Efficient utilization of energy resources is always the challenge in the field of Wireless Sensor Networks (WSNs). Besides energy utilization, the integration of internet of thing (IoT) with WSNs cause the issue of spectrum utilization in WSNs. There is an exponential growth in the data, which may b
Prediction based data reduction and controlled transmission in WSN for weather forecasting
Efficient utilization of energy resources is always the challenge in the field of Wireless Sensor Networks (WSNs). Besides energy utilization, the integration of internet of thing (IoT) with WSNs cause the issue of spectrum utilization in WSNs. There is an exponential growth in the data, which may be hindered if no control over the data generation is taken. One solution for both problems could be that the part of the sensed data can be predicted without triggering transmissions and congesting the wireless medium.
Our project proposes an energy efficient technique by using predictive models to predict future data which controls the data transmission. Instead of transmitting sensed data at every instant, we are implementing data prediction algorithm which continues to output data until or unless sensed data is changed which halts the prediction system, transmits the data and updates the sink node.
The system will contain 2 transmitting nodes and 1 receiving node. The prediction algorithm is applied on both sides, firstly on transmission side which predicts the data and transmit if and only if when the input data crosses the predefined error bound.
The main objectives of this project are given below:
The benefits provided by our project are as follows:
The final deliverable will be a hardware based system of “Prediction based controlled transmission between WSN nodes” which would consists of 2 nodes with sensor to extract parameters and 1 node as receiver station.
The sensor nodes will send the sensed data to the receiver station in case of sudden change in values and the receiver station will update its parameters accordingly. After the change in parameters, the receiver station will continue to predict the value until it changes again.
Along with the hardware application of the project, we will also compare the parameters obtain from different prediction algorithms determine which produced the best results with help of software.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Arduino Uno dev board | Equipment | 3 | 800 | 2400 |
| XBee Communication Module(Pair) | Equipment | 3 | 10000 | 30000 |
| BME280 pressure, temperature, and humidity sensor | Equipment | 2 | 1200 | 2400 |
| Battery Backup | Equipment | 3 | 2500 | 7500 |
| Arduino Mountable Shield | Equipment | 3 | 1300 | 3900 |
| SD card + Circuit (Data Logging) | Equipment | 3 | 1500 | 4500 |
| Real time clock circuit | Miscellaneous | 3 | 800 | 2400 |
| Others | Miscellaneous | 10 | 200 | 2000 |
| Total in (Rs) | 55100 |
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