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Machine learning driven aggregation aware bitmap MAC protocol for energy efficient data transmission in WSNs.

Fuhid Alanazi¹, Mohammad N Alanazi²

¹Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, 42351, Saudi Arabia. alanazi@iu.edu.sa.

Scientific Reports

|October 22, 2025

Summary

This summary is machine-generated.

An Aggregation-Aware Energy-Efficient Bit-Mapping Medium Access Control Protocol (AABMP) reduces packet transmission in Wireless Sensor Networks. This method enhances energy efficiency by intelligently aggregating data and using machine learning to predict data deviation for transmission decisions.

Keywords:

Aggregation aware data transmission Bit mapping Medium access control Quality of service Transmission probability

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Area of Science:

Computer Science
Electrical Engineering
Networking

Background:

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) require energy-efficient data transmission protocols.
Existing Medium Access Control (MAC) protocols often struggle with balancing energy consumption and data transmission efficiency in WSNs.

Purpose of the Study:

To propose an Aggregation-Aware Energy-Efficient Bit-Mapping Medium Access Control Protocol (AABMP) for efficient data transmission in WSNs and IoT.
To reduce energy consumption in sensor nodes by minimizing unnecessary data transmissions.

Main Methods:

AABMP aggregates data by estimating the mean value of a sliding window and calculating the deviation of current readings.
Machine learning methods are employed to predict data deviation for transmission decisions.
The optimal machine learning method is integrated with the bit-mapping MAC protocol.

Main Results:

The aggregation-aware ML approach significantly reduces the number of transmitted packets by identifying redundant data.
Performance evaluation using the Intel LAB dataset demonstrates practical applicability and energy savings.
AABMP outperforms existing MAC protocols in terms of energy efficiency in various scenarios.

Conclusions:

AABMP offers a practical and energy-efficient solution for data transmission in IoT-oriented WSN deployments.
The proposed deviation-aware aggregation and ML integration effectively minimizes redundant data transmission.
The protocol demonstrates significant energy savings, making it suitable for resource-constrained WSN environments.