A Smart Ration Distribution System for Fraud Prevention, Real-Time Tracking, and Secure Transactions
The existing ration distribution faces numerous challenges, such imprecise product assignments, sluggish processing times, extended waiting periods, and pervasive corruption. These issues hinder beneficiaries from receiving their rightful goods in an equitable and timely fashion. To tackle these problems, we suggest a Smart Ration Distribution System that utilizes automation to enhance transparency, efficiency, and dependability. Our system substitutes traditional ration cards with Aadhaar-connected smart cards, facilitating secure and verified transactions while keeping a digital log of all distributions. This mitigates the chances of duplication and unauthorized access. Furthermore, our strategy moves past conventional methods by integrating state-of-the-art technologies: o Fraud detection powered by machine learning to spot suspicious activities and avert unauthorized usage. o Dem and forecasting driven by data to optimize stock availability and reduce shortages. o IoT-enabled inventory monitoring for real-time tracking of stock and effective resource management. o Decentralized ledger technology to bolster transaction security, ensuring transparency and data integrity. By incorporating these sophisticated technologies, our system lessens manual involvement, lowers errors, and dramatically decreases fraudulent actions. More importantly, it improves access to vital resources for those in greatest need. Successfully rolling out this system could transform the Public Distribution System (PDS), making it more efficient, secure, and user-friendly. Ultimately, this initiative aspires to establish a more equitable and accountable distribution network, benefiting both the government and the millions who rely on it.
Authors
- Vedant Jadhav
Publication Details
- Journal
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2025-04-21
- DOI
- https://doi.org/10.22214/ijraset.2025.68984
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00