Edge Computing Projects with Source Code
Edge computing brings computation and storage closer to data sources to reduce latency, bandwidth use and dependence on the cloud. It powers IoT gateways, on-device AI, real-time video analytics and industrial control systems.
This page lists 60+ high-impact edge computing topics aligned with IoT, embedded and cloud curricula. Tools include AWS IoT Greengrass, Azure IoT Edge, K3s, Docker, TensorFlow Lite, OpenVINO, NVIDIA Jetson, Raspberry Pi and MQTT. Ideal for BE, BTech, MTech and research students in Bangalore and across India.
Core Frameworks & Tools
Platforms and runtimes commonly used in academic and industrial edge computing projects.
AWS Greengrass
Azure IoT Edge
K3s / Docker
TensorFlow Lite
OpenVINO
Jetson / RPi
Best Edge Computing Project Topics & Tools (60+)
Grouped by application theme. Each topic lists primary tools and platforms.
| # | Project Topic | Primary Tools / Platforms |
|---|---|---|
| 🧠 Edge AI · On-Device Inference | ||
| 1 | AIImage Classification on Edge with TensorFlow Lite | TFL, RPi / Jetson, camera |
| 2 | AIObject Detection (YOLO / SSD) on Jetson Nano | Jetson, OpenCV, TensorRT |
| 3 | AIOpenVINO Optimized Inference on Intel NUC / RPi | OpenVINO, ONNX models |
| 4 | AIKeyword Spotting / Audio ML at the Edge | TFL Micro, ESP32 / RPi |
| 5 | AIAnomaly Detection Model Deployed on Gateway | Edge Impulse / TFL, MQTT |
| 6 | AIModel Quantization and Latency Benchmark on Edge | TFL, int8, power meter |
| 7 | AIMulti-Model Pipeline (detect → classify) on Device | Jetson, TensorRT / TFL |
| 8 | AIFederated Learning Lite Demo (edge clients + server) | Flower / custom, RPi nodes |
| 🚪 IoT Edge Gateways · Protocol Bridging | ||
| 9 | GWMQTT Gateway with Local Rules Engine | Mosquitto, Node-RED, RPi |
| 10 | GWProtocol Bridge: Modbus / BACnet → MQTT | Node-RED, industrial PLC sim |
| 11 | GWAWS IoT Greengrass Device Shadow + Local Lambda | Greengrass v2, Python |
| 12 | GWAzure IoT Edge Module with Offline Buffer | Azure IoT Edge, Docker |
| 13 | GWStore-and-Forward Gateway for Intermittent Connectivity | SQLite, MQTT, RPi |
| 14 | GWMulti-Protocol Edge Aggregator (BLE + Wi-Fi + LoRa) | ESP32 / RPi, MQTT broker |
| 15 | GWSecure Gateway with TLS and Device Identity | Mosquitto TLS, certs, Greengrass |
| 📹 Edge Video Analytics | ||
| 16 | VidReal-Time Person Counting / Occupancy on Edge | Jetson / RPi, OpenCV, YOLO |
| 17 | VidIntrusion / Zone Violation Detection | Jetson, DeepStream / OpenCV |
| 18 | VidTraffic Density Estimation from Camera Feed | RPi/Jetson, object tracking |
| 19 | VidFace Blurring / Privacy Filter at the Edge | OpenCV, face detect, RTSP |
| 20 | VidPPE (Helmet / Vest) Compliance Detection | Jetson, custom YOLO model |
| 21 | VidMulti-Camera Edge Aggregation and Selective Upload | DeepStream, MQTT / HTTP |
| 22 | VidLow-Bandwidth Event-Triggered Video Upload | Motion detect, RTSP, S3/Azure |
| 📦 Containers · K3s · Edge Orchestration | ||
| 23 | K8sK3s Cluster on Raspberry Pi Nodes | K3s, Helm, RPi cluster |
| 24 | K8sDeploy ML Inference Service as Container on Edge | Docker, K3s, TFL serving |
| 25 | K8sEdge GitOps with Flux / Argo on K3s | K3s, FluxCD, Git repo |
| 26 | K8sResource-Constrained Scheduling Study on K3s | K3s, resource limits, metrics |
| 27 | K8sOffline Container Registry and Air-Gapped Deploy | Harbor / local registry, K3s |
| 28 | K8sSidecar Pattern for Edge Logging and Metrics | Docker, Prometheus, Loki |
| ☁️ Fog Computing · MEC · Hierarchical Edge | ||
| 29 | FogThree-Tier Architecture: Device → Fog → Cloud | MQTT, RPi fog node, cloud |
| 30 | FogLatency Comparison: Edge vs Fog vs Cloud Inference | TFL local, remote API, timing |
| 31 | FogTask Offloading Decision Engine (edge vs cloud) | Python policy, load metrics |
| 32 | FogMEC-Style Cache and Compute Node for CDN-like Use | Nginx, Redis, RPi/NUC |
| 33 | FogHierarchical Aggregation of Sensor Streams | MQTT, time-series DB at fog |
| 🏭 Industrial · Smart City · Domain Edge Apps | ||
| 34 | IoTPredictive Maintenance Edge Node (vibration ML) | IMU, TFL, MQTT dashboard |
| 35 | IoTSmart Agriculture Edge Station (soil + weather + ML) | Sensors, RPi, LoRa / Wi-Fi |
| 36 | IoTSmart Building HVAC Control with Local Policy | Node-RED, sensors, actuators |
| 37 | IoTParking Occupancy Edge System with Local Display | Ultrasonic/camera, RPi, display |
| 38 | IoTWater Quality / Leak Detection Edge Monitor | Sensors, anomaly model, alerts |
