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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Green Communication for Underwater Wireless Sensor Networks: Triangle Metric Based Multi-Layered Routing Protocol.

Ahmad M Khasawneh¹, Omprakash Kaiwartya², Jaime Lloret^3,4

¹Department of Mobile Computing, Amman Arab University, Amman 11953, Jordan.

Sensors (Basel, Switzerland)

|December 23, 2020

Summary

We introduce a new routing protocol for underwater wireless sensor networks (UWSNs) called TM2RP. This protocol enhances network lifetime and efficiency by balancing energy consumption using supernodes and depth information.

Keywords:

link quality pressure sensors residual energy sink node supernodes triangle metric underwater wireless sensor network

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Area of Science:

Computer Science
Networking
Wireless Sensor Networks

Background:

Underwater wireless sensor networks (UWSNs) face unique challenges including limited energy and harsh environments.
Existing routing protocols often struggle with efficient energy management and reliable data delivery in UWSNs.

Purpose of the Study:

To propose a novel non-localization routing protocol for UWSNs named Triangle Metric based Multi-layered Routing Protocol (TM2RP).
To enhance energy efficiency and network lifetime in UWSNs through intelligent routing decisions.

Main Methods:

Developed TM2RP, a multi-layered, multi-metric protocol utilizing supernodes, depth, residual energy, and link quality.
Evaluated TM2RP performance using the aqua-sim package within the ns-2 simulator.
Compared TM2RP against Depth Based Routing (DBR) and Multi-layered Routing Protocol (MRP).

Main Results:

TM2RP demonstrated superior performance compared to DBR and MRP.
Achieved significant improvements in energy consumption and network lifetime.
Showcased enhanced packet delivery ratio and reduced end-to-end delay.

Conclusions:

TM2RP offers a more reliable and energy-efficient solution for UWSN routing.
The integration of link quality and residual energy optimizes next-hop selection.
TM2RP effectively addresses key performance metrics in UWSNs.