people living around. In previous works, presented by authors around the world, many solutions in different domains related to smart cities are provided, catering to different needs of users but still, there is scope for better access control and authentication mechanisms for accessing various services provided to users over a cloud-based environment. Smart cities are based on the usage of online computing services provided by various ICT/IOT technologies. It also faces many challenges and security threats, just like services in a cloud computing environment. The work presented here will discuss the concepts of the convergence of IOE and cloud computing in smart cities and the challenges faced in future-generation cities employing IOE. Along with this catering to security requirements in smart cities, this work proposes a security framework focussed on providing secure access control and authentication services delivered over the cloud-based system used in smart cities.

Smart Cities are gaining attention due to the ever-increasing population. The increasing population and expanding urbanization resulted in increased congestion and numerous environmental problems. Due to COVID-19, more efficient urbanization increased the need for smart cities. To address the societal and efficient urban management challenges of these Smart Cities, advanced technologies like Blockchains can play vital roles. Smart Contracts are blockchain-based applications. Smart contracts are used to automate agreements without the involvement of a middleman. Smart contracts are executed by a network of computers once predefined conditions are met. The execution action can be in the form of the release of funds to parties, vehicle registration, sending notifications, and issuing a ticket. The information on the transactions completed is updated on the blockchain. This chapter provides insight into how blockchain technology works for smart contracts, which deliver numerous services in Smart cities ecosystems' in more reliable, data secured, and beneficial for the population in Smart cities. The chapter will contribute to the planning of Smart cities planners, developers, architects, and thinkers for using smart contracts to deliver various services in the smart city’s governance.