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| Description: | Student Innovation-Solutions could be in the form of waste segregation, disposal, and improve sanitization system. |
| Organization: | AICTE |
| Department: | AICTE, MIC-Student Innovation |
| Category: | Software |
| Theme: | Clean & Green Technologytd> |
| Youtube Link: | Not provided |
| Dataset Link: | Not provided |
| Contact info: | Not provided |
Problem Statement ID: SIH26195
Title: Student Innovation-Solutions could be in the form of waste segregation, disposal, and improve sanitization system.
Rapid urbanization and population growth in Indian municipalities have strained legacy waste management and public sanitation infrastructures [KNOWN]. The Ministry of Environment, Forest and Climate Change (MoEF&CC) has mandated four-stream source segregation (Wet, Dry, Sanitary, and Special Care Waste) under the Solid Waste Management (SWM) Rules 2026 [RESEARCHED]. However, municipal local bodies (ULBs) struggle with low source segregation compliance, unverified door-to-door waste collection logistics, and unmonitored public sanitation facilities [INFERENCE]. This dossier provides a comprehensive technical architecture and execution blueprint for SwachhDrishti (CleanFlow)—an offline-first, edge-AI powered, multi-stakeholder civic operating platform. The system combines web-based edge computer vision for real-time citizen waste classification, lightweight mobile applications for waste collectors, automated public toilet hygiene scorecards via computer vision proof, and an integrated GIS dashboard for Urban Local Bodies (ULBs).
| Field | Value | Confidence Tag |
|---|---|---|
| PS Number | SIH26195 | [PS] |
| Title | Student Innovation-Solutions could be in the form of waste segregation, disposal, and improve sanitization system. | [PS] |
| Organization | AICTE | [PS] |
| Department | AICTE, MIC-Student Innovation | [PS] |
| Category | Software | [PS] |
| Theme | Clean & Green Technology | [PS] |
| Deadline | 30 September 2026 | [PS] |
| Ideas Submitted / Cap | 1 / 500 | [PS] |
| Dataset Link | Not Provided (N/A) | [PS] |
To deliver an integrated, offline-resilient digital infrastructure that automates household waste segregation guidance via browser-based edge AI, verifies door-to-door waste disposal logistics using geotagged proof, and continuously monitors public sanitation facility cleanliness for municipal administration [INFERENCE].
The explicit problem statement description reads:
"Student Innovation-Solutions could be in the form of waste segregation, disposal, and improve sanitization system." [PS]
This problem breaks down into three core functional verticals:
┌─────────────────────────────────────────┐
│ SIH26195 Scope │
└────────────────────┬────────────────────┘
│
┌─────────────────────────────────┼─────────────────────────────────┐
▼ ▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Waste Segregation│ │ Waste Disposal │ │Sanitization Syst.│
└────────┬─────────┘ └────────┬─────────┘ └────────┬─────────┘
│ │ │
• Source Sorting • Door-to-Door • Public Toilet
• 4-Stream Rules Logistics Proof Cleanliness
• Edge-AI Guidance • MRF Facility Route • Inspection Proof
• User Incentive • Dump Tracking • SLA Escalation
Sub-Problem 1: Waste Segregation (Source Classification & Awareness)
Sub-Problem 2: Waste Disposal & Logistics Tracking
Sub-Problem 3: Public Sanitization System Improvement
| Stakeholder Group | Primary Goal | Current Pain Point | Required System Need |
|---|---|---|---|
| Citizens & Households | Easily identify correct waste stream and receive timely pickup notifications [INFERENCE]. | Confusion over segregation categories; inconsistent pickup times; dirty public toilets [KNOWN]. | Real-time camera-based segregation guide; pickup ETA alerts; quick QR-code feedback for public toilets [INFERENCE]. |
