Sustainable AI and Green IoT for Climate-Resilient Smart Cities

Context
The rapid urbanization of modern societies requires climate-resilient smart city infrastructures. Artificial Intelligence (AI) and Internet of Things (IoT) offer pathways to improve energy efficiency, mobility, and environmental monitoring, yet sustainability and carbon footprints remain underexplored. This project examines how “green” IoT architectures, combined with AI-driven analytics, can reduce energy use and strengthen urban resilience against climate change.
Research Objectives

• Green IoT Architectures: Design low-power, scalable IoT simulation frameworks optimized for urban data collection.
• AI for Energy Efficiency: Implement small language models to forecast energy demand and optimize resource allocation.
• Blockchain & Data Integrity: Explore federated learning for secure, transparent data-sharing across energy communities.
• Policy Integration: Assess how these technologies can align with global sustainability frameworks (e.g., SDGs, EU Green Deal).

Programs & Tools Students Can Use

• Sustainable AI Frameworks: Energy-efficient model design using TinyML, TensorFlow Lite, ONNX Runtime.
• Simulation & Modelling: MATLAB/Simulink, AnyLogic, or SUMO (Simulation of Urban Mobility) for smart city scenarios.
• Blockchain Tools: Hyperledger Fabric, Ethereum test networks, and Truffle Suite for decentralized energy trading.
• Environmental Data Sources: Open urban climate datasets, APIs (e.g., Copernicus Climate Data Store).

Expected Outcomes

• Prototype of a sustainable AI-IoT benchmark framework for city-scale deployment.
• Simulation-based case study of energy optimization in a modelled smart district.
• A joint academic–policy paper on sustainable digital transformation for cities.

Student Gains
Students will develop expertise in IoT systems, sustainable computing, blockchain applications, and environmental analytics. They will also practice interdisciplinary problem-solving, combining engineering with sustainability and policy perspectives. Students will also practice end-to-end prototyping: deploying IoT nodes, implementing lightweight AI, simulating smart city energy flows, and proposing policy-linked recommendations.
Requirement
Experience Python/Matlab simulation tools is mandatory. Knowledge of energy-efficient AI (TinyML, TensorFlow Lite), or exposure to blockchain-based systems will be considered a strong advantage.
Related Areas
[Computer Science and Engineering, Electronics and Industrial Engineering, Environmental and Sustainability Studies, Blockchain and IoT Systems, Business Analytics and Policy]
Application Process (Final Note):
You should prepare:
Latest Academic Transcript (official or system-generated PDF from your student portal)
1-page Letter of Interest explaining:

• why you are interested in the project,
• any prior experience (coursework, projects, internships),
• how you want to contribute (e.g., technical development, policy analysis, data science).

After you complete your online application, email the documents above to polat.goktas@sabanciuniv.edu with the chosen Project Name in the subject line.