Bussiness Ideas
India faces a growing crisis of women’s safety, with over 4.2 lakh cases reported annually. Over 13 lakh women missing in India in past 5 years(NCRB). Existing solutions like CCTV and panic apps are reactive, often delayed, and ineffective in real-time emergencies. There’s a clear gap in proactive, technology-enabled response systems.
Project ABHAY-WASP solves this by integrating AI, autonomous drones, IoT wearables, and mobile-cloud platforms to create a real-time urban safety grid. It enables automatic threat detection and instant drone response within minutes — reducing harm and improving trust in public safety systems. The platform is scalable, smart, and built to prevent crimes before they escalate.
Project ABHAY-WASP proposes an integrated, proactive approach to women’s safety using AI, autonomous drones, IoT wearables, and cloud-based systems. The platform functions as a real-time safety grid that detects threats and dispatches help within minutes, bridging the critical response-time gap in emergencies.
The solution begins with smart IoT wearables worn by users that can detect distress via biometric signals (e.g., elevated heart rate, sudden movement) or manual SOS. This alert is transmitted instantly to a central cloud platform, which evaluates the severity using AI-based threat analysis models. If verified, the system automatically deploys the nearest drone — equipped with 4K camera, GPS, and night vision — to the location.
The drone provides live video feed, helping responders assess the situation in real time. Simultaneously, nearby emergency services are notified through an integrated command and control dashboard. The platform also includes a mobile app for public use, data analytics for city planning, and interfaces for police and security teams.
This end-to-end system ensures faster, smarter, and autonomous intervention, unlike traditional systems that rely on delayed, manual responses. Scalable across cities and institutions, ABHAY-WASP aims to transform urban safety from reactive reporting to intelligent prevention.
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