| Sanitation Workers / Drivers | Log daily door-to-door pickups and report route blockages without complex input [INFERENCE]. | Poor connectivity; complex apps; manual paper logs; physical fatigue [KNOWN]. | One-tap offline app; multilingual voice/icon interface; automatic QR scanning for house pickups [INFERENCE]. |
| Ward Sanitary Inspectors | Monitor worker attendance, waste collection compliance, and toilet hygiene SLAs [INFERENCE]. | Lack of field visibility; fake/unverified manual reports; delayed citizen grievance resolution [KNOWN]. | Real-time GIS ward map; geotagged photo proof verification; automated SLA breach alerts [INFERENCE]. |
| ULB / Municipal Administrators | Maximize Swachh Survekshan rankings; optimize vehicle fuel; enforce SWM Rules 2026 [RESEARCHED]. | High operational fuel costs; low segregation compliance; unmonitored legacy waste dumps [KNOWN]. | City-wide dashboard with heatmaps, waste stream analytics, vehicle route efficiency, and open API access [INFERENCE]. |
| Recycling & MRF Facility Operators | Receive pre-segregated dry waste streams to maximize resource recovery [RESEARCHED]. | Mixed waste contamination; manual sorting costs; hazardous materials mixed in recyclables [KNOWN]. | Inbound waste volume forecasting; batch tracking of incoming vehicle streams [INFERENCE]. |
| Feature ID | Proposed System Feature | Source Requirement | Justification & Implementation |
|---|---|---|---|
| FEAT-01 | Browser Edge-AI Waste Classifier | Waste Segregation [PS] | Provides instantaneous (<100ms) item identification without cloud latency or data charges, classifying waste into Wet, Dry, Sanitary, and Hazardous [INFERENCE]. |
| FEAT-02 | Household & BWG QR Identification | Waste Disposal [PS] | Unique QR stickers placed at household entrances allow collectors to log pickups and mark segregation compliance [INFERENCE]. |
| FEAT-03 | Collector Route & Pickup PWA | Waste Disposal [PS] | Enables offline logging of collection routes, geotagged dump alerts, and vehicle fill-level reporting [INFERENCE]. |
| FEAT-04 | Public Toilet QR & AI Inspector | Improve Sanitization System [PS] | QR codes inside public toilets allow citizens to report issues; caretakers upload geotagged proof of cleaning verified by AI [INFERENCE]. |
| FEAT-05 | ULB GIS Command Center | Waste Segregation, Disposal & Sanitization [PS] | Centralized dashboard displaying Garbage Vulnerable Points (GVPs), toilet cleanliness heatmaps, and route completion % [INFERENCE]. |
| FEAT-06 | Open311 Standard API Bridge | System Scalability & Governance [INFERENCE] | Ensures inter-operability with national platforms like Swachhata-MoHUA and municipal ERPs [RESEARCHED]. |
Connectivity Realities:
Hardware & Operating Device Constraints:
Language & Digital Literacy:
Physical Field Conditions:
Regulatory Alignment:
| Data Element | Purpose | Ideal Real-World Source | Hackathon Substitute | Access / Acquisition | Availability | Privacy Level | Confidence |
|---|---|---|---|---|---|---|---|
| Waste Image Classification Set | Train AI to recognize wet, dry, sanitary, and hazardous waste. | TACO Dataset, TrashNet, CPCB Waste Repository [RESEARCHED]. | Combined TACO + TrashNet + Kaggle Waste sub-datasets (~12,000 images) [RESEARCHED]. | Open Source Download via Roboflow / Kaggle [RESEARCHED]. | High [KNOWN] | Public / Non-PII | [RESEARCHED] |
| Household GIS & QR Mapping | Associate collection events with specific households or BWGs. | ULB Municipal Property Tax Database [KNOWN]. | Synthetic GeoJSON generated for 100 sample households across 2 wards [ASSUMPTION]. | Script-generated synthetic dataset [ASSUMPTION]. | High (Synthetic) | Mock PII | [ASSUMPTION] |
