AI Blockchain and IoT Suite
Budget: ₹750 – ₹1,250 INR
I am rolling out a trio of inter-linked prototypes that tackle pressing urban and environmental challenges while showcasing cutting-edge AI, blockchain, and IoT engineering.
First, the AI-driven Smart Traffic System must ingest multi-sensor feeds, apply real-time reinforcement learning, and demonstrate measurable gains in a simulation such as SUMO—specifically a double-digit reduction in congestion and wait times across a 20-intersection grid.
Second, the Blockchain Voting System needs an end-to-end verifiable ballot flow. I expect audited Solidity or Substrate contracts, mobile-friendly front-end, zero-knowledge vote secrecy, and a testnet demo with at least fifty dummy voters that passes a public audit script.
Third, the Solar-Powered Smart Watering System will run on a low-power microcontroller (ESP32/Arduino acceptable), read soil and weather data, and autonomously schedule irrigation while managing a small photovoltaic battery budget. A simple web or mobile dashboard should visualise the data.
Alongside these flagships, my lab is experimenting with plant-disease image classifiers, self-driving delivery rovers (ROS2, SLAM, path-planning), and GPT-style tutoring chatbots. Each module will be treated as a separate milestone, yet adhere to the same quality bar.
Deliverables
• Clean, well-documented source code in public or private Git repos
• Training notebooks, inference scripts, and hardware-in-the-loop tests where relevant
• Deployment instructions for Ubuntu 22.04 or the target microcontroller
• A concise technical report comparing baseline vs. prototype performance
Acceptance criteria
– Source compiles and runs without modification on the stated platform
– ML models reproduce stated metrics using provided data
– Voting demo passes independent audit script
– Traffic simulation shows ≥ 15 % average wait-time reduction
If you need data samples, CAD files, or clarifications on preferred libraries (TensorFlow, PyTorch, ROS2, Hardhat, Web3.js), let me know early so I can share the assets and keep the schedule tight.
First, the AI-driven Smart Traffic System must ingest multi-sensor feeds, apply real-time reinforcement learning, and demonstrate measurable gains in a simulation such as SUMO—specifically a double-digit reduction in congestion and wait times across a 20-intersection grid.
Second, the Blockchain Voting System needs an end-to-end verifiable ballot flow. I expect audited Solidity or Substrate contracts, mobile-friendly front-end, zero-knowledge vote secrecy, and a testnet demo with at least fifty dummy voters that passes a public audit script.
Third, the Solar-Powered Smart Watering System will run on a low-power microcontroller (ESP32/Arduino acceptable), read soil and weather data, and autonomously schedule irrigation while managing a small photovoltaic battery budget. A simple web or mobile dashboard should visualise the data.
Alongside these flagships, my lab is experimenting with plant-disease image classifiers, self-driving delivery rovers (ROS2, SLAM, path-planning), and GPT-style tutoring chatbots. Each module will be treated as a separate milestone, yet adhere to the same quality bar.
Deliverables
• Clean, well-documented source code in public or private Git repos
• Training notebooks, inference scripts, and hardware-in-the-loop tests where relevant
• Deployment instructions for Ubuntu 22.04 or the target microcontroller
• A concise technical report comparing baseline vs. prototype performance
Acceptance criteria
– Source compiles and runs without modification on the stated platform
– ML models reproduce stated metrics using provided data
– Voting demo passes independent audit script
– Traffic simulation shows ≥ 15 % average wait-time reduction
If you need data samples, CAD files, or clarifications on preferred libraries (TensorFlow, PyTorch, ROS2, Hardhat, Web3.js), let me know early so I can share the assets and keep the schedule tight.
Related categories:
Wireless
Electronics
Microcontroller
Machine Learning (ML)
Arduino
Blockchain
Solidity
Reinforcement Learning