Join students in Tamil Nadu's premier hackathon and unlock your potential
Enhance your technical skills, learn new technologies and work on real-world problems that matter.
Meet like-minded peers, industry experts and potential collaborators. Build lasting relationships.
Compete for exciting prizes, recognition and opportunities. Showcase your innovation to industry leaders.
Contribute to solving real-world challenges aligned with Sustainable Development Goals.
Choose from our curated problem statements focusing on cybersecurity, sustainability, and smart city solutions. Each problem offers unique challenges and opportunities for innovation.
With the growing use of social media and messaging platforms, cyberbullying among teenagers and students has become a major psychological and social issue. Victims often hesitate to report incidents due to fear or lack of awareness, leading to mental distress and social withdrawal. Existing moderation systems are platform-specific and fail to protect users across multiple apps or private chats.
To develop an AI-powered mobile application that continuously monitors user messages, social media interactions, and public comments for offensive, threatening, or abusive content. Using natural language processing (NLP) and sentiment analysis, the app should detect harmful patterns and issue real-time alerts or guidance. It should also provide reporting options, mental health resources, and parental dashboards (optional for minors).
A real-time, privacy-aware mobile safety companion that automatically identifies and flags cyberbullying incidents, promotes digital well-being, and supports early intervention. The project aims to reduce online harassment rates and increase awareness among youth and parents.
Citizens often face cyber issues like fake profiles, online scams, identity theft, and harassment but are unaware of proper reporting channels or standard operating procedures (SOPs). Information on government and cyber cell websites is often fragmented and hard to navigate.
To design an LLM-powered assistant (web/app/chatbot) trained on official government guidelines, CERT-In advisories, and cybercrime SOPs. The model should understand natural queries (e.g., "Someone made a fake account of me" or "How to report online money fraud?") and instantly provide step-by-step instructions, relevant official links, and reporting forms.
An interactive, multilingual knowledge assistant that empowers users with accurate, immediate, and verified responses to cybercrime-related queries — reducing dependency on manual support and increasing the speed of citizen response in cyber incidents.
In emergencies, women and elderly individuals may not have time to contact help or share their location. Current safety apps often require manual actions or fail when there's no internet connectivity.
To create a lightweight safety mobile app that supports one-touch SOS alerts, geo-fencing, and real-time location sharing. The system automatically detects if a user exits a predefined safe zone or triggers panic detection (e.g., sudden acceleration, loud noise). The app should send alerts via SMS, WhatsApp, or calls to pre-registered contacts and local authorities even in low-network conditions.
A reliable personal safety system offering proactive protection and rapid response. The app enhances security for vulnerable groups, fosters community safety awareness, and can be integrated with local police helplines or smart city emergency frameworks.
Most users lack awareness about how their data is tracked, stored, or shared online. As a result, they unknowingly give consent to unsafe cookies, phishing links, and malicious apps, exposing themselves to data theft and privacy violations.
To develop an AI-based educational chatbot that interacts with users through engaging conversations, quizzes, and examples to raise awareness about data privacy, cookies, phishing, social engineering, and safe browsing practices. The chatbot should personalize advice based on user behavior (e.g., "You often use public Wi-Fi, here's how to stay safe").
An interactive and gamified chatbot platform that makes cybersecurity education accessible and enjoyable. It aims to improve user digital literacy, reduce privacy-related incidents, and build a culture of safe digital behavior among the general public and students.
IoT devices like smart cameras, wearables, and home assistants often have weak security configurations. Many users are unaware that their devices are vulnerable to attacks or misconfigured, exposing their networks to exploitation.
To build a network-scanning and vulnerability-assessment tool that detects IoT devices connected to a local Wi-Fi network, identifies their make/model, and checks for known vulnerabilities (CVE database) or default credentials. The tool should provide a security score and recommendations for securing each device (e.g., firmware updates, port closures).
A comprehensive IoT vulnerability scanner that empowers users and enterprises to detect, analyze, and mitigate IoT security risks. This leads to stronger endpoint security, reduced attack surfaces, and greater visibility in smart home or industrial IoT environments.
India is the third-largest producer of electronic waste, yet over 90% of e-waste is handled by the unorganized sector with minimal environmental safeguards. In Tamil Nadu, informal recycling practices and lack of citizen participation exacerbate environmental and health risks. There's no unified, transparent system for tracking how e-waste moves from households to collection centers or recyclers.
To develop a prototype web/mobile platform for tracking e-waste collection and incentivizing responsible disposal using a lightweight blockchain or ledger-based system. Users can register and log their e-waste items, receive digital "eco-points" for each submission. Collectors update the system when items are collected, transported, or recycled. Authorities can view real-time dashboards for tracking e-waste flow.
A functional end-to-end prototype demonstrating the digital traceability of e-waste and citizen-driven participation model. Expected results include improved transparency between citizens, collectors, and local authorities, and a scalable model supporting SDG 12: Responsible Consumption and Production.
In many semi-urban and rural areas of India, especially in Tamil Nadu, groundwater and local reservoirs are contaminated by industrial waste, fertilizers, and heavy metals. Water quality testing is mostly manual, infrequent, and reactive — contamination is often detected only after disease outbreaks or visible waterborne issues occur.
To design a low-cost, AI-powered predictive system for monitoring and forecasting water contamination levels using a combination of IoT sensors, open data, and machine learning models. The system should provide real-time visibility, predict contamination spikes, and notify village panchayats, NGOs, and public health departments.
A proof-of-concept predictive monitoring platform capable of providing real-time visibility into community water safety, predicting contamination events days in advance, and empowering local authorities with actionable insights. Contributing to SDG 6 (Clean Water and Sanitation) and SDG 3 (Good Health and Well-being).
India's urban road networks are uniquely complex — characterized by irregular lane patterns, mixed traffic, temporary barricades, frequent construction zones, and poor road maintenance. Current traffic modeling tools fail to accurately capture these real-world irregularities, leading to inaccurate predictions for congestion, safety risks, and infrastructure planning.
To design and develop an AI-assisted, MATLAB-integrated toolset that automatically generates realistic digital twins of Indian road networks from minimal inputs such as map data, drone imagery, or sensor datasets. The system should simplify digital twin creation, incorporate generative AI for auto-populating road environments, and integrate seamlessly with MATLAB, Simulink, and RoadRunner.
A prototype workflow that automates Indian road network model creation, achieving 70–90% reduction in manual modeling time, increased realism in simulation scenarios, and seamless integration with MATLAB tools. This enables more data-driven urban planning and autonomous driving research.
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All participants must use this official PPT template for their submissions. This ensures uniformity and fairness in presentation across all teams.