| Collection Routes & Vehicle GPS | Track waste vehicle coverage and transit to MRF/landfills. | Vehicle Tracking System (VTS) GPS Telematics [KNOWN]. | GPX/JSON simulation files created via OpenStreetMap route generator [ASSUMPTION]. | Generated route simulation scripts [ASSUMPTION]. | High (Simulated) | Non-PII | [ASSUMPTION] |
| Public Toilet Infrastructure Registry | Map public toilets, facilities, and caretaker assignments. | SBM Urban / MyToilet Portal API [RESEARCHED]. | Synthetic JSON dataset mapping 10 public sanitation blocks [ASSUMPTION]. | Manual schema definition [ASSUMPTION]. | High (Synthetic) | Public Info | [ASSUMPTION] |
| Sanitation Inspection Photos | Train/verify toilet hygiene visual evaluation. | Sanitation Inspector photo archives [NEEDS VALIDATION]. | Custom collected sample photos (clean vs uncleaned floor/urinal) [ASSUMPTION]. | Mobile camera capture [ASSUMPTION]. | High (Custom) | Public Space | [ASSUMPTION] |
┌────────────────────────────────────────────────────────────────────────┐
│ EXISTING ECOSYSTEM GAPS │
├───────────────────────┬────────────────────────┬───────────────────────┤
│ Swachhata-MoHUA │ Recykal / SWM │ Smart IoT Bins │
│ Grievance App │ Enterprise │ (Ultrasound Sensors) │
├───────────────────────┼────────────────────────┼───────────────────────┤
│ • Reactive reporting │ • Commercial focus │ • High hardware cost │
│ • No source sorting │ • Missing public │ • Battery maintenance │
│ guidance │ sanitation link │ failures │
│ • High cloud latency │ • No edge-AI sorting │ • Vandalism risk │
└───────────┬───────────┴───────────┬────────────┴───────────┬───────────┘
│ │ │
└───────────────────────┼────────────────────────┘
▼
┌────────────────────────────────────────────────────────────────────────┐
│ PROPOSED: SwachhDrishti │
│ Unified, Offline-First, Edge-AI Source Segregation + Sanitation Platform│
└────────────────────────────────────────────────────────────────────────┘
Swachhata-MoHUA Official App (MoHUA / Janaagraha)
Commercial SWM ERPs (e.g., Recykal, Saahas Zero Waste)
Hardware-Heavy Smart IoT Bin Systems
+-----------------------------------------------------------------------------------+
| CANDIDATE ARCHITECTURE COMPARISON |
+-----------------------------------+-----------------------------------------------+
| Approach A: Cloud-AI Mobile App | High Latency, Dependent on 4G, High Cloud Cost|
+-----------------------------------+-----------------------------------------------+
| Approach B: IoT Hardware Bins | High CAPEX, Maintenance Failure, Vandalism |
+-----------------------------------+-----------------------------------------------+
| Approach C: Unified Edge-AI + PWA | Zero Cloud AI Cost, Offline First, High Scale |
| (RECOMMENDED) | |
+-----------------------------------+-----------------------------------------------+
┌─────────────────────────────────┐
│ Approach C: Unified Platform │
└────────────────┬────────────────┘
│
┌──────────────────────────────────────┼──────────────────────────────────────┐
▼ ▼ ▼
┌─────────────────────────┐ ┌─────────────────────────┐ ┌─────────────────────────┐
│ Browser Edge-AI Sorting │ │ Offline Collector PWA │ │ AI Sanitation Inspector │
│ • Quantized YOLOv8/ONNX │ │ • IndexedDB Sync │ │ • Visual Proof Check │
│ • Zero Cloud Cost │ │ • QR Code House Scan │ │ • SLA Timers & Escalation│
│ • Instant Feedback │ │ • Route Tracking │ │ • Public QR Feedback │
└─────────────────────────┘ └─────────────────────────┘ └─────────────────────────┘
+-----------------------------------------------------------------------------------+
| 1. USER INTERACTION & FRONTEND LAYER |
| • Citizen Web App (Next.js / Tailwind / PWA) |
| • Field Worker & Driver App (React Native / Offline PWA) |
| • Sanitation Caretaker Portal & ULB GIS Command Center (React + Leaflet.js) |
