The Smart India Hackathon (SIH) problem statements is based on real life problems actually faced by the government of India different ministries, and solution proposed in them often lead to the real world implementation based on how good idea you have and how better implementation you have too.
So, I take the problem statement of the SIH of last year and try to think and build solution from my side just as a fun with this and I don't claim this is a good idea it just a try. These government problems is real world problems so students who is always telling about we want real world project ideas this is one of those kind problem statements which really improve your skills, this things actually work if you not win hackathon you definitely learn skills and build industry grade real world project for at least portfolio and there is no shortage of ideas .Also open for suggestion to improve my plans.
problem Statement
Title: Smart Community Health Monitoring and Early Warning System for Water-Borne Diseases in Rural Northeast India
Description: Water-borne diseases like diarrhea, cholera, typhoid and hepatitis A are common across rural and tribal belts of the Northeast, especially during monsoon, largely due to contaminated water sources and weak sanitation infrastructure. The ask is a Smart Health Surveillance and Early Warning System that: collects health data from local clinics, ASHA workers and community volunteers via mobile app or SMS; applies AI/ML to detect outbreak patterns from symptoms, water-quality reports and seasonal trends; integrates with water-testing kits or IoT sensors monitoring turbidity, pH and bacterial presence; pushes real-time alerts to district health officials; offers a multilingual interface for community reporting; and gives health departments a dashboard to visualize hotspots and allocate resources.
Requirements
Functional :
Receive data via smartphone app, basic SMS, and automated IoT sensor streams. Means the data come in bundle in one go.
Detect localized symptom spikes before clinical diagnoses are confirmed. the disease pattern using simulated data or trend analysis.
Push dynamic alerts to District Medical Officers (DMOs) based on severity thresholds. alerts through apps and SMS services.
GIS-mapped heatmaps showing active clusters and real-time hospital bed/medicine inventory. integrate the inventory and beds availability data and generate heat map after data simulation and AI trend detection.
Support for Assamese, Bengali, Bodo, Hindi, English,other local languages by having the language translation model for SMS and the app both.
NON-Functional :
The mobile app must allow data entry without internet and auto-sync immediately upon network reconnection.
IoT protocols and app payloads must be aggressively compressed (measured in bytes, not kilobytes).
Must adhere to India's DISHA (Digital Information Security in Healthcare Act) guidelines.
The ingestion and alerting APIs must maintain 99.99% uptime, especially during monsoon-triggered mass events.
Constraints :
Connectivity: Means You not continuously sync the data from the phone. So data will come in burst. due to high hills and forest regions or low connectivity regions.
Sensor Calibration: Due to weather conditions sensors information can be inaccurate. Sensor can be damaged or may be distorted from accuracy.
Literacy & UI/UX: Humans are not machines ASHA workers is also human if calibration is taking long time and feel like a exam no one will continue to it properly so solution need to be highly visual and quick data collection.
SMS Formatting: There is chance of the SMS based data collection contain local slang and typos.
Plan
The ASHA workers collect data from the _ React Native _ application. and the data is collected using the _ SQLite _ or _ WatermelonDB _, if there was no internet the data will be queue in database locally, if app is not working then SMS service.
Machine send the raw telemetry message, which we made compressed via _ Protocol Buffers _ over _ MQTT _.
when internet come ASHA worker data will sync not bombard way in a queue on main server without losing data. Simultaneously the backend also decompress the sensor data.
Add the Check on the machine data does it will sending distorted data we can mark it easily and filter out also we can figure out the which sensor is not good.
We add the massive data cleaning organising and sorting. also use different data science methods to get the appropriate data ,And save it into the database of _ MongoDB _ or _ PostgreSQL _ for feeding the AI.
Then the _ Node.js _ server sending the HTTP request to the isolated _ Python _ microservice where we Simulate the environment properly including this new data and various different real time government and scientific data like geographical regions, location, symptoms, weather, season, timeline, medical past record of the region, biodiversity, etc. using existing _ Python _ library which help to simulate data, and sharing of data mostly in real time using the _ Kafka _.
In there using those simulation data we can generate heat map of the location.
And the edge computing backend framework server take all real time data from _ MongoDB _ database and _ Python _ microservice and send it to the AI API endpoint. The AI analyze massive context and Generate localised outbreak risk score using mathematics and different operations.
Dashboard update and heat map updated of the admin side of the district health officials and also alerts is given to them through app and also SMS, why because low connectivity region this SMS is helpful.
Special care
We have to intentionally design the app for ASHA worker minimal and more visual so that it does not overwhelm them.
we intentionally have data in bytes not too big otherwise apply compressor and decompressor both end.
we use edge computing and edge networking technology so the information can be send from anywhere with minimum latency.
the communication between the sensor and the microservices is on _ Kafka _ so that real time data is collected even between other government geographical data and medical database.
we add the multilingual translator also in the express side so the SMS done in native side is easily predictable.
If possible then we enforce the native SMS structuring like this format if SMS come then more better.
we introduce the reward system but authentic one so ASHA workers is rewarded for the real data.
we demand the location information even there was a service which translate the address into the GPS coordinates easily from backend side.
for data from sensor we add _ Kafka _ to sit directly behind _ MQTT _ broker which queue capture incoming data instantly and holds safely into memory.
This plan is not sure perfect for the real world, try my best but also I don't consider cost effectiveness in this case of plan which is also a necessary part. and also testing things here too. which is considerable also. There was a mention of the input devices otherwise My plan was I should made the highly portable alerting systems which can do the alert anywhere easily using AI , like services already exist in the world. Humm. creating this scalable site is good and big project for the individual programmer I hope I may be develop this for now Please give suggestion if you have to improve this things. comment below and what you think is my plan good enough to execute.
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