CPAC: Energy-efficient data collection through adaptive selection of compression algorithms for sensor networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Sensor networks are crucial for data collection but face energy constraints.
• Optimizing energy efficiency is vital for extending network lifetime and performance.

Purpose of the Study:

• To propose a technique for optimizing energy efficiency in sensor networks through selective data compression.
• To determine optimal sensor node selection and compression algorithm usage for energy savings.

Main Methods:

• Formulating the optimization problem as binary integer programs.
• Developing an algorithm to make optimal decisions on sensor node compression execution and algorithm choice.
• Conducting simulations to evaluate energy consumption and performance.

Main Results:

• The proposed optimization algorithm significantly reduces network-wide energy consumption.
• Achieved 47% energy savings compared to the CTP protocol in stationary environments.
• Demonstrated superior performance in intermittent, high-interference networks, saving up to 55% energy.

Conclusions:

• Selective data compression is an effective strategy for enhancing sensor network energy efficiency.
• The developed optimization technique provides substantial energy savings, particularly in challenging network conditions.
• This approach offers a significant improvement over existing data collection protocols.