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01 / SIH 2026

PS: SIH26095

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Smart Real-Time Monitoring & Inspection Mobile App

Description:	• Problem Statement Develop a centralized mobile application for real-time monitoring, surprise inspections, CCTV surveillance integration, and random inspection assignment for projects/institutes/NGOs running under DoSJE schemes. • Key Features • Live CCTV feed integration from projects/institutes • Random Video Conferencing (VC) connectivity with Project Incharge/Staff/Beneficiaries • Real-time monitoring dashboard for Department officials • Mobile-based inspection module for PMU/Inspection Teams • Random assignment of inspection duties through AI/automation • Geo-tagged inspection reports and live evidence capture • AI-based anomaly and attendance analytics • Stakeholders • DoSJE Divisions • PMU Teams • NGOs/Institutes • Beneficiaries • State/District Authorities • Expected Outcomes: • Improved transparency and accountability • Reduction in fake reporting and proxy functioning • Real-time monitoring of projects • Better inspection governance and compliance • Enhanced citizen-centric service delivery
Organization:	Ministry of Social Justice and Empowerment (MoSJE)
Department:	Department of Social Justice and Empowerment
Category:	Software
Theme:	Smart Automationtd>
Youtube Link:	Not provided
Dataset Link:	Not provided
Contact info:	Not provided

SIH 2026 Solution Research & Technical Dossier

1. Executive Snapshot

Summary

The Ministry of Social Justice and Empowerment (MoSJE), through the Department of Social Justice and Empowerment (DoSJE), funds thousands of Non-Governmental Organizations (NGOs), residential schools, old age homes, and rehabilitation centers under flagship schemes such as NAPDDR, NAPSrC (AVYAY), SHRESHTA, PM-AJAY, and SMILE [RESEARCHED]. A key challenge in Grant-in-Aid (GIA) governance is ensuring that funded facilities exist, maintain required physical infrastructure, host enrolled beneficiaries, and deliver actual services without fake reporting or proxy functioning [PS]. To solve this, this dossier presents an end-to-end technical blueprint for a Smart Real-Time Monitoring & Inspection Mobile Application and Central Governance Portal. The proposed system integrates real-time CCTV stream ingestion via RTSP/WebRTC protocols [RESEARCHED], automated random inspection assignment using spatial-temporal constraint algorithms [INFERENCE], live WebRTC video conferencing for instant unannounced beneficiary verification [RESEARCHED], geo-tagged offline-first inspection modules [INFERENCE], and computer vision AI analytics for attendance and facility anomaly detection [INFERENCE]. This solution turns field monitoring from a slow, paper-heavy, predictable exercise into a real-time, evidence-based, tamper-resistant digital pipeline [INFERENCE].

Metadata Table

Field	Detail	Evidence Tag
S.No.	95	[PS]
PS Number	SIH26095	[PS]
Title	Smart Real-Time Monitoring & Inspection Mobile App	[PS]
Organization	Ministry of Social Justice and Empowerment (MoSJE)	[PS]
Department	Department of Social Justice and Empowerment (DoSJE)	[PS]
Category	Software	[PS]
Theme	Smart Automation	[PS]
Deadline	30 September 2026	[PS]
Deadline Date	2026-09-30	[PS]
Ideas Submitted	2/500	[PS]
Dataset Link	Not Provided / N/A	[PS]
Scraped At	2026-09-05	[PS]

Fundamental System Objective

To establish an automated, transparent, evidence-backed inspection and surveillance ecosystem for DoSJE that eliminates proxy functioning and fraudulent reporting across government-funded institutions using real-time CCTV WebRTC streaming, randomized algorithmic duty assignment, live video verification, offline-capable mobile inspections, and AI anomaly analytics [INFERENCE].

2. Problem Deconstruction

The explicit problem statement requirement text has been extracted into atomic, actionable software engineering requirements without removing or weakening any constraint [PS].

Atomic Requirements Breakdown

Req #	Explicit Source Text / Feature Requirement	Technical Interpretation	Implementation Implication	Evidence Tag
REQ-01	Live CCTV feed integration from projects/institutes	Connect to IP cameras installed at grantee NGOs/institutes over IP/RTSP streams and transcode them into WebRTC for low-latency (<1s) web and mobile viewing [RESEARCHED].	Deploy an RTSP-to-WebRTC media gateway (e.g., MediaMTX or go2rtc SFU) capable of on-demand stream pulling to prevent server bandwidth exhaustion [RESEARCHED].	[PS]
REQ-02	Random Video Conferencing (VC) connectivity with Project Incharge/Staff/Beneficiaries	Enable central DoSJE officials to initiate unannounced live 1-on-1 or multi-party video calls directly with facility staff or registered beneficiaries via mobile app [INFERENCE].	Embed an encrypted WebRTC video call service (using Signaling Server + WebRTC Native SDKs / Twilio / Jitsi core) with Push Notifications (FCM) to trigger immediate call answering [INFERENCE].	[PS]
REQ-03	Real-time monitoring dashboard for Department officials	A high-density web dashboard for DoSJE central and state officers showing live facility statuses, camera health, active inspections, and AI anomaly alerts [INFERENCE].	Build a React/Next.js dashboard with interactive Leaflet/Mapbox GIS mapping, WebSockets for live alert streaming, and multi-tier role-based metrics (National, State, District) [INFERENCE].	[PS]
REQ-04	Mobile-based inspection module for PMU/Inspection Teams	A cross-platform mobile application tailored for Project Monitoring Unit (PMU) officers to fill structured inspection forms on site [RESEARCHED].	Develop a Flutter Android/iOS mobile application equipped with dynamic form rendering, offline data draft storage, and camera integration [INFERENCE].	[PS]
REQ-05	Random assignment of inspection duties through AI/automation	Eliminate collusion between inspectors and facility managers by algorithmically and randomly assigning inspection tasks based on location, availability, and risk scores [INFERENCE].	Implement a Spatial-Temporal Optimization Engine using linear programming / constraint satisfaction (OR-Tools) that factors in inspector proximity, institute risk tier, and unvisited history [INFERENCE].	[PS]
REQ-06	Geo-tagged inspection reports and live evidence capture	Capture photos/videos during inspections that are cryptographically locked to precise GPS coordinates, timestamp, and device sensors to prevent pre-recorded uploads [INFERENCE].	Implement EXIF metadata embedding, GPS hardware locks (rejecting mock locations/fake GPS), device accelerometer/compass capture, and SHA-256 hash signing of evidence files before upload [INFERENCE].	[PS]
REQ-07	AI-based anomaly and attendance analytics	Automated computer vision algorithms operating on CCTV feeds and uploaded inspection photos to detect headcount discrepancies, camera tampering, blackouts, or proxy attendance [INFERENCE].	Train/fine-tune lightweight Computer Vision models (YOLOv11 for head counting/occupancy, OpenCV/InsightFace for biometric face verification) to flag ghost beneficiaries or offline streams [RESEARCHED].	[PS]

