## 4 Edge Computing-Based IoT  
EC-IoT involves using edge computing paradigms such as fog computing, multi-access edge computing, and cloudlet to manage security-critical and time-sensitive data generated by IoT devices [19]. This strategy consists of deploying computing resources in close proximity to IoT devices for tasks such as data filtering, preprocessing, and aggregation.  
The process is mainly conducted by designing the IoT layer sitting at the bottom of the architecture, while the resource providers are at the network edge. Edge computing optimizes IoT data processing [79]. In this architecture, IoT devices collect data from their environment, which is then processed locally on edge devices instead of being sent directly to distant cloud servers. This local processing capability allows for real-time data analysis and decision-making, which is critical in applications requiring immediate action such as autonomous driving, real-time health monitoring, and smart city infrastructure [80].  
Similarly, edge computing-based IoT offers enhanced security features by limiting the amount of sensitive data transmitted over the network, thus reducing exposure to potential cyberthreats. It also supports more scalable deployments by distributing processing tasks across numerous edge devices, alleviating the load on central servers and reducing network congestion. One example would be facilitating vehicles' quick and accurate localization through IoT sensors and radar systems [81]. EC-IoT has a wide range of applications. While hidden in plain sight, these applications exist in extensive healthcare and urban applications, ensuring timely and accurate data processing [82] of these applications. In urban planning, the optimization of ecological landscape structures through EC-IoT has positively impacted biodiversity indices, highlighting the effectiveness of edge computing in diverse applications [83].  
Figure 2 depicts the high-level three-layer architecture of the EC-IoT paradigm. The basic composition is the same as the conventional edge computing structure, with IoT devices being a part of the end-user subset of the edge computing layer. On the other hand, the edge computing layer does not exist for the standard IoT architecture. The things layer forms the foundation and comprises physical devices and sensors that collect environmental data. These can range from simple embedded systems to advanced industrial machinery, generating crucial operational data. Directly above this, the edge layer includes edge nodes and gateways that perform preliminary data processing tasks such as filtering, aggregation, and local analysis. This layer plays a pivotal role in reducing latency by handling dataclose to their origin, thus enabling quick local responses and minimizing the volume of data transmitted upwards. At the top, the cloud layer handles more complex processing tasks and storage needs that are less time-sensitive but require substantial computational resources. It manages advanced analytics, machine learning operations, and extensive data storage, providing centralized control over applications distributed across numerous edge devices. This structured approach ensures that each layer optimizes the processing and utility of the data in EC-IoT systems, enhancing overall efficiency and scalability.