Over the next few years, smart city technologies will be rolled out, and the IoT devices and AI-centric systems will provide even more far-reaching connectivity. This book presents various concepts in the design and development of a smart city and methodologies and solutions involved in designing contemporary infrastructure for building smart cities around the world. The book will focus mainly on six areas of smart city infrastructures: smart city entities, IoT-based solutions, AI-centric control systems, smart systems, cybersecurity mechanisms, data science, and cloud computing for the deployment of the smart ecosystem. AI-Centric Smart City Ecosystem: Technologies, Design, and Implementation will discuss the role of AI-centric innovative systems and beyond intelligent solutions in the smart city framework. Readers will discover how to apply design principles and technologies for operating intelligent cities and develop an understanding of how to integrate AI-based control systems to make systems smarter. The book will present various concepts in the design and development of smart cities and methodologies and solutions involved in designing modern infrastructure. Also, readers can discover how to develop applications and connect the IoT devices for collecting and mining real-time data and uncover the challenges and techniques for improving the automatic operation in the smart city by using high-tech solutions. This book is intended to serve the needs of the industry, engineers, professionals, researchers, and master’s and doctoral students studying emerging technologies in smart city ecosystems.

Purpose: Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.Design/methodology/approach: In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.Findings: A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.Originality/value: The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

With the pandemic gripping the entire humanity and with uncertainty hovering like a black cloud over all our future sustainability and growth, it became almost apparent that though the development and advancement are at their peak, we are still not ready for the worst. New and better solutions need to be applied so that we will be capable of fighting these conditions. One such prospect is delivery, where everything has to be changed, and each parcel, which was passed people to people, department to department, has to be made contactless throughout with as little error as possible. Thus, the prospect of drone delivery and its importance came around with optimization of the existing system for making it useful in the prospects of delivery of important items like medicines, vaccines, etc. These modular AI-guided drones are faster, efficient, less expensive, and less power-consuming than the actual delivery.

The increase in the use of Internet of Things, otherwise IoT, has impacted numerous industries such as health, facility, and manufacturing. Although connections have become more sophisticated, security and privacy risks have also emerged through connected devices. This research focuses on the security risks posed by IoT devices and seeks to recommend measures to protect these IoT networks. Based on the analysis of the state-of-the-art, the areas that should be further secured are defined, including energy-aware security mechanisms, privacy-preserving protocols, and edge computing vulnerabilities. The results indicate that 78% of IoT devices used in healthcare facilities can be compromised to release private info to unauthorized personnel; 65% of smart building systems are not secure enough when it comes to encryption. Furthermore, the research showed that by implementing artificial intelligence, organizations can decrease security threats by as much as 42 percent in IoT settings. The study then calls for a layered security solution that incorporates energy management, data protection, and the development of a comprehensive security solution for the IoT systems. Further work should be directed to the research of the flexible safety frameworks, which would allow handling new threats in terms of secure functioning of IoT networks.