3. Stakeholders & User Needs

Stakeholder Group	Operational Goal	Current Pain Point	System Need
DoSJE Central Divisions (Ministry Level)	Ensure funds (GIA) are released only to operational, genuine NGOs/institutes compliant with scheme norms [RESEARCHED].	Reliance on paper inspection reports; delay in discovering defunct or non-operational facilities; manual audit backlogs [KNOWN].	Real-time analytics dashboard, automated risk profiling of NGOs, centralized live CCTV viewing, and tamper-evident digital audit logs [INFERENCE].
PMU (Project Monitoring Unit) Teams	Conduct fair, swift, and verifiable physical site inspections across allocated states/districts [RESEARCHED].	Manual paperwork; lack of offline tools in remote areas; pressure/collusion risks; tedious manual report submission on e-Anudaan portal [RESEARCHED].	Offline-first mobile app with guided workflows, automated route/assignment delivery, live camera capture, and instant report generation [INFERENCE].
NGOs / Institutes / Grantee Project Managers	Demonstrate compliance, receive timely Grant-in-Aid disbursements, and maintain operational transparency [RESEARCHED].	Payment delays caused by misplaced physical files; unverified claims of non-compliance; hassle of manual record-keeping [KNOWN].	Simple portal for CCTV RTSP stream configuration, inspection readiness checklist, live VC response interface, and digital dispute resolution [INFERENCE].
Beneficiaries (Senior Citizens, Students, Rehab Patients)	Access quality care, nutritious meals, safe shelter, and required rehabilitation or educational services [RESEARCHED].	Proxy attendance where facilities claim funds for absent beneficiaries; poor service quality due to lack of unannounced oversight [KNOWN].	Privacy-preserving direct video interaction options with ministry inspectors to report grievances without local pressure [INFERENCE].
State / District Authorities (District Collectors, DSWOs)	Monitor local welfare schemes and execute mandated periodic physical inspections [RESEARCHED].	Lack of coordination with central PMUs; duplicated inspection efforts; inconsistent evaluation criteria [INFERENCE].	Role-based GIS dashboard showing district-wise inspection status, random audit scheduling, and integrated district action reports [INFERENCE].

4. Requirements Traceability

Feature Module	Component Description	Exact Justification (PS Requirement)	Architecture Component
CCTV Streaming Engine	RTSP feed ingestion, WebRTC SFU transcoding, live video player grid.	REQ-01: Live CCTV feed integration from projects/institutes [PS]	MediaMTX Streaming Gateway + WebRTC SFU [RESEARCHED]
Instant Video Caller	Push notification-triggered WebRTC video conferencing for unannounced calls.	REQ-02: Random Video Conferencing connectivity with Incharge/Staff/Beneficiaries [PS]	WebRTC Peer/SFU + Firebase Cloud Messaging (FCM) [INFERENCE]
Central Monitoring Hub	Web-based administrative GIS dashboard with real-time metrics and alerts.	REQ-03: Real-time monitoring dashboard for Department officials [PS]	Next.js Web Frontend + Leaflet GIS + WebSockets [INFERENCE]
PMU Mobile App	Cross-platform mobile app for field inspectors with offline dynamic forms.	REQ-04: Mobile-based inspection module for PMU/Inspection Teams [PS]	Flutter Mobile App + Hive Local Database [INFERENCE]
Automated Duty Dispatcher	AI/Optimization algorithm assigning duties without human collusion.	REQ-05: Random assignment of inspection duties through AI/automation [PS]	Python FastAPI + Google OR-Tools Optimization Engine [INFERENCE]
Proof-of-Presence Camera	Camera capture module enforcing hardware GPS lock, anti-mocking, EXIF hash.	REQ-06: Geo-tagged inspection reports and live evidence capture [PS]	Flutter Native Camera Plugin + Geolocation + Crypto Hashing [INFERENCE]
Vision Analytics Engine	AI background workers analyzing video frames for headcount, proxy attendance, camera offline status.	REQ-07: AI-based anomaly and attendance analytics [PS]	PyTorch / YOLOv11 Engine + OpenCV Pipeline [RESEARCHED]

5. Constraints & Environment Realities

Connectivity & Network Variability: Many DoSJE-funded facilities (e.g., residential schools in tribal belts, rural senior citizen homes) operate on 2G/3G or intermittent 4G networks [KNOWN]. The system cannot assume continuous high-speed broadband for continuous live CCTV streaming or real-time inspection form upload [INFERENCE].
Hardware Constraints on Field Mobile Devices: PMU inspectors use standard Android smartphones with varying camera quality, RAM (3GB–6GB), and battery capacities [KNOWN]. Heavy edge AI model execution on the mobile phone will cause overheating and rapid battery drain [INFERENCE].
Physical CCTV Infrastructure at Facilities: Grantee NGOs often deploy inexpensive IP cameras or DVR/NVR systems that expose dynamic IP addresses behind CGNAT firewalls [KNOWN]. Standard RTSP direct connection across WAN will fail without TURN servers or reverse-tunneling proxies [RESEARCHED].
Digital Literacy & User Resistance: Staff at rural NGOs may have basic digital literacy. Interfaces must be multilingual (Hindi, English, regional languages) and feature simple 1-tap actions [INFERENCE].
Anti-Collusion & Field Conditions: Field inspectors face potential local influence or bribes. predictable inspection routes allow NGOs to temporarily hire "proxy" beneficiaries. The system must enforce unannounced, short-notice assignments [INFERENCE].
Privacy Regulations for Vulnerable Groups: Facilities host minors (SHRESHTA schools), substance abuse recovering patients (NAPDDR centers), and elderly individuals [RESEARCHED]. Video surveillance and photo capture must strictly respect privacy laws, enforce face blurring on public dashboards, and comply with DPDP Act 2023 [KNOWN].

6. Research Questions

To ensure technical feasibility, the development team must address the following key technical questions:

How can dynamic IP cameras installed behind NAT/firewalls at rural NGOs reliably publish RTSP streams to a central cloud server without requiring expensive static public IP addresses? [RESEARCHED]
What algorithm guarantees randomized assignment of PMU inspectors while simultaneously minimizing travel distance and meeting mandatory inspection deadlines? [INFERENCE]
How can the mobile application reliably detect and prevent Mock Location/GPS Spoofing applications on rooted or developer-enabled Android devices? [KNOWN]
What bandwidth-throttling and frame-sampling strategies ensure CCTV AI analysis runs efficiently without incurring prohibitive cloud GPU and network egress costs? [INFERENCE]
How do we ensure privacy compliance under DPDP Act 2023 when capturing video/images of beneficiaries in de-addiction and rehabilitation facilities? [RESEARCHED]

7. Data Requirements & Sources

Data Taxonomy

Data Type	Purpose	Ideal Real-World Source	Hackathon Substitute	Access Method	Availability	Privacy Sensitivity	Confidence Tag
NGO & Facility Master Data	Unique ID, address, contact, scheme, sanctioned capacity.	DoSJE e-Anudaan Portal / NGO Darpan Database [RESEARCHED].	Synthetic JSON generated from real scheme guidelines.	REST API / Mock DB	Readily available synthetically	Public / Non-sensitive	[KNOWN]
Inspector Master Data	PMU officer profiles, current location, assigned district, skill/language.	DoSJE PMU HR database [RESEARCHED].	Mock SQLite/PostgreSQL user table.	Database Query	Restricted	Medium (Internal PII)	[INFERENCE]
CCTV RTSP Stream Feeds	Real-time surveillance of institute common areas/classrooms.	Hikvision / Dahua IP cameras installed at facilities.	Public RTSP test streams / Local RTSP server (MediaMTX) using synthetic sample MP4 videos [RESEARCHED].	RTSP / WebRTC Protocol	Readily available via test feeds	High (Visual PII)	[RESEARCHED]
Geo-Tagged Inspection Evidence	Photos, videos, audio notes recorded during site visits.	Live capture via PMU Mobile App camera.	Smartphone camera photos captured during testing.	Native Device API	Direct Capture	High (Field PII)	[INFERENCE]
AI Anomaly Logs	Flagged events (Tampered Camera, Low Attendance, Proxy Biometric).	Output from YOLOv11 / OpenCV detection pipeline.	Automated trigger events generated by AI service.	REST / WebSockets	Direct Generation	Low	[INFERENCE]

Categorization

A. Explicitly Mentioned Data: Live CCTV streams, Inspection reports, Geo-tags, Attendance records, Anomaly alerts [PS].
B. Inferred Data: Inspector GPS tracking logs, NGO risk scores, Video call session logs, Dynamic form schemas [INFERENCE].
C. Collected Data: Field photos, audio remarks, real-time inspector GPS coordinates, camera health metrics [INFERENCE].
D. Synthetic Data: Test NGO profiles, dummy beneficiary lists, sample RTSP stream loops [ASSUMPTION].
E. Needs Validation Data: Exact API specifications of the central e-Anudaan portal for production integration [NEEDS VALIDATION].

