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The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions

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IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks.

Shujaat Ali¹, Muhammad Nadeem¹, Sheeraz Ahmed²

¹Department of Computer Science, Faculty of Computing and Information Technology International, Islamic University, Islamabad, Pakistan.

Scientific Reports

|October 15, 2024

Summary

This summary is machine-generated.

This study introduces a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) for acoustic sensor networks. REEFSM significantly cuts energy use and boosts data reliability for underwater environmental monitoring.

Keywords:

Energy efficient Scheme Neighbor Discovery Algorithm Reliable Communication Sensor Network.Underwater Acoustic Network

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Area of Science:

Environmental Science
Oceanography
Computer Science

Background:

Increasing demand for environmental monitoring and oceanographic research.
Challenges in energy efficiency and data transfer reliability in Acoustic Sensor Networks (ASNs).
Limitations of existing methods in addressing energy constraints and unreliable data transmission.

Purpose of the Study:

To propose a novel framework, REEFSM, for enhancing energy efficiency and data reliability in ASNs.
To optimize energy consumption and improve data transmission in underwater sensor networks.
To provide a sustainable solution for energy-efficient data collection in aquatic environments.

Main Methods:

Development of a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM).
Implementation of advanced energy management strategies: adaptive duty cycling and efficient data transmission.
Minimizing the number of forwarding nodes to reduce energy expenditure.

Main Results:

REEFSM reduces energy consumption by up to 43%.
Data reliability is increased by 35% compared to EERBCR and DEADS protocols.
Ensured zero dead nodes, minimized packet drops, and maintained high data accuracy.

Conclusions:

REEFSM offers a sustainable and reliable solution for underwater acoustic sensor networks.
The framework effectively addresses energy and reliability challenges in environmental monitoring.
Future work includes integrating autonomous path planning, energy harvesting, and machine learning.