Edge Computing is a distributed computing paradigm that processes data close to the source of generation, such as sensors or devices, instead of relying solely on centralized cloud servers. In IoT, edge computing involves deploying software on local nodes (e.g., gateways, microcontrollers) to perform tasks like filtering, aggregation, or real-time analytics before data is sent to the cloud. Implementing edge computing requires using frameworks like AWS Greengrass, Azure IoT Edge, or open-source tools like EdgeX Foundry, which provide APIs for data ingestion, containerized application deployment, and local decision-making. Software engineering at the edge often focuses on optimizing resource usage, including memory and CPU, while maintaining real-time constraints. Security is a key concern, with the need for secure boot, encrypted data storage, and secure communication between edge nodes and the cloud. Developers can build microservices at the edge for different functions, such as protocol translation (e.g., Modbus to MQTT), local data storage, or AI-based anomaly detection. Implementing edge orchestration software to manage containerized workloads across multiple nodes can significantly enhance the scalability and resilience of edge computing systems.