Edge Computing & IoT Application Development is an advanced, hands-on course designed to equip learners with the knowledge and practical skills needed to build distributed applications that operate at the edge of the network while interfacing seamlessly with the Internet of Things (IoT). The course begins with an overview of the evolution of computing paradigms—from centralized cloud systems to edge and fog computing—and highlights the limitations of latency, bandwidth, reliability, and privacy in traditional cloud-based models. Learners explore the core components of an IoT architecture including sensors, actuators, edge devices, gateways, communication protocols, and cloud platforms. The course introduces microcontrollers like Arduino and single-board computers like Raspberry Pi, teaching learners how to program these devices using languages such as Python and C/C++. Popular edge operating systems like Ubuntu Core, BalenaOS, and EdgeX Foundry are explored along with containerized application deployment using Docker. Communication protocols such as MQTT, CoAP, and HTTP are examined for lightweight, secure, and real-time data transmission. Learners develop custom IoT applications that collect sensor data (e.g., temperature, motion, light) and perform localized processing and decision-making before syncing with cloud systems such as AWS IoT Core, Azure IoT Hub, and Google Cloud IoT. Real-world projects include predictive maintenance for industrial machines, smart home automation, connected health monitoring devices, and intelligent agriculture solutions. The course covers edge analytics using tools like TensorFlow Lite, OpenVINO, and NVIDIA Jetson for performing inference and ML tasks directly on edge devices without cloud dependency. Learners deploy anomaly detection models, image classification tasks, and voice recognition systems at the edge, exploring performance trade-offs and power consumption optimization. Topics in edge orchestration are introduced using tools like KubeEdge, AWS Greengrass, and Azure IoT Edge, allowing for remote management, provisioning, and updates of fleets of edge devices. Security is a critical focus, covering hardware-based root-of-trust, secure boot, data encryption, key rotation, over-the-air updates (OTA), and device authentication. Learners integrate identity management, certificate provisioning, and role-based access control for end-to-end security across edge-to-cloud pipelines. The course also explores network management, including LPWAN, 5G, LoRaWAN, and NB-IoT, analyzing trade-offs between range, throughput, and energy efficiency. Students build dashboards and visualizations using platforms like Grafana, Node-RED, or custom-built web applications to monitor edge devices, sensor metrics, and alerts in real time. Event-driven architecture and edge-native serverless functions are demonstrated to optimize responses to local changes, using lightweight frameworks such as FaaS and OpenFaaS. By the end of the course, learners will have designed and deployed full-stack IoT applications with real-time edge computing capabilities, addressed challenges of intermittent connectivity and low-latency processing, and understood the architectural patterns that power intelligent distributed systems. This course is ideal for embedded developers, IoT engineers, system architects, and cloud practitioners seeking to develop next-generation applications that bring intelligence closer to where data is generated, reducing cost, improving responsiveness, and enabling new use cases in manufacturing, transportation, energy, healthcare, and smart cities.