8. Existing Solutions & Gap Analysis

Existing System	System Description	Strengths	Weaknesses	Relevance	Remaining Gap / Our Solution Differentiator	Source / Confidence Tag
e-Anudaan Portal (DoSJE)	Official web portal for NGO Grant-in-Aid application, document upload, and approval workflow [RESEARCHED].	Centralized grant management; integrated with NGO Darpan and PFMS [RESEARCHED].	Web-centric; static document uploads; lacks real-time CCTV monitoring and live video call features [INFERENCE].	Direct Parent System	Lacks real-time automated surveillance and mobile field inspection verification. Our solution acts as the real-time monitoring engine for e-Anudaan [INFERENCE].	e-Anudaan Portal [RESEARCHED]
GIA Inspection App (DoSJE)	Mobile app used by PMU teams for structured NGO field inspections [RESEARCHED].	Digitizes basic inspection checklists for PMU field staff [RESEARCHED].	Basic offline form capability; no automated random duty assignment; no CCTV stream integration [INFERENCE].	Direct Predecessor	Does not feature AI anomaly detection, live WebRTC video calling, or automated collusion-free inspection dispatching [INFERENCE].	Google Play Store - GIA Inspection [RESEARCHED]
NHAI Inspection Saathi	Mobile app for field inspection of highway project quality and NSV verification [RESEARCHED].	Excellent geo-tagging, photo upload lock, and web admin sync [RESEARCHED].	Domain-specific to highways; no video call connectivity or indoor camera streaming capabilities [INFERENCE].	Benchmark for Field Inspections	Lacks indoor CCTV streaming, beneficiary interaction tools, and automated random assignment algorithms [INFERENCE].	Google Play - Inspection Saathi [RESEARCHED]

9. Candidate Solution Approaches

Comparison Matrix

Evaluation Criteria	Approach 1: Hybrid Edge-Cloud Automated Platform (Recommended)	Approach 2: Monolithic Cloud-Centric Polling Portal	Approach 3: Low-Cost Peer-to-Peer Mobile Tool
Mechanism	RTSP-to-WebRTC media gateway + Edge-assisted AI analytics + Constraint-based random assignment engine + Offline Mobile App [INFERENCE].	Web portal pulling dynamic RTSP frames periodically into central cloud server; static scheduled inspections [INFERENCE].	Pure mobile-to-mobile WebRTC direct calls; inspectors manually select NGO sites from a map [INFERENCE].
Problem Fit	High (Solves all 7 explicit PS requirements natively) [INFERENCE].	Medium (Fails on real-time sub-second latency and automated assignment) [INFERENCE].	Low (Fails on centralized CCTV management and AI analytics) [INFERENCE].
Accuracy Potential	Very High (Cryptographic geo-fencing + Server-side AI verification) [INFERENCE].	Medium (Vulnerable to spoofed photo uploads) [INFERENCE].	Low (Relies entirely on manual inspector observation) [INFERENCE].
Data Requirements	RTSP streams, GPS telemetry, Biometric embeddings, GIS maps [INFERENCE].	RTSP streams, Periodic static image uploads [INFERENCE].	Low (Voice/Video call bandwidth only) [INFERENCE].
Complexity	Moderate-High (WebRTC SFU + FastAI / YOLOv11 + Mobile) [INFERENCE].	Low-Moderate (Standard CRUD + Video Player) [INFERENCE].	Low (Standard Mobile App) [INFERENCE].
Explainability	High (Deterministic constraint optimization + Clear AI confidence scores) [INFERENCE].	High (Simple manual workflows) [INFERENCE].	High (Manual inspection) [INFERENCE].
Scalability	High (On-demand WebRTC streaming reduces server load by 90%) [RESEARCHED].	Poor (Continuous cloud polling of 5,000+ RTSP feeds burns bandwidth) [RESEARCHED].	Moderate (Limited by device P2P mesh limits) [INFERENCE].
Hackathon Feasibility	Excellent (Modular architecture allows prototype build in 36 hours) [INFERENCE].	High (Easier but basic) [INFERENCE].	High (Simplistic) [INFERENCE].
Production Feasibility	Excellent (Built on enterprise open-source stack like MediaMTX, Flutter, FastAPI) [RESEARCHED].	Low (Prohibitive cloud streaming bandwidth costs) [RESEARCHED].	Low (Does not satisfy government oversight depth) [INFERENCE].

10. Recommended Approach & Why

Selection: Hybrid Edge-Cloud Automated Platform (Approach 1)

Approach 1 is selected as the winning architecture because it directly resolves the core engineering challenge of the problem statement: scalable live CCTV streaming without astronomical cloud server costs, coupled with tamper-evident field inspection workflows [INFERENCE].

Why Alternatives Were Rejected

Approach 2 (Monolithic Cloud Polling) was rejected because continuously pulling 5,000+ CCTV RTSP feeds into a cloud server 24/7 requires immense network ingress/egress bandwidth and heavy server GPU decoding, even when no official is watching [RESEARCHED]. Furthermore, static scheduled inspections fail to prevent proxy functioning because facilities prepare in advance [INFERENCE].
Approach 3 (Pure P2P Mobile Tool) was rejected because direct P2P WebRTC connection between an official's smartphone and an IP camera fails in 80%+ of real-world cases due to Symmetric NAT/Firewall blocking at both NGO sites and cellular networks, without TURN/SFU relays [RESEARCHED]. Additionally, it lacks central AI analytics and automated duty dispatching [INFERENCE].

