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An Adjustable Wireless Backhaul Link Selection Algorithm for LEO-UAV-Sensor-Based Internet of Remote Things Network.

Rui Chen^1,2, Wennai Wang¹, Wei Wu¹

¹Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210049, China.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

This study introduces an adjustable algorithm for selecting wireless backhaul links in Internet of Remote Things (IoRT) networks. It optimizes data transfer between sensors, unmanned aerial vehicles (UAVs), and low-earth-orbit (LEO) satellites for better performance.

Keywords:

Internet of Remote Things (IoRT)backhaul link selection low-earth-orbit (LEO) satellite multiservices support unmanned aerial vehicle (UAV)

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Internet of Remote Things (IoRT) networks face challenges with dynamic backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites.
Rapidly changing link conditions due to high mobility impact data transfer and system performance.
Diverse Quality of Service (QoS) requirements for multiservice IoRT applications necessitate efficient link management.

Purpose of the Study:

To propose an adjustable wireless backhaul link selection algorithm for LEO-UAV-sensor-based IoRT networks.
To optimize the selection of backhaul links considering both remaining service time and capacity.
To enhance overall system performance and balance satellite load in multiservice IoRT environments.

Main Methods:

Developed an optimization model for backhaul link selection with an objective function Q integrating remaining service time and capacity.
Employed the snapshot method to convert dynamic network topology into a static one for analysis.
Designed a heuristic optimization algorithm to solve the link selection problem.

Main Results:

The proposed algorithm demonstrated superior system performance compared to traditional maximum service time and maximum capacity algorithms.
Numerical simulations indicated a more balanced distribution of satellite overload.
The algorithm effectively achieved a trade-off between remaining service time and capacity through dynamic parameter adjustment.

Conclusions:

The adjustable backhaul link selection algorithm is effective for multiservice IoRT scenarios.
The proposed method enhances system performance and load balancing in dynamic LEO-UAV-sensor networks.
Dynamic adjustment of parameters allows for flexible optimization of IoRT backhaul links.