+-----------------------------------------------------------------------------------+
│
▼
+-----------------------------------------------------------------------------------+
| 2. EDGE COMPUTE & CLIENT AI ENGINE |
| • ONNX Runtime Web / TensorFlow.js Engine |
| • Quantized YOLOv8n SWM-2026 Model (4-Stream Waste Classifier) |
| • Offline Storage (IndexedDB / RxDB with Web Workers) |
+-----------------------------------------------------------------------------------+
│ (REST / WebSockets / GraphQL)
▼
+-----------------------------------------------------------------------------------+
| 3. API GATEWAY & MICROSERVICES LAYER (Node.js / Express / Python FastAPI) |
| • Auth & RBAC Service (JWT / Firebase Auth / OAuth2) |
| • Waste Logistics & QR Verification Microservice |
| • Sanitation Facility & Caretaker Audit Microservice |
| • GIS Routing & Heatmap Microservice (OpenRouteService / PostGIS) |
| • Open311 API Compatibility Gateway |
+-----------------------------------------------------------------------------------+
│
▼
+-----------------------------------------------------------------------------------+
| 4. DATA PERSISTENCE & ANALYTICS LAYER |
| • Primary DB: PostgreSQL + PostGIS (Spatial Indices for Wards & Routes) |
| • Cache & Queue: Redis (Session, Rate Limiting, Geo-Spatial Indexing) |
| • Object Storage: AWS S3 / MinIO (Geotagged Proof Photos, Compressed WebP) |
+-----------------------------------------------------------------------------------+
[Citizen] ──1. Point Camera──► [WebAssembly / Edge AI Engine] ──2. Instant Classify──► [4-Stream Result]
│
3. Household Disposal
│
▼
[Collector PWA] ◄──5. Sync Log ─── [Offline IndexedDB] ◄──4. Scan House QR Code ◄── [Door Pickup]
│
6. Network Restored
│
▼
[API Gateway] ──7. PostGIS Insert──► [PostgreSQL Spatial DB] ──8. WebSockets──► [ULB GIS Dashboard]
swachhdrishti.app in any web browser.12.4 MB)./api/v1/logistics/collect.| Layer | Selected Technology | Why Chosen | Alternative Considered | Why Alternative Rejected |
|---|---|---|---|---|
| Frontend Framework | Next.js 14 (React) + Tailwind CSS | Server-side rendering for landing pages + fast PWA capabilities for app views [KNOWN]. | Angular / Vue.js | Slower mobile loading and larger bundle size compared to Next.js [INFERENCE]. |
| Edge AI Engine | ONNX Runtime Web (WASM + WebGL) | Runs model locally in browser at near-native C++ performance with hardware acceleration [RESEARCHED]. | Cloud TensorFlow API | Cloud API requires continuous internet and incurs high per-request API costs [INFERENCE]. |
| Field Mobile App | React Native (Expo) | Cross-platform (Android/iOS), easy offline SQLite integration, direct camera access [KNOWN]. | Native Android (Java/Kotlin) | Requires double code development effort for iOS and takes longer for student teams [INFERENCE]. |
| Backend API | Node.js (Express) + TypeScript | Fast async I/O, shared TypeScript interfaces with frontend, high throughput [KNOWN]. | Python Django | Heavy framework overhead; higher latency for real-time WebSocket sync [INFERENCE]. |
| Database | PostgreSQL + PostGIS extension | Gold standard for open-source geospatial GIS queries (ward boundary intersections, spatial buffering) [KNOWN]. | MongoDB GeoJSON | Lacks robust spatial SQL joining and multi-table ACID transactions for municipal audits [INFERENCE]. |
| Cache & Queue | Redis | In-memory spatial pub/sub for real-time vehicle movement tracking and rate limiting [KNOWN]. | Memcached | Lacks native spatial indexing and data persistence features [INFERENCE]. |
| Object Storage | Cloudflare R2 / AWS S3 | S3-compatible, low-cost storage for inspection proof images [KNOWN]. | Local Disk File System | Ephemeral containers in cloud deployments lose locally stored files on restart [KNOWN]. |