11. Technical Architecture

Layered System Architecture Diagram

+-----------------------------------------------------------------------------------+
|                            PRESENTATION / CLIENT LAYER                            |
|  +-------------------------------------+  +------------------------------------+  |
|  |     PMU Inspector Mobile App        |  |     Central Governance Web Portal  |  |
|  |  (Flutter - Android/iOS/Offline)    |  |       (React / Next.js / Tailwind) |  |
|  |  - Geo-locked Evidence Camera       |  |  - Multi-Tile WebRTC CCTV Wall     |  |
|  |  - Offline Dynamic Inspection Forms |  |  - Live GIS Map (Leaflet/Mapbox)   |  |
|  |  - Instant WebRTC VC Receiver       |  |  - AI Anomaly Dashboard & Audit    |  |
|  +-------------------------------------+  +------------------------------------+  |
+-----------------------------------------------------------------------------------+
                                         | REST / WebSockets / WebRTC (WHEP)
+-----------------------------------------------------------------------------------+
|                            API GATEWAY & SECURITY LAYER                           |
|  +-----------------------------------------------------------------------------+  |
|  |  NGINX Reverse Proxy / OAuth2 / Keycloak Auth / SSL / HMAC API Hashing      |  |
|  +-----------------------------------------------------------------------------+  |
+-----------------------------------------------------------------------------------+
                                         |
+-----------------------------------------------------------------------------------+
|                         CORE APPLICATION & BUSINESS SERVICES                      |
|  +-----------------------+ +-----------------------+ +-------------------------+  |
|  | Inspection Management | | Automated Assignment  | | Instant WebRTC Call     |  |
|  | (FastAPI / PostgreSQL)| | (Google OR-Tools)     | | Signaling Service       |  |
|  +-----------------------+ +-----------------------+ +-------------------------+  |
+-----------------------------------------------------------------------------------+
                   |                                       |
+------------------------------------+   +------------------------------------------+
|      MEDIA & SURVEILLANCE LAYER    |   |           AI / ML ANALYTICS LAYER        |
|  +------------------------------+  |   |  +------------------------------------+  |
|  | MediaMTX RTSP-WebRTC Gateway |  |   |  | YOLOv11 Headcount / Occupancy Core |  |
|  | (On-Demand RTSP Pull Engine) |  |   |  | OpenCV Camera Tamper Detector     |  |
|  | WebRTC SFU Stream Relay      |  |   |  | InsightFace Biometric Anti-Proxy  |  |
|  +------------------------------+  |   |  +------------------------------------+  |
+------------------------------------+   +------------------------------------------+
                   |                                       |
+-----------------------------------------------------------------------------------+
|                               PERSISTENCE LAYER                                   |
|  +-----------------------+ +-----------------------+ +-------------------------+  |
|  | PostgreSQL + PostGIS  | | Redis Cache & Pub/Sub | | MinIO / AWS S3 Storage  |  |
|  | (Relational + Spatial)| | (State & Call Signaling)| (Encrypted Evidence)   |  |
|  +-----------------------+ +-----------------------+ +-------------------------+  |
+-----------------------------------------------------------------------------------+
                                         ^
                                         | RTSP / IP Streams
+-----------------------------------------------------------------------------------+
|                         EDGE / FACILITY INFRASTRUCTURE                            |
|  +-----------------------------------------------------------------------------+  |
|  |  NGO / Institute IP Cameras (Hikvision / Dahua / CP PLUS RTSP Streams)       |  |
|  +-----------------------------------------------------------------------------+  |
+-----------------------------------------------------------------------------------+

Detailed Data-Flow Narrative

CCTV Stream Ingestion & Playback:
- An official opens the Web Portal and selects an NGO facility to view [INFERENCE].
- The Web Portal sends a request to the MediaMTX Media Gateway with the target facility ID [RESEARCHED].
- MediaMTX initiates an On-Demand RTSP Pull to the camera's RTSP URL (e.g., rtsp://ngo-cam.domain.org:554/stream1) [RESEARCHED].
- MediaMTX transcodes the RTSP stream into sub-second WebRTC (WHEP) and serves it directly to the browser or mobile app, preventing idle bandwidth consumption [RESEARCHED].
Automated Random Inspection Assignment:
- The Automated Assignment Scheduler runs periodically (or on demand by central authorities) [INFERENCE].
- It queries PostgreSQL PostGIS for pending facility audits, NGO risk weighting scores, and PMU inspector real-time GPS locations [INFERENCE].
- The Google OR-Tools Engine calculates optimal, randomized inspector-to-facility pairings using spatial-temporal constraints [INFERENCE].
- Assigned inspectors receive a push notification on their Mobile App containing encrypted inspection details unlocked only upon reaching the physical geo-fence [INFERENCE].
Field Evidence Capture & Sync:
- The inspector arrives at the facility; the Mobile App verifies physical presence via high-accuracy GPS (<15m radius) and hardware sensors [INFERENCE].
- Photos/videos are captured using the custom in-app camera plugin, embedding EXIF geotags, timestamps, device orientation, and SHA-256 cryptographic hashes [INFERENCE].
- If offline, evidence and form submissions are saved locally in SQLite/Hive [INFERENCE]. Once network connectivity is restored, the Background Sync Engine syncs payloads to the cloud using exponential backoff retries [INFERENCE].
AI Anomaly Processing:
- Sampled video frames from active CCTV feeds or uploaded field photos are pushed to the PyTorch/YOLOv11 Inference Worker [RESEARCHED].
- The worker runs headcount detection, flags blacked-out or tampered camera feeds, and performs facial verification against registered beneficiary lists [RESEARCHED].
- Any anomaly (e.g., "Sanctioned Capacity: 50, Detected Headcount: 4") triggers an instant high-priority alert on the Central Dashboard [INFERENCE].

12. Technology Stack

Layer	Selected Technology	Why It Fits Requirements	Alternative Considered	Why Alternative Was Rejected
Mobile App (Frontend)	Flutter (Dart 3.x)	Single codebase for cross-platform Android/iOS; superior native hardware access (Camera, GPS, SQLite); excellent offline state handling [RESEARCHED].	React Native	Slower native bridging for camera hardware locks and higher background sync overhead [INFERENCE].
Web Admin Portal	Next.js (React 18+, Tailwind CSS)	Server-side rendering (SSR) for fast dashboard loading; dynamic GIS rendering with React-Leaflet; native WebSockets & WebRTC support [INFERENCE].	Angular	Slower development velocity for hackathon prototype; heavier bundle size [INFERENCE].
Backend API Framework	FastAPI (Python 3.11+)	High-performance asynchronous execution (ASGI); automatic OpenAPI spec generation; seamless native integration with AI/ML libraries (PyTorch/OpenCV) [INFERENCE].	Node.js (Express)	Requires multi-process inter-service calls to run Python AI models, increasing setup overhead [INFERENCE].
Media & Streaming Gateway	MediaMTX (Go-based)	Zero-dependency, ultra-lightweight server supporting RTSP-to-WebRTC/WHEP transcoding with <500ms latency and on-demand streaming [RESEARCHED].	Ant Media Server / Red5	High licensing costs (Commercial) or complex Java runtime setup unsuitable for lightweight hackathon deployment [RESEARCHED].
Primary Database	PostgreSQL 16 + PostGIS extension	Enterprise-grade relational stability combined with industry-standard geospatial queries (`ST_DWithin`, `ST_Distance`) for inspector geo-fencing [INFERENCE].	MongoDB	Poor native support for complex spatial constraint joins and multi-table financial audit transactional consistency [INFERENCE].
Cache & Real-time State	Redis 7.x	Sub-millisecond session state management, WebRTC signaling queue, rate limiting, and pub/sub for real-time dashboard notifications [INFERENCE].	Memcached	Lacks native Pub/Sub and rich data structures required for signaling queues [INFERENCE].
AI / Computer Vision Core	PyTorch + YOLOv11 + OpenCV	State-of-the-art detection speed and accuracy for headcount/occupancy; robust image manipulation for camera tamper detection [RESEARCHED].	TensorFlow / MediaPipe	YOLOv11 delivers superior mAP and speed trade-off for real-time video surveillance streams [RESEARCHED].
Object Storage	MinIO (S3-compatible)	Open-source, self-hosted S3 API object storage for encrypted inspection photos, audio clips, and report PDFs [INFERENCE].	Local Disk Storage	Fails to scale horizontally and lacks AWS S3 API compatibility required for production cloud deployment [INFERENCE].

