CMOS: efficient clustered data monitoring in sensor networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are crucial for large-scale data collection.
• Energy limitation is a critical challenge for battery-powered sensors, especially in outdoor environments.
• Data transmission consumes significantly more energy than computation in sensor nodes.

Purpose of the Study:

• To present an energy-efficient approximate data gathering technique for WSNs.
• To reduce the overall energy consumption of sensor nodes.
• To ensure sensor readings are obtained within a defined error bound.

Main Methods:

• Developed a Kalman filter-based technique named CMOS (Cluster-based Approximate Data Gathering).
• Grouped spatially close sensors into clusters.
• Implemented an energy-efficient clustering method to balance energy load among cluster headers.

Main Results:

• CMOS enables efficient data gathering by using cluster headers to generate approximate readings.
• The proposed clustering method effectively distributes energy consumption.
• Simulation results demonstrate the efficiency and accuracy of the CMOS technique.

Conclusions:

• CMOS offers an effective solution for reducing energy consumption in WSNs.
• The technique allows for efficient data retrieval while maintaining acceptable accuracy.
• This approach is particularly beneficial for WSN applications with limited power resources.