Foreword

This book provides a comprehensive overview of both Generative Artificial Intelligence (AI) and Wireless Sensor Networks (WSNs), describing the features, capabilities, and uses of each technology. It looks at how generative AI could enhance data analysis, anomaly detection, predictive modeling, and WSN optimization. Additionally, it focusses on the security risks and weaknesses brought about by the usage of generative AI in wireless sensor networks. It addresses practical examples that demonstrated the implementation of generative AI and wireless sensor networks.

In this book, Chapter 1 gives an overview of GAI, along with its gradual evolution and various application areas. This chapter specially discusses its application in the field of programming. Programming jobs have the potential to revolutionise software development procedures. The advantages and drawbacks of using GAI to automate code generation, intelligent debugging support, Natural Language Processing (NLP) interfaces, code completion, and documentation generation are examined in this study. This study attempts to offer insights into how GAI might be used to enhance programming techniques and increase the performance and reliability of WSNs through a thorough analysis. Chapter 2 presents the concept of automated agriculture. The authors have provided information regarding the Advanced Crop Management System, how it has empowered farmers to the extent they are today and and how can they perceive events occurring in the future. In this chapter, a comprehensive study of methods for agriculture enhancement from machine learning to deep learning is provided. Chapter 3 discusses digital technologies to enhance the quality of traveling for commuters by integrating wireless sensors in IoT and AI. It gives an idea about how AI Algorithms form a vital part in most effectively handling traffic flow by collecting and integrating data from various sources such as Traffic Sensors, GPS Devices, Weather forecasts, and historical traffic patterns. The further study presented in Chapter 4 discusses the Generative AI-based methods to handle challenges in WSN-based data dissemination systems. The book also provides a study in chapter 5 that examines how big data analytics and Artificial Intelligence (AI) might progress the industrial supply chain's sustainability as manufacturing supply chains are realizing the necessity for eco-friendly operations due to rising healthcare costs and environmental concerns. Keeping this in consideration an Automatic Number Plate Recognition System in Real-time (ANPRS) is an essential component of modern vehicle surveillance and management. In Chapter 6, a Generative AI-based novel framework for real-time Automatic Number Plate Detection is proposed and implemented. Also, a framework for optimizing energy conversation based on Meta-heuristic Algorithms is discussed in Chapter 7. A study in Chapter 8 is presented to investigate the possible advantages and security concerns of incorporating generative AI into communication systems by examining the overlap between GAI and communication engineering. At last, Chapter 9 of this book discusses and explores different generative AI models and their recent trends present in communication engineering in the spectrum of network optimization, resource allocation, etc. The potential applications of these models are also discussed, along with security concerns.

This book showcases effective deployments, lessons learned, and best practices. By meeting the stated objective and dimension, this book promises to be a helpful resource for academics, practitioners, and students interested in understanding the potential and security risks related to the integration of wireless sensor networks and generative AI.