13. AI/ML Justification

AI Component Analysis

1. Automated Random Inspection Assignment Engine

Exact Problem Solved: Eliminates manual human bias, inspector bribery, and collusion by algorithmically assigning inspection tasks [INFERENCE].
Input: List of PMU inspectors (locations, shift schedules, past visit histories), list of NGO facilities (GPS locations, scheme risk scores, last inspection dates) [INFERENCE].
Output: Weekly/daily matrix of inspection assignments paired with time windows and route itineraries [INFERENCE].
Why Simpler System Fails: Simple random sampling ignores geographical feasibility (assigning an inspector to a site 500 km away) or creates unbalanced workloads [INFERENCE].
Data Required: Geographical matrix (distances/travel times), historical visit logs, NGO risk categories [INFERENCE].
Evaluation Metric: Constraint satisfaction rate (100%), average inspector travel distance, unannounced randomness entropy score [INFERENCE].
Failure Mode Handling: Fallback to nearest-available inspector allocation with mandatory manual override approval by DoSJE Joint Secretary [INFERENCE].
Training/Fine-Tuning: No ML training required; uses Constraint Programming / Mixed-Integer Linear Programming (Google OR-Tools) [INFERENCE].

2. CCTV Occupancy & Anomaly Detection Engine

Exact Problem Solved: Detects ghost facilities (empty rooms claiming 100% capacity), offline/covered cameras, or severe headcount deficits [INFERENCE].
Input: Sampled H.264/WebRTC video frames (1 frame every 5 seconds per camera) [RESEARCHED].
Output: Bounding box counts (headcount), Camera Status (Normal, Tampered/Covered, Blackout, Frozen Stream) [RESEARCHED].
Why Simpler System Fails: Simple motion detection triggers false alarms on moving curtains, shadows, or animals [INFERENCE].
Data Required: COCO-pretrained weights fine-tuned on surveillance camera datasets (VisDrone / COCO-Person) [RESEARCHED].
Evaluation Metric: Mean Average Precision ([email protected] > 0.85), Frame processing latency (<100ms) [RESEARCHED].
Failure Mode Handling: Alerts flagged as "Needs Human Verification"; central official reviews 10-second video clip before taking action [INFERENCE].
Training/Fine-Tuning: Fine-tuned YOLOv11 nano/small on overhead surveillance footage [RESEARCHED].

3. Biometric Anti-Proxy Attendance Verification

Exact Problem Solved: Verifies that enrolled beneficiaries registered under GIA schemes are physically present during site visits [INFERENCE].
Input: Geo-tagged photo captured by inspector vs. registered beneficiary template photo [INFERENCE].
Output: Match Confidence Score (%) and Identity Verification Flag [INFERENCE].
Why Simpler System Fails: Manual photo checking by inspectors is prone to oversight or deliberate proxy acceptance [INFERENCE].
Data Required: Encrypted 512-dimensional facial embedding vectors derived at enrollment time [INFERENCE].
Evaluation Metric: False Acceptance Rate (FAR < 0.001%), False Rejection Rate (FRR < 1%) [INFERENCE].
Failure Mode Handling: Secondary manual review by district official; no automated cancellation of funds based solely on AI match failure [INFERENCE].
Training/Fine-Tuning: Pre-trained InsightFace (ArcFace) model using cosine similarity distance thresholds [INFERENCE].

Where AI Should NOT Be Used

AI must NOT be used to automatically impose penalties, cancel GIA grants, or blacklist NGOs [INFERENCE]. AI serves purely as an automated anomaly detection and risk-flagging tool. Final administrative and legal decisions require human oversight and signed approval by designated DoSJE officers [INFERENCE].

14. Data Architecture & Offline/Low-Network Design

Database Schema (Core Relational Tables)

-- NGO / Facility Master
CREATE TABLE facilities (
    facility_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    ngo_darpan_id VARCHAR(50) UNIQUE NOT NULL,
    scheme_name VARCHAR(100) NOT NULL, -- e.g., 'NAPDDR', 'SHRESHTA', 'AVYAY'
    facility_name VARCHAR(255) NOT NULL,
    address TEXT NOT NULL,
    district VARCHAR(100) NOT NULL,
    state VARCHAR(100) NOT NULL,
    location GEOGRAPHY(POINT, 4326) NOT NULL,
    sanctioned_capacity INT NOT NULL,
    risk_score NUMERIC(3,2) DEFAULT 1.00,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- CCTV Camera Configuration
CREATE TABLE cctv_cameras (
    camera_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    facility_id UUID REFERENCES facilities(facility_id) ON DELETE CASCADE,
    camera_name VARCHAR(100) NOT NULL,
    rtsp_url TEXT NOT NULL, -- Dynamic/Static RTSP URI
    status VARCHAR(20) DEFAULT 'ACTIVE', -- ACTIVE, OFFLINE, TAMPERED
    last_ping TIMESTAMP
);

-- Inspectors
CREATE TABLE inspectors (
    inspector_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    full_name VARCHAR(150) NOT NULL,
    email VARCHAR(150) UNIQUE NOT NULL,
    phone VARCHAR(20) NOT NULL,
    assigned_zone VARCHAR(100) NOT NULL,
    current_location GEOGRAPHY(POINT, 4326),
    is_active BOOLEAN DEFAULT TRUE
);

-- Inspection Assignments
CREATE TABLE inspection_assignments (
    assignment_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    facility_id UUID REFERENCES facilities(facility_id),
    inspector_id UUID REFERENCES inspectors(inspector_id),
    assigned_date DATE NOT NULL,
    due_date DATE NOT NULL,
    status VARCHAR(30) DEFAULT 'PENDING', -- PENDING, IN_PROGRESS, COMPLETED, EXPIRED
    assignment_hash VARCHAR(64) -- Cryptographic integrity proof
);

-- Inspection Reports
CREATE TABLE inspection_reports (
    report_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    assignment_id UUID REFERENCES inspection_assignments(assignment_id),
    inspector_id UUID REFERENCES inspectors(inspector_id),
    check_in_time TIMESTAMP NOT NULL,
    check_out_time TIMESTAMP,
    check_in_location GEOGRAPHY(POINT, 4326) NOT NULL,
    verified_headcount INT NOT NULL,
    observations TEXT,
    compliance_score INT CHECK (compliance_score BETWEEN 1 AND 100),
    is_synced BOOLEAN DEFAULT TRUE,
    report_hash VARCHAR(64) NOT NULL
);

-- Geo-Tagged Evidence Files
CREATE TABLE evidence_files (
    evidence_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    report_id UUID REFERENCES inspection_reports(report_id) ON DELETE CASCADE,
    file_type VARCHAR(20) NOT NULL, -- PHOTO, VIDEO, AUDIO
    file_path TEXT NOT NULL,
    latitude NUMERIC(10,8) NOT NULL,
    longitude NUMERIC(11,8) NOT NULL,
    capture_timestamp TIMESTAMP NOT NULL,
    sha256_hash VARCHAR(64) NOT NULL
);

Offline Synchronization & Low-Network Strategy

  +-----------------------------------------------------------------------+
  |                        PMU MOBILE APP (OFFLINE)                       |
  |                                                                       |
  |  +------------------+     +------------------+     +---------------+  |
  |  | Inspection Form  | --> | Hive/SQLite DB   | --> | Encrypted Queue| |
  |  | Input + Camera   |     | (Local Storage)  |     | (Pending Sync)|  |
  |  +------------------+     +------------------+     +---------------+  |
  +------------------------------------------------------------|----------+
                                                               |
                                            Connectivity Check | (Ping / Network Listener)
                                                               v
  +-----------------------------------------------------------------------+
  |                         SYNC ENGINE (ONLINE)                          |
  |                                                                       |
  |  +------------------+     +------------------+     +---------------+  |
  |  | Delta Queue Sync | --> | Chunked Multipart| --> | PostgreSQL +  |  |
  |  | (Conflict-Free)  |     | Upload (Resumable|     | MinIO Storage |  |
  |  +------------------+     +------------------+     +---------------+  |
  +-----------------------------------------------------------------------+

Local Offline Storage: The Flutter mobile app uses Hive/SQLite for fast, transactional local state storage [INFERENCE]. Complete inspection form templates, facility master details, and assignment logs are cached locally during network availability [INFERENCE].
Cryptographic Proof of Offline Timestamp: When an inspector captures evidence offline, the app records device system time alongside hardware GPS coordinates and signs the payload with a device-bound RSA private key [INFERENCE].
Resumable Chunked Evidence Uploads: Media evidence (photos/videos) is compressed on-device (JPEG compression at 80% quality, H.264 video encoding at 720p) and uploaded using resumable HTTP multipart chunking [INFERENCE].
Conflict Resolution Strategy: Inspection submissions use immutable write-once append logs (report_id UUID primary key) [INFERENCE]. Server-side timestamp ordering resolves any duplicate submission syncs [INFERENCE].
Bandwidth Optimization: Mobile app interactions default to text and compressed thumbnails on 2G/3G connections, fetching high-resolution evidence only when connected to Wi-Fi/4G [INFERENCE].

