## 7 Conclusions  
Communication technologies are essential to ensure reliable and continuous connectivity in smart cities. However, the proliferation of smart devices can present difficulties. Therefore, current research explores the potential of communication technologies enabled by the Internet of Things (IoT) and their possible applications in smart cities. Wi-Fi, ZigBee, and Z-Wave are suitable for short distances where devices and their coverage areas are limited, as they offer higher data throughput rates than long-distance technologies such as LoRaWAN. However, the choice of communication technology depends on the specific requirements of each application and the range of coverage needed. ZigBee is commonly used for low-power applications with a low data rate, providing a secure network and longer battery life. It offers network topologies suitable for various applications, such as mesh, star, and tree. On the other hand, some long-range technologies require devices to be connected through a central gateway to collect information and sink the data. These technologies include LoRaWAN, NB-IoT, Sigfox, and LTE, and they operate on licensed and unlicensed spectrums, depending on the applications. When choosing a communication technology, not only the low-power spectrum is essential, but scalability, network capacity, security, and regularity must also be considered. Cellular networks can be a valuable resource for deploying new applications in a smart city. Narrowband IoT (NB-IoT) is a technology that focuses on low-cost devices while ensuring high network security and reliability. It also takes advantage of existing cellular networks, which can reduce the cost of installing a network for new smart city applications. On the other hand, LoRaWAN and Sigfox are unlicensed spectrum technologies that can help overcome the cost issues associated with licensed technologies. Depending on the application and region, these technologies typically use ISM bands, including 2.4 GHz, 868 MHz, 915 MHz, and 433 MHz. Sigfox is particularly useful for low-data applications  
Figure 9: Illustration of the combination of IoT, 5G and AI, which would be the integral components of a futuristic smart city. such as smart environments, smart agriculture, and smart retailing. It claims to connect a million devices within a large coverage area. Utilizing several AI algorithms in smart cities can enhance efficiency, sustainability, and quality of life. The algorithms discussed include ML, DL, NLP, CV, and RL. ML algorithms enable machines to identify patterns, learn from data, and make predictions based on input. On the other hand, DL models utilize artificial neural networks to understand hierarchical representations of data, allowing them to detect complex features and patterns. NLP algorithms enable computers to comprehend, interpret, and create human language, while CV algorithms enable machines to examine and understand visual data, recognize image patterns and features, and leverage this information to make informed decisions and predictions. RL is a machine learning algorithm in which an agent interacts with an environment through trial and error to maximize a cumulative reward. These algorithms can be used in various applications in smart cities, such as traffic management, energy optimization, waste management, and public safety. They can make smart cities more efficient, sustainable, and safer for residents. The future of the smart city is going to depend on the development of every sector, such as a smart environment, smart agriculture, smart economy, smart business, and smart governance. The ability to incorporate the latest 5G communication technology, IoT devices, and AI algorithms into a city's infrastructure is going to play a crucial role in the success of a smart city. Integrating these technologies can harmonize to produce a more efficient, sustainable, and livable city. Combining all the sectors can help build a smart city; this review explains the requirements.