###### Abstract
* Smart cities are becoming more and more of a reality, thanks to the enormous technological research enabling the development of IoT, which offers a wide range of applications around different types of sensors. More sustainable, environmentally friendly, and economical smart cities and technologies are needed to cope with the growing population in cities. For smart-city management, numerous sensors, cameras, and actuators are installed everywhere. These sensors collect and send a bulk of data in actual time. The analysis and processing of the collected data should be almost instantaneous for efficient management of city operations. Additionally, for instant processing, high-speed internet connectivity is essential. With the advent of smart-city devices, internet-connected devices will transmit large amounts of data in real time. While this data contributes to the efficiency of city functions, it also poses serious security risks that cannot be ignored. Data from parking lots, security cameras, electric vehicle charging stations, and GPS systems contain citizens' confidential information. Not every networked device is already cyber-resistant. If it is, criminals can easily access the data and use it for illegal purposes. Therefore, governments and IT professionals should strengthen the security perimeters of smart devices and supporting infrastructure. Identifying and solving smart-city challenges is a collaborative approach. Governments and IT professionals, private organizations, and citizens should join together to work for a common goal--the success of the smart city
* Smart farming. With the world's population growing and climatic changes resulting in unpredictable weather in the world's food chains, the race for the sustainability of farming and the efficient use of dwindling resources such as water is a global challenge for countries worldwide. Smart farming uses sensors implanted in plants and fields to take measurements that assist make decisions and plant protection. Precision farming is an essential part of the smart-farming paradigm, in which sensors are implanted in plants to take a certain measure to allow targeted care measurement. Precision farming will be needed to ensure food protection in the future; therefore, it is essential for farming sustainability. The primary use cases of AI in IoT for farming are plant health and disease identification and data-based crop protection. In the computer science and symmetry domain [11], IoT can support various applications and services in different fields such as healthcare, home automation, security, and vehicles. The massive increase in data generated by vehicular networks makes vehicular communication vulnerable to attacks such as anomalies or outliers. These are critical factors that affect traffic flow and network security and impact the global economy. Robustness to outliers is a critical issue in modern machine-learning systems for many practical reasons, such as adversarial attacks, data corruption, etc. AI is a field of computer science that deals with developing intelligent machines, and complex and novel communication systems to maximize the chances of success. Nowadays, AI is a critical component to realize a vast amount of data collected by IoT devices. AI helps in analyzing the wireless network and data volume for data processing, detection, flow, accuracy, and timing reliability. Robust intelligent techniques will be needed for adapting the network and managing resources of 5G for several facilities in multiple schemes. The ML is ideal for working on a 5G network because it requires a lot of data to make predictions and helps ensure that the 5G network can transmit a large amount of information. One of the best ML methods used for simulating nonlinear optimization functions is Deep Learning (DL). Recent advances in DL and ML techniques are promising for solving previously intractable and challenging problems.