| 39 | IoTRetail Shelf / Queue Analytics at the Edge | Camera, Jetson, occupancy model |
| 40 | IoTFactory Line Defect Detection (edge vision) | Camera, YOLO, PLC interface |
| 41 | IoTEnergy Meter Edge Aggregator with Load Forecasting | Modbus energy meters, TFL |
| 🏥 Healthcare · Education · Security Edge | ||
| 42 | DomainPatient Fall / Activity Edge Wearable Gateway | IMU BLE, gateway ML, alert |
| 43 | DomainClassroom Attention / Noise Edge Monitor | Mic, RPi, local classifier |
| 44 | DomainAccess Control with Edge Face / Card + Policy | Camera/RFID, local decision |
| 45 | DomainDrone / Robot Local Navigation Assist on Edge | Jetson, vision SLAM lite |
| 46 | DomainEnvironmental Compliance Edge Logger (noise/air) | Sensors, local storage, uplink |
| 🔬 Advanced · Security · Research-Oriented | ||
| 47 | AdvEdge Device Fleet Management and OTA Updates | Greengrass / balena / Mender |
| 48 | AdvZero-Trust Edge Access with Mutual TLS | Certs, SPIFFE-style identity |
| 49 | AdvDifferential Privacy / On-Device Data Minimization | Local aggregation, noise |
| 50 | AdvEdge–Cloud Continuum Simulator / Cost Model | Python sim, latency/bandwidth |
| 51 | AdvHardware Accelerator Comparison (CPU vs NPU vs GPU) | Coral / Jetson / OpenVINO |
| 52 | AdvEnergy-Aware Scheduling of Edge Workloads | Power sensors, scheduler policy |
| 53 | AdvResilient Edge App under Network Partition | Local DB, eventual sync |
| 54 | AdvMulti-Tenant Isolation on Shared Edge Node | Docker/K3s namespaces, quotas |
| 55 | AdvObservability Stack for Edge (metrics + traces) | Prometheus, Grafana, OpenTelemetry |
| 56 | AdvDigital Twin Lite: Edge Sensor → Local Digital Model | MQTT, local state machine |
| 57 | Adv5G / MEC Integration Demo (emulated or lab) | MEC concepts, latency study |
| 58 | AdvSecure Enclave / TEE for Sensitive Edge Inference | OP-TEE / platform TEE study |
| 59 | AdvContinuous Deployment Pipeline for Edge Models | CI/CD, model registry, OTA |
| 60 | AdvEnd-to-End Edge Product: Sense → Infer → Act → Cloud Sync | Full stack: sensors, TFL, MQTT, dashboard |
| 61 | AdvComparative Study: Greengrass vs Azure IoT Edge vs K3s | Same workload, metrics table |
| 62 | AdvBandwidth Savings Analysis: Edge Filtering vs Raw Upload | Traffic capture, before/after |
Topics reflect common IoT, industrial and cloud-edge practice. Contact us for architecture notes, deployment scripts, model setup, evaluation metrics, university-format report, PPT and viva Q&A for any topic above.
Why Choose Us for Edge Computing Projects?
Bangalore-based guidance for BE, BTech and MTech students working on edge AI, gateways and fog architectures.
Edge AI
On-device inference with TensorFlow Lite, OpenVINO and Jetson — quantization, latency and accuracy trade-offs.
Gateways & Protocols
MQTT bridges, Greengrass/Azure IoT Edge modules, store-and-forward and multi-protocol aggregation.
Video Analytics
Person counting, intrusion, PPE detection and event-triggered upload on Jetson and Raspberry Pi.
K3s & Fog
Lightweight Kubernetes at the edge, hierarchical fog pipelines and edge–cloud continuum experiments.
Frequently Asked Questions — Edge Computing Projects
Top topics include edge AI inference with TFL/OpenVINO, IoT gateways with MQTT and local rules, real-time video analytics on Jetson/RPi, K3s clusters, fog hierarchical pipelines, offline-first apps and predictive maintenance at the edge.
AWS IoT Greengrass, Azure IoT Edge, K3s, Docker, MQTT (Mosquitto), TensorFlow Lite, OpenVINO, NVIDIA Jetson, Raspberry Pi, Node-RED, Prometheus/Grafana.
Yes. Packages include architecture notes, deployment scripts, model/runtime setup, evaluation metrics, demo guidance, university-format report, PPT and viva Q&A.
Edge processes data near the source for low latency. Fog is an intermediate layer between edge and cloud. Cloud is centralized, high-capacity processing with higher latency and bandwidth use.
Edge Computing Lab — Bangalore
Gateways, Jetson/RPi nodes, cameras and cloud edge platforms for BE, BTech and MTech projects.
Edge AI
Inference Lab
TFL / OpenVINOInference Lab
MQTT Gateway
& Node-RED
Protocol Bridge& Node-RED
Jetson Video
Analytics
YOLO / DeepStreamAnalytics
K3s Cluster
on RPi
Containerson RPi
Greengrass /
Azure IoT Edge
Cloud EdgeAzure IoT Edge
Fog Hierarchy
Experiments
Device→Fog→CloudExperiments
Observability
Stack
Prometheus / GrafanaStack
Viva & Report
Preparation
DocumentationPreparation