# System Architecture Design of IoT-Based Smart Cities
## 8 Related Work  
In our earlier work [17; 18; 19], we discussed the design of a reference architecture for IoT-based smart farming. Although smart farming and smart cities are based on IoT, it is important to analyze the domain thoroughly since the requirements differ for both domains. On the other hand, we could use the reference architecture for IoT systems for both domains. In recent years, there has been an increasing interest in developing smart cities, which use advanced technology, data analytics, and the Internet of Things (IoT) to optimize the efficiency of city operations and services. As a result, several studies have been conducted to address the challenges and opportunities associated with smart cities. Several studies have provided secondary studies, including literature reviews on IoT, for example, [12; 13; 20; 21; 22]. In addition, a specific topic on IoT has been security issues discussed in several literature reviews, such as in [23; 24]. Some researchers have proposed architectures and frameworks for designing and developing IoT-based smart cities. For example, in [1], the authors propose a framework for integrating IoT devices into smart cities, focusing on using sensors and actuators to collect and process data. Similarly, in [25; 26], the authors propose an architecture for integrating IoT devices in smart cities, focusing on cloud computing and big data analytics. Another important aspect of smart cities is the use of data and analytics to make informed decisions and improve the city's overall performance. Several studies have been conducted in this area, including [27], which focuses on using data analytics to improve the efficiency of transportation systems in smart cities. Similarly, in [28], the authors propose a framework for using data analytics in smart cities, focusing on machine learning and artificial intelligence techniques. Finally, another study has focused on big data analytics for smart cities [29]. In addition to these specific studies, there have also been some general surveys and reviews of the literature on smart cities. For example, in [30], the authors provide an overview of the various challenges and opportunities associated with the development of smart cities, and in [1,2,31], the authors review the literature on the use of IoT in smart cities. Several studies have focused on specific applications of smart cities using IoT. In [32], the authors discuss the optimization problem of offloading decisions, CPU frequencies, and transmit powers for IoT devices in a multi-mobile edge computing (MEC) server and multi-IoT device cellular network, aimed at minimizing computing pressure on the primary MEC server, energy consumption of the network, and task dropping cost. The problem is formulated as a mixed integer nonlinear program (MINLP) problem, which is difficult to solve due to strongly coupled constraints and discrete integer variables. To solve this problem, a deep reinforcement learning (DRL)-based optimization algorithm is proposed, considering the dynamic nature of the environment. The simulation results show the effectiveness of the proposed algorithm. In [33] the implementation of a model a smart city with six elements is discussed, namely a smart garbage system, a smart irrigation system, a smart building, a smart parking system, a restaurant menu ordering system, and a manhole detection and monitoring system. The project aims to make advancements in these elements by enabling technologies such as Internet of Things (IoT), embedded systems, and networking. The goal is to make all these systems automatic without any human involvement, thus making life easier. The paper highlights the importance of IoT in combining various sensors with embedded devices to create solutions for real-time problems and connect with the internet society. Study [34] addresses the gap in the literature that focuses on the four sides of the problem in the automation strategy of smart cities, which includes video surveillance, algorithms, datasets, and embedded systems for edge vision computing.