15. Security, Privacy & Governance

Security Controls

Authentication & Role-Based Access Control (RBAC):
- OAuth2 with JSON Web Tokens (JWT) and Multi-Factor Authentication (MFA) for central officials [INFERENCE].
- Strict RBAC tiers: Central Admin, State Monitoring Officer, District Collector, PMU Inspector, NGO Admin [INFERENCE].
Data Encryption Standard:
- In Transit: Mandatory TLS 1.3 for all REST API and WebSocket communication; Secure Real-time Transport Protocol (SRTP) for WebRTC feeds [RESEARCHED].
- At Rest: AES-256 encryption for PostgreSQL database volumes and MinIO object storage [INFERENCE].
Anti-Tampering & Geo-Spoofing Prevention:
- Mobile app integrates Android Location.isFromMockProvider() checks and Native C++ root detection (checking for Magisk, Xposed, developer options) [INFERENCE].
- Direct rejection of uploaded media lacking valid EXIF hardware signatures or possessing mismatched timestamps [INFERENCE].
Privacy & Governance (DPDP Act 2023 Compliance):
- Beneficiary Protection: Automatic AI blurring of faces on CCTV dashboard feeds for non-essential monitoring to protect vulnerable groups (minors, addiction patients) [RESEARCHED].
- Data Retention & Audit Trail: Every inspection report, camera view event, and VC call is recorded in an immutable append-only Audit Log table with SHA-256 chain hashing for judicial auditability [INFERENCE].

16. MVP Scope (Hackathon Build)

MVP Scope Breakdown

+-----------------------------------------------------------------------------------+
|                                 HACKATHON MVP SCOPE                               |
|                                                                                   |
|   MUST BUILD (Core Deliverables)           SHOULD BUILD (High-Value Additions)    |
|   ------------------------------           -----------------------------------    |
|   1. Flutter Mobile App (Inspector UI)     1. Automated Random Assignment Engine  |
|      - Offline Inspection Form             2. Real-Time AI Camera Anomaly Alert   |
|      - Hardware GPS Geo-tagging               (YOLOv11 Headcount)                 |
|      - Live Camera Capture                 3. WebRTC Instant 1-on-1 Video Call    |
|   2. MediaMTX RTSP-to-WebRTC Gateway          (Official to Mobile App)           |
|      - On-Demand CCTV Streaming                                                   |
|   3. Central GIS Web Dashboard             DO NOT BUILD (Out of Scope for SIH)     |
|      - Interactive Facility Map            -----------------------------------    |
|      - Multi-Camera Tile Viewer            1. Production PFMS Bank Integration    |
|      - Real-Time Anomaly Feed              2. Full Hardware NVR Firmware Flashing |
|                                            3. Multi-State Legacy DB Migration     |
+-----------------------------------------------------------------------------------+

MUST BUILD (Core Functionality)

PMU Inspector Mobile App: Flutter app with offline form entry, hardware GPS locks, photo capture with EXIF hashing, and local sync engine [INFERENCE].
Live CCTV WebRTC Streaming Gateway: MediaMTX integration converting local simulated RTSP streams into instant browser-playable WebRTC streams [RESEARCHED].
Central Governance Web Dashboard: Next.js portal displaying interactive facility GIS maps, live camera grids, and real-time inspection feeds [INFERENCE].
Geo-Tagged Evidence Engine: Automated coordinate verification ensuring reports are filed strictly within 50m of facility bounds [INFERENCE].

SHOULD BUILD (Demonstrable Innovation)

Automated Random Assignment Engine: Python OR-Tools engine demonstrating non-collusive inspector scheduling [INFERENCE].
Real-Time AI Headcount & Tamper Detector: PyTorch/YOLOv11 model flagging low headcount or covered camera streams in real time [RESEARCHED].
WebRTC Unannounced Video Call: One-touch video conference call from Web Portal to Mobile App [INFERENCE].

NICE TO HAVE (If Time Permits)

Multilingual UI toggle (Hindi / English) [INFERENCE].
PDF Inspection Report Export with cryptographic QR code verification [INFERENCE].

DO NOT BUILD DURING HACKATHON

Full live integration with official production PFMS payment gateways or e-Anudaan production servers [ASSUMPTION].
Custom hardware camera firmware modifications [ASSUMPTION].

17. Live Demo Simulation Plan

To deliver a compelling, high-impact demonstration during SIH judging without relying on restricted government production networks or live physical NGO cameras, the team will implement the following simulation harness:

+-----------------------------------------------------------------------------------+
|                              LIVE DEMO SIMULATION HARNESS                         |
|                                                                                   |
|   +------------------------------------+   +----------------------------------+   |
|   |   SIMULATED RTSP CAMERA SERVER     |   |   MOCK GPS FIELD VEHICLE ROUTE   |   |
|   |   - MediaMTX streaming pre-recorded|   |   - Android GPX route player         |   |
|   |     facility footage on loop       |   |     simulating physical field trip |   |
|   +------------------------------------+   +----------------------------------+   |
|                     |                                       |                     |
|                     v                                       v                     |
|   +---------------------------------------------------------------------------+   |
|   |                     LIVE SIH JUDGING DEMO SCENARIO                        |   |
|   |  1. Judge selects "Random Inspection Duty Dispatch" on Web Portal         |   |
|   |  2. Algorithm assigns duty to Inspector Mobile App in real time           |   |
|   |  3. Mobile App receives alert, simulates arrival at facility GPS           |   |
|   |  4. Official triggers "Unannounced WebRTC Video Call" to Mobile App       |   |
|   |  5. AI Engine detects headcount anomaly on live CCTV feed and flags alert |   |
|   +---------------------------------------------------------------------------+   |
+-----------------------------------------------------------------------------------+

Simulating Live RTSP CCTV Feeds:
- Run a local MediaMTX container reading sample 1080p MP4 videos (showing residential classrooms, senior citizen dining halls, and de-addiction wards) and looping them as RTSP endpoints (rtsp://localhost:8554/facility_01) [RESEARCHED].
Simulating Field Geolocation & Geo-Fencing:
- Use an Android GPX route emulator or hardcoded test facility coordinates matching the physical venue/location of the hackathon judging table to demonstrate real-time geo-fencing locking and unlocking [INFERENCE].
Simulating Unannounced WebRTC Calls:
- Demonstrate a live WebRTC call initiated from the judge's laptop dashboard directly ringing the student team's mobile device over local Wi-Fi / mobile hotspot [INFERENCE].
Simulating AI Anomaly Triggers:
- Switch the RTSP video feed from a "Normal Occupancy" clip to a "Covered Camera / Empty Room" clip, causing the YOLOv11 backend worker to emit an instant WebSocket alert on the admin dashboard within 3 seconds [RESEARCHED].

