IoT approach for smart cities based on edge computing

Edge computing has emerged as a transformative technology that processes data close to its source,
rather than relying on remote centralized data centers or the cloud. This approach significantly reduces
latency, enhances processing speed, and enables real-time decision-making, making it ideal for meeting
the dynamic needs of smart cities.
In urban environments, edge computing plays a critical role by supporting various applications such as
smart traffic management, energy consumption monitoring, and public safety through smart cameras
and sensors. Edge computing contributes to creating more efficient, responsive, and sustainable urban
systems in real time, by enabling faster data analysis and reducing the burden on central networks, thus
contributing to the development of smart cities. In this thesis, we use edge computing techniques and
methods to support the development and operation of smart cities in various applications including
supporting the work of self-driving cars, healthcare applications, and supporting data security in edge
computing units. This integration between edge computing and smart cities allows data to be collected
and analyzed close to the processing unit, allowing applications to make decisions in real time and
improve their performance