## 4 Edge Computing-Based IoT
### Advantages of EC-IoT  
AI-based security countermeasures enhance the protection of both EC-IoT and non-EC-based IoT systems, but their impacts vary significantly due to data processing and architecture differences. In EC-IoT systems, AI-driven security solutions operate at the edge, enabling real-time threat detection and response. This proximity to data generation points ensures the swift identification and elimination of potential attacks, reducing the window of vulnerability. Localized AI models also enhance data privacy by eliminating the need to move sensitive information to centralized servers, thereby lowering the risk of interception and unauthorized access. Conversely, AI-based security measures primarily function within centralized cloud environments in non-EC-based IoT systems. While this allows for deploying more sophisticated and resource-intensive AI models, it introduces higher threat detection and response latencies due to the time required to transmit data to and from the cloud. This centralized approach can also create a single point of failure, where a successful attack on the cloud infrastructure can compromise the entire IoT network. Given these differences, EC-IoT systems offer several advantages. The following are the main advantages that make EC-IoT stand out.  
Reduced latency and improved bandwidth utilization: Edge computing processes data locally at the edge of the network, close to where they are generated. This proximity significantly cuts down the latency involved in sending data to a central server for processing. A primary advantage of this approach is the capability to perform tasks with a reduced latency, lower energy consumption, and more efficient use of network bandwidth [84]. This reduces the bandwidth requirements and alleviates congestion in network traffic, which is particularly important with the increasing number of IoT devices generating vast amounts of data [84].  
Increased reliability and scalability: Edge computing enables devices to operate independently of the cloud, which increases the system's reliability. In scenarios where  
Figure 2: High-level depiction of edge computing combined with IoT.  
connectivity to a central server might be compromised or unavailable, edge devices can continue to function effectively, making decisions based on real-time data. Deploying edge computing devices across different locations is scalable, as it distributes the processing load. It allows for incremental additions without the need for significant infrastructure overhauls, which is ideal for scaling up IoT applications [13].  
Context-aware computing and real-time data processing: Edge computing facilitates real-time data processing by eliminating the delays associated with transmitting data to remote servers, which is vital for applications that depend on instant data analysis and action. It enables edge devices to make decisions locally based on real-time environmental data, resulting in more context-sensitive and responsive computing solutions. This capability is particularly advantageous for sectors like smart city infrastructure and healthcare monitoring systems, where timely and relevant data are crucial [85,86].