| GIS Mapping | Leaflet.js + OpenStreetMap | Free, open-source, lightweight map renderer with custom tile layer support [KNOWN]. | Google Maps API | Expensive tile and geocoding API billing limits for student hackathons [INFERENCE]. |
+-----------------------------------------------------------------------------------+
| AI/ML UTILIZATION MATRIX |
+----------------------------------+------------------------------------------------+
| Component 1: Waste Classifier | USED: YOLOv8n ONNX Web (Instant Edge Detection)|
+----------------------------------+------------------------------------------------+
| Component 2: Toilet Cleanliness | USED: MobileNet V3 Classification (Proof Check)|
+----------------------------------+------------------------------------------------+
| Component 3: Vehicle Routing | NOT USED: Rule-Based Dijkstra / OR-Tools |
| | (Deterministic & Predictable) |
+----------------------------------+------------------------------------------------+
12.4 MB).640x640x3 RGB tensor). +----------------------+ +----------------------+
| households | | pickup_logs |
+----------------------+ +----------------------+
| id (PK, UUID) |1 *| id (PK, UUID) |
| property_id (VARCHAR)|◄─────────| household_id (FK) |
| qr_code_hash (VARCHAR| | worker_id (FK) |
| ward_number (INT) | | status (ENUM) |
| geom (GEOMETRY-Point)| | synced_at (TIMESTAMP)|
+----------------------+ +----------------------+
▲
│*
+----------------------+
| field_workers |
+----------------------+
| id (PK, UUID) |
| name (VARCHAR) |
| ward_id (INT) |
+----------------------+
-- PostgreSQL / PostGIS Schema Overview
CREATE EXTENSION IF NOT EXISTS postgis;
CREATE TYPE waste_stream AS ENUM ('WET', 'DRY', 'SANITARY', 'HAZARDOUS', 'UNSEGREGATED');
CREATE TYPE ticket_status AS ENUM ('OPEN', 'IN_PROGRESS', 'RESOLVED', 'SLA_BREACHED');
CREATE TABLE households (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
property_id VARCHAR(50) UNIQUE NOT NULL,
qr_code_hash VARCHAR(64) UNIQUE NOT NULL,
ward_number INT NOT NULL,
address_text TEXT,
geom GEOMETRY(Point, 4326),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE pickup_logs (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
household_id UUID REFERENCES households(id),
worker_id UUID NOT NULL,
stream_collected waste_stream NOT NULL,
is_segregated BOOLEAN NOT NULL,
scanned_at TIMESTAMP NOT NULL,
device_synced_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
geom GEOMETRY(Point, 4326)
);
CREATE TABLE sanitation_facilities (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
facility_code VARCHAR(30) UNIQUE NOT NULL,
name VARCHAR(100) NOT NULL,
ward_number INT NOT NULL,
geom GEOMETRY(Point, 4326),
caretaker_id UUID
);
CREATE TABLE sanitation_tickets (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
facility_id UUID REFERENCES sanitation_facilities(id),
issue_category VARCHAR(50) NOT NULL,
status ticket_status DEFAULT 'OPEN',
reported_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
resolved_at TIMESTAMP,
proof_photo_url TEXT,
ai_cleanliness_score NUMERIC(5,2)
);
UUIDv4, a created_at_utc timestamp, and a hash sequence [INFERENCE].navigator.onLine listener), service worker fires POST request to /api/v1/sync carrying batch array [INFERENCE].property_id and scanned_at timestamp window. If duplicate scans exist for the same household within 4 hours, server deduplicates using client scan UUID, retaining the initial scan [INFERENCE].60% quality, max 1024px width), reducing raw camera file size from ~3.5MB down to ~120KB before queueing for transit [INFERENCE].Personal Data & Privacy Protection (DPDP Act 2023 Compliance):
property_id and spatial location; no citizen phone numbers, names, or personal identity details are tied to waste pickup scans [RESEARCHED].Authentication & Access Control (RBAC):
