A collaborative intelligence framework for smart campus; the case of disastrous situation
The project focuses on using of collaborative intelligence in a real time environment, using a case of disastrous situation of fire. Here our goal is to tackle any barrier in the old approach only using sensors. We also see the use of mathematical model of the Bayesian rule to predict the fire
2025-06-28 16:24:58 - Adil Khan
A collaborative intelligence framework for smart campus; the case of disastrous situation
Project Area of Specialization Artificial IntelligenceProject SummaryThe project focuses on using of collaborative intelligence in a real time environment, using a case of disastrous situation of fire. Here our goal is to tackle any barrier in the old approach only using sensors. We also see the use of mathematical model of the Bayesian rule to predict the fire and the safest route along the way. We tackle all the limitations of the traditional systems like accuracy. This paper presents the concept of CI enhanced with the Bayesian model to work more accurately with the human input to provide real time guidance at time of disaster. The key challenge here to collaborate the sensors with the humans to provide such efficient system that leaves no room of error.
Project Objectives- To propose a disastrous communication framework for a smart campus.
- To propose a mathematical model for CI based disastrous smart campus use case.
- To design a fire disaster handling prototype for smart campus.
The data is collected from sensors and the human and is sent to ThingSpeak. The collected data is analyzed. After it is analyzed, it is then pre-processed. Pre-processing includes data integration, data transformation, data cleaning, data reduction and data prioritization. In the next step is the computing process using hybrid computing (Cloud and Edge) where the data is saved for further processing. Lastly the recommendation system based on the BBN makes suitable recommendations which are then displayed on the android-based app.
Benefits of the Project- Effective and efficient fire detection
- Most accurate evacuation routes
- No latency in detection and evacuation of fire
- Leaves no room for error
- Helps save lives of students, faculty and staff in fire disaster
- Can be implemented in large scale building
- A complete prototype of the system using sensors and equipment which presents whole evacuation and detection system
- Giving the complete guidance for evacuation using collaborative intelligence and the Bayesian network
- A complete fire detection using collaborative intelligence and Bayesian mathematical model
- Focusing on collaborative intelligence and Bayesian model to give accurate and efficient answers
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 40450 | |||
| Raspberry Pie | Equipment | 1 | 25000 | 25000 |
| Node MCU | Equipment | 3 | 2500 | 7500 |
| Heat and Humidity Sensor | Equipment | 3 | 350 | 1050 |
| Flame Sensor | Equipment | 3 | 300 | 900 |
| CO2 Sensor | Equipment | 3 | 1000 | 3000 |
| Prototype and Printing | Miscellaneous | 1 | 3000 | 3000 |