Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.
Uncertain nonlinear systems can be modeled with fuzzy differential equations (FDEs) and the solutions of these equations are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this book chapter, the solutions of FDEs are approximated by utilizing the fuzzy Sumudu transform (FST) method. Here, the uncertainties are in the sense of fuzzy numbers and Z-numbers. Important theorems are laid down to illustrate the properties of FST. This new technique is compared with Average Euler method and Max-Min Euler method. The theoretical analysis and simulation results show that the FST method is effective in estimating the solutions of FDEs.
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