Data Encryption Standards:
Liability & AI Failure Disclaimers:
┌─────────────────────────────────────────────────────────────────────────┐
| HACKATHON MVP SCOPE |
├───────────────────────────────────┬─────────────────────────────────────┤
| IN SCOPE (MUST BUILD) | OUT OF SCOPE (PRODUCTION FUTURE) |
├───────────────────────────────────┼─────────────────────────────────────┤
| • Browser Edge-AI Waste Sorting | • Physical IoT Ultra-Sonic Bin |
| (WebAssembly / ONNX YOLOv8n) | Hardware Sensors |
| • Collector PWA with Offline | • Automated Biometric Staff |
| QR Scan & Sync Simulation | Attendance Hardware |
| • Public Toilet QR Code Citizen | • Direct Commercial Bank Payment |
| Feedback Flow | Gateway for Fine Levying |
| • Interactive ULB GIS Dashboard | • Live Satellite Dump Scanning |
| with Ward Heatmaps & Alerts | Integration |
| • Open311 Compatible API Endpoint | |
└───────────────────────────────────┴─────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────┐
| SIH LIVE DEMO STORYBOARD |
├──────┬───────────────────────┬──────────────────────────────────────────┤
| Step | Phase | Actions & Visual Output |
├──────┼───────────────────────┼──────────────────────────────────────────┤
| 1 | Citizen Waste Scan | Point phone camera at plastic bottle. |
| | | AI instantly displays "DRY WASTE - BLUE" |
| 2 | Offline Collection | Toggle Airplane Mode ON in Worker PWA. |
| | Scan | Scan 3 House QRs -> Saves to IndexedDB. |
| 3 | Data Sync | Toggle Airplane Mode OFF. |
| | | Auto syncs logs -> Background turns green|
| 4 | Public Sanitation | Scan Toilet QR code -> Log "Water Issue".|
| | | Caretaker uploads photo -> AI score 88%. |
| 5 | ULB Command Center | GIS Dashboard map auto-refreshes ward |
| | | metrics & shows resolved ticket badge. |
└──────┴───────────────────────┴──────────────────────────────────────────┘
Target Deadline: 30 September 2026 [PS]
Phase 1: Foundation (Weeks 1-2)
├── Data Collection (TACO/TrashNet)
├── YOLOv8 Model Training & Quantization
└── Database Schema Setup
Phase 2: Core Components (Weeks 3-4)
├── Edge AI Browser Integration
├── Offline PWA Storage & Sync
└── Backend API Microservices
Phase 3: Dashboard & Integration (Weeks 5-6)
├── GIS Leaflet Command Dashboard
├── Sanitation Caretaker Module
└── Open311 API Implementation
Phase 4: Testing & Demo Prep (Weeks 7-8)
├── Offline Field Simulation
├── Accuracy Optimization
└── SIH Pitch Deck & Demo Script
target < 15MB).| Risk Event | Likelihood | Impact | Severity | Mitigation Strategy |
|---|---|---|---|---|
| Edge AI Performance Lag on Low-End Phones | Medium | High | High | Fallback to ultra-light MobileNet classification or server-side compression endpoint when WASM frame rate drops below 10 FPS [INFERENCE]. |
| Worker Resistance to QR Scanning | High | High | High | Keep QR scan UI one-touch; introduce gamified daily collection completion badges for workers [INFERENCE]. |
| Clock Skew on Offline Devices | Medium | Medium | Medium | Server overrides client timestamp with monotonic sequence validation upon synchronization [INFERENCE]. |
| Fraudulent Caretaker Photo Uploads | High | Medium | Medium | Force live camera capture (disable gallery uploads); check photo EXIF hash and run AI surface validation [INFERENCE]. |
| Municipal API Integration Rejection | Medium | Low | Low | Expose standardized Open311 REST endpoints requiring zero internal database structural changes by ULBs [RESEARCHED]. |
+-----------------------------------------------------------------------------------+
| KEY SYSTEM DIFFERENTIATORS |
+-----------------------------------------------------------------------------------+
| 1. SWM Rules 2026 Ready: Native support for mandatory 4-stream segregation. |
| 2. Browser Edge-AI Compute: 0 Cloud Inference Cost + Instant Offline Guidance. |