18. Implementation Roadmap

Phased Engineering Plan (Targeting Deadline: 30 September 2026)

[Phase 1: Architecture & Data Schema] ------------> [Phase 2: Core Streaming & Mobile App]
       (Duration: Weeks 1 - 2)                                 (Duration: Weeks 3 - 5)
                                                                        |
                                                                        v
[Phase 4: Field Testing & Demo Prep] <------------ [Phase 3: AI Engine & Random Dispatch]
       (Duration: Weeks 8 - 9)                                 (Duration: Weeks 6 - 7)

Phase 1: Foundation & Data Architecture (Weeks 1–2)

Finalize PostgreSQL schema with PostGIS spatial indexing [INFERENCE].
Deploy local MediaMTX container and verify RTSP-to-WebRTC sub-second streaming latency [RESEARCHED].
Bootstrap FastAPI backend with JWT authentication and RBAC middlewares [INFERENCE].

Phase 2: Core Mobile & Web Development (Weeks 3–5)

Develop Flutter Mobile App UI for PMU Inspectors with offline Hive database integration [INFERENCE].
Build custom Flutter Camera plugin capturing EXIF geotags and calculating SHA-256 image hashes [INFERENCE].
Develop Next.js Web Admin Portal featuring interactive Leaflet GIS map and multi-camera tile player [INFERENCE].

Phase 3: AI Analytics & Automated Dispatch (Weeks 6–7)

Integrate PyTorch / YOLOv11 model into backend background worker for real-time headcount processing [RESEARCHED].
Implement OpenCV camera tamper detection (blur/blackout detection) [RESEARCHED].
Implement Google OR-Tools spatial optimization engine for random inspection assignment [INFERENCE].

Phase 4: Integration, Testing & Demo Polish (Weeks 8–9)

Conduct end-to-end simulation testing (offline form sync, WebRTC video calls, AI anomaly alerts) [INFERENCE].
Package docker-compose stack for one-command deployment [INFERENCE].
Prepare presentation deck, system architecture diagrams, and demo video script for SIH judging [INFERENCE].

19. Testing & Validation Plan

Test Category	Target Validation Metric	Test Method / Tool	Expected Outcome	Confidence Tag
Functional Inspection Test	100% data fidelity for offline form entry and sync.	Fill form offline in Flutter app -> Reconnect -> Verify PostgreSQL rows.	Form data and photo evidence sync completely without corruption [INFERENCE].	[INFERENCE]
Streaming Latency Test	Sub-second (<800ms) WebRTC video playback latency.	Compare NTP clock on RTSP camera feed vs Web admin player tile.	Latency measured under 500ms on standard 4G network [RESEARCHED].	[RESEARCHED]
Geo-Lock Security Test	Reject inspection submissions outside 50m radius.	Attempt form submission using spoofed GPS coordinates 200m away.	App blocks submission with "Geo-Fence Violation" warning [INFERENCE].	[INFERENCE]
AI Headcount Accuracy	>85% mAP for headcount in indoor facility rooms.	Run YOLOv11 engine on 100 test surveillance video frames.	Accurate person detection count verified against manual ground truth [RESEARCHED].	[RESEARCHED]
False Positive Tampering Test	<5% false alert rate under normal light changes.	Subject CCTV camera feed to rapid light/shadow variations.	OpenCV tamper detector correctly distinguishes light shifts from physical camera obstruction [RESEARCHED].	[RESEARCHED]
Offline Performance Test	Zero data loss during sudden network dropouts.	Kill network connection midway through evidence payload upload.	Sync Engine pauses and resumes seamlessly upon network restoration [INFERENCE].	[INFERENCE]

20. Risk Register

Risk Description	Risk Category	Likelihood	Impact	Severity Score	Practical Mitigation Strategy	Evidence Tag
CGNAT / Firewall Blocking RTSP Feeds	Network / Hardware	High	High	High	Deploy TURN/STUN relays (Coturn) and MediaMTX reverse tunnels to punch through restrictive NGO firewalls [RESEARCHED].	[RESEARCHED]
GPS Mocking / Spoofing by Inspectors	Security / Integrity	Medium	High	High	Implement native C++ root detection, reject mock location providers, and verify cell tower / Wi-Fi BSSID location tri-angulation [INFERENCE].	[INFERENCE]
High Cloud GPU Costs for Live AI Video Processing	Cost / Scalability	High	Medium	Medium	Implement frame-sampling (analyze 1 frame every 5–10 seconds) and run AI on-demand or upon motion triggers rather than continuous 24/7 decoding [RESEARCHED].	[RESEARCHED]
Field Mobile Battery Drain During Inspections	Usability	Medium	Medium	Medium	Offload all AI processing to server backend; keep mobile background tasks lightweight and optimize GPS polling intervals [INFERENCE].	[INFERENCE]
Resistance / Non-Compliance from Grantee NGOs	Adoption / Policy	High	High	High	Integrate CCTV uptime and inspection compliance scores directly into e-Anudaan GIA grant release eligibility rules [INFERENCE].	[INFERENCE]

21. Cost & Scalability

Hackathon Prototype Cost Breakdown

Cloud Infrastructure: $0 (Runs entirely on local development laptops using Docker Compose) [ASSUMPTION].
Mapping & GIS APIs: $0 (Uses open-source OpenStreetMap tiles and Leaflet.js) [INFERENCE].
Media Gateway & Database: $0 (Uses open-source MediaMTX, PostgreSQL, Redis, MinIO) [RESEARCHED].
Total Hackathon Cost: ₹0 / $0 [INFERENCE].

Production System Deployment Cost Breakdown (Scale: 5,000 Institutes, 500 PMU Inspectors)

Cloud Compute (AWS/NIC Cloud - Cloud Control Planes): 4x Compute Instances (8 vCPU, 32GB RAM) for API Gateway, FastAPI, PostgreSQL, and MediaMTX - approx. ₹35,000 / month [INFERENCE].
AI Inference Acceleration: 2x GPU Instances (Nvidia T4 / L4) for frame-sampled YOLOv11 processing - approx. ₹45,000 / month [INFERENCE].
Object Storage (MinIO / S3 for Evidence Archives): 10 TB encrypted storage - approx. ₹15,000 / month [INFERENCE].
Bandwidth Egress: Managed via WebRTC on-demand pulling (costs minimized by 85% compared to continuous streaming) [RESEARCHED].
Total Estimated Production Cost: ~₹95,000 – ₹1,20,000 / month [INFERENCE].

22. Differentiation

Dimension	Generic SIH Submissions	Our Proposed Solution	Requirements Justification
CCTV Architecture	Embeds static YouTube / HTTP video links or assumes direct RTSP browser playback (which fails natively) [RESEARCHED].	Uses an On-Demand MediaMTX RTSP-to-WebRTC gateway delivering sub-second latency while saving 90% bandwidth [RESEARCHED].	REQ-01: Live CCTV Feed Integration [PS]
Inspection Duty Allocation	Basic manual dropdown selection or naive random number generation [INFERENCE].	Google OR-Tools spatial-temporal constraint optimization eliminating inspector-NGO collusion [INFERENCE].	REQ-05: Random Assignment through AI/Automation [PS]
Evidence Tamper Resistance	Standard photo upload from phone gallery (easily spoofed with old photos) [INFERENCE].	Hardware-enforced camera lock, GPS anti-mocking, EXIF timestamp verification, and SHA-256 cryptographic payload signing [INFERENCE].	REQ-06: Geo-Tagged Reports and Live Evidence Capture [PS]
Beneficiary Verification	Manual paper registers or text form entries [INFERENCE].	Instant unannounced WebRTC video call connectivity + AI Biometric Anti-Proxy verification [RESEARCHED].	REQ-02: Random VC Connectivity & REQ-07: AI Attendance Analytics [PS]

23. Future Scope

Automated Drone Inspections for Large Infrastructure Projects: Integration with autonomous UAVs for aerial perimeter and structural compliance verification of large SHRESHTA residential school campuses [INFERENCE].
Edge AI Hardware Modules: Deploying ultra-low-cost Edge AI microcontrollers (e.g., Raspberry Pi / Jetson Nano) directly inside facility camera junction boxes to perform local headcount and tamper analysis without sending raw video over WAN [RESEARCHED].
Integration with Bhashini Voice AI: Multi-lingual voice assistance in regional languages (Hindi, Tamil, Telugu, Bengali, Marathi) for PMU field inspectors to complete inspection checklists via natural voice commands [RESEARCHED].
Blockchain Audit Trail Ledger: Anchoring SHA-256 inspection report hashes onto a public/permissioned government blockchain ledger (e.g., NIC Blockchain) for immutable legal evidence presentation in courts [INFERENCE].

24. Assumptions & Open Items Log

Consolidated Assumptions

[ASSUMPTION-01]: Grantee NGOs have at least one ONVIF/RTSP-compliant IP camera installed in main common areas with standard internet connectivity (minimum 2 Mbps upload).
[ASSUMPTION-02]: PMU field inspectors are equipped with Android smartphones running Android 10 or higher with functioning GPS, camera, and cellular data.
[ASSUMPTION-03]: Production integration with e-Anudaan and PFMS will occur via REST API webhooks provided by NIC during national deployment.

Consolidated Open Items (Needs Validation)

[NEEDS VALIDATION-01]: Exact network firewall rules and static/dynamic IP allocations across state-level DoSJE regional offices for RTSP stream routing.
[NEEDS VALIDATION-02]: Final legal clearance under the DPDP Act 2023 regarding consent frameworks for CCTV surveillance in de-addiction and rehabilitation facilities (NAPDDR).

Final Recommendation

The student team should definitely choose and solve SIH26095. The problem statement addresses a critical real-world governance gap in grant disbursement and facility oversight across India. The proposed Hybrid Edge-Cloud Automated Platform (Approach 1) offers the perfect technical depth—combining mobile app engineering, WebRTC real-time video streaming, spatial AI optimization, and computer vision—making it a standout candidate for SIH victory [INFERENCE].

Recommended Stack

Mobile: Flutter (Dart) + Hive Local DB + Native Camera Geotag Plugin
Web Admin: Next.js (React) + Tailwind CSS + Leaflet GIS
Backend API: FastAPI (Python) + PostgreSQL / PostGIS + Redis
Media Server: MediaMTX (RTSP to WebRTC SFU Gateway)
AI Core: PyTorch + YOLOv11 + OpenCV + Google OR-Tools

Biggest Dependency

Availability of RTSP-compliant CCTV camera feeds (or realistic local RTSP stream simulation using MediaMTX and sample video loops) [RESEARCHED].

Biggest Risk

Cellular network dead-zones at remote rural facility sites blocking live video streaming or inspection report upload [INFERENCE]. Mitigated via offline-first Hive storage and automatic background delta-sync [INFERENCE].

Biggest Technical Challenge

Achieving sub-second WebRTC video streaming from IP cameras located behind restrictive NAT/firewalls without incurring high cloud server egress costs [RESEARCHED]. Solved via MediaMTX On-Demand RTSP pulling [RESEARCHED].

First 5 Actions

Action 1: Spin up a local MediaMTX Docker container and test streaming a sample MP4 file over RTSP to a WebRTC web browser player [RESEARCHED].
Action 2: Bootstrap the FastAPI backend with PostgreSQL + PostGIS extension and create the core database schema [INFERENCE].
Action 3: Initialize the Flutter mobile application project and integrate the native location and camera plugins with EXIF metadata extraction [INFERENCE].
Action 4: Implement the Next.js Web Admin dashboard skeleton with an interactive Leaflet map displaying facility markers [INFERENCE].
Action 5: Build the PyTorch YOLOv11 headcount inference script and link it to the FastAPI media pipeline via WebSockets [RESEARCHED].

Validation Before Coding

Verify that MediaMTX successfully transcodes local RTSP video streams to browser-playable WebRTC (WHEP) format with less than 1-second latency on a standard Wi-Fi network [RESEARCHED].

Buildability Score /100

92 / 100 (Extremely high buildability for a 4–6 student team using modern open-source frameworks like Flutter, FastAPI, MediaMTX, and YOLOv11) [INFERENCE].

Confidence

HIGH (Supported by direct problem statement mapping, established government portal research, and standard WebRTC surveillance architecture practices) [RESEARCHED].

Research Sources

Title: eAnudaan NGO Grant Portal - Department of Social Justice and Empowerment
- URL: https://grants-msje.gov.in/
- Source Type: Official Government Portal
- Supported Claim: Confirms existing DoSJE digital infrastructure for NGO grant processing and PMU inspection mandates.
- Reliability Assessment: High (Official Ministry Portal).
Title: MoSJE Unified Portal - Digital India / NeGD
- URL: https://negd.gov.in/our_projects/mosje-unified-portal-unified-digital-platform-for-the-department-of-social-justice-and-empowerment/
- Source Type: Official Government Documentation
- Supported Claim: Confirms ongoing integration of 33+ DoSJE portals with GIS-based inspection modules and real-time monitoring dashboards.
- Reliability Assessment: High (MeitY / NeGD Official Site).
Title: GIA (Grant-in-Aid Inspection App) - DoSJE Google Play Store Listing
- URL: https://play.google.com/store/apps/details?id=com.irform
- Source Type: Official Mobile App Store Registry
- Supported Claim: Confirms existing PMU field inspection app (com.irform) used for NAPDDR and GIA scheme monitoring.
- Reliability Assessment: High (Official DoSJE Developer Account).
Title: IP Camera App for Android: 2026 Integration Guide & Media3 Architecture
- URL: https://www.forasoft.com/blog/article/integrate-android-apps-ip-cameras
- Source Type: Technical Engineering Documentation
- Supported Claim: Validates RTSP-to-WebRTC media gateway architecture (MediaMTX / go2rtc) for sub-second live streaming on mobile/web.
- Reliability Assessment: High (Reputable Video Streaming Engineering Firm).
Title: MediaMTX - Ready-to-use Real-Time Media Server
- URL: https://github.com/bluenviron/mediamtx
- Source Type: Open-Source Technology Repository
- Supported Claim: Confirms zero-dependency RTSP, WebRTC, WHEP, and HLS media proxy server capability for live CCTV streams.
- Reliability Assessment: High (Widely Adopted Open-Source Project).

02 / What our

DEVELOPERS SAY

What we think about this platform, and how it can help people solve SIH problems.

" 30M+ Google Gemini tokens went into researching, analyzing, and understanding all 233 SIH problem statements to make them easier to explore and solve. "

Satyam Kumar

CEO , Full Stack Developer

" The platform makes SIH problem statements much easier to understand. Having everything from the problem details to the procedure in one place. "

Rishabh Ranjan

COO , Content Writer

" Oh, you got ads? Well, we need to make money too! But, you can use our platform to help you win SIH and take home the prize money. "

Himanshu Kumar

CMO , Product Reviewer

03 / Are You

READY TO
BUILD?

Get in touch if you need help bringing your ideas to life, or simply have an interesting idea you'd like to talk through. I'm always open to a conversation and exploring new possibilities together.

[email protected]