| 3. Integrated Sanitation: Links Door Waste Logistics with Public Toilet Governance.|
| 4. Open311 Interoperability: Plugs into existing MoHUA & Municipal Dashboards. |
+-----------------------------------------------------------------------------------+
[ASSUMPTION-01]: WebAssembly / ONNX Runtime Web maintains acceptable execution speed across entry-level Android devices running Chrome/Edge browsers.[ASSUMPTION-02]: Municipalities can distribute weather-resistant QR code stickers to households during routine ward visits.[ASSUMPTION-03]: Synthetic datasets and OSM route generators sufficiently simulate field waste collection logistics for judging evaluation.[NEEDS VALIDATION-01]: AI visual inspection accuracy for public toilet floor cleanliness under low indoor lighting conditions.[NEEDS VALIDATION-02]: Formal approval process and API authentication keys required for live integration with MoHUA Swachhata-MoHUA servers.GO / SOLVE THIS PROBLEM. The problem statement SIH26195 presents an urgent civic need directly backed by national policy directives (SWM Rules 2026 & Swachh Bharat Mission 2.0) [RESEARCHED]. The recommended software-first approach (Approach C) requires zero custom hardware expenditure, achieves infinite client compute scalability, and delivers immediate value for SIH judging [INFERENCE].
Acquiring and training a high-quality multi-stream Indian waste image dataset covering regional packaging, organic waste types, and sanitary items [INFERENCE].
Client-side web browser hardware acceleration limitations on very low-end (<2GB RAM) field mobile phones [INFERENCE].
Implementing zero-data-loss offline sync queues with automatic server-side timestamp conflict resolution in poor network environments [INFERENCE].
92 / 100
(High feasibility: Pure software implementation leveraging robust open-source web frameworks, standard GIS components, and lightweight client-side AI) [INFERENCE].
HIGH
(The solution directly addresses all explicit requirements of SIH26195 and aligns with statutory SWM Rules 2026 specifications) [INFERENCE].
Title: Solid Waste Management Rules 2026 Notification
URL: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2219676
Source Type: Official Government Source (Press Information Bureau, MoEF&CC)
Supported Claim: Mandatory four-stream segregation (Wet, Dry, Sanitary, Special Care) coming into force April 1, 2026 [RESEARCHED].
Reliability: Authoritative / Tier-1.
Title: Swachhata-MoHUA Official App Architecture & Categories
URL: https://sbmurban.org/digital_Inovation
Source Type: Official Government Portal (Ministry of Housing and Urban Affairs)
Supported Claim: 18 grievance categories, location geotagging, and sanitary inspector workflow [RESEARCHED].
Reliability: Authoritative / Tier-1.
Title: TACO (Trash Annotations in Context) Dataset Specification
URL: http://tacodataset.org/
Source Type: Academic / Open Source Research Dataset
Supported Claim: Bounding box and segmentation annotations for waste in the wild [RESEARCHED].
Reliability: Highly Reliable / Tier-2.
Title: On-Device Waste Classification Performance (YOLOv8 & YOLOv11 Edge Benchmarks)
URL: https://www.researchgate.net/publication/398274842
Source Type: Peer-Reviewed Academic Publication (IJIGSP)
Supported Claim: Quantized YOLO edge models achieve acceptable mAP (>80%) on lightweight embedded and mobile web environments [RESEARCHED].
Reliability: Reliable / Tier-2.
Title: Open311 Standard Specification for Civic Service Requests
URL: https://www.open311.org/
Source Type: International Open Standards Body
Supported Claim: Open API specification for interoperable civic complaint and service request tracking [RESEARCHED].
Reliability: Authoritative Open Standard / Tier-1.
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