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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Time-Constrained Node Visit Planning for Collaborative UAV-WSN Distributed Applications.

Andrea Augello¹, Salvatore Gaglio^1,2, Giuseppe Lo Re¹

¹Department of Engineering, University of Palermo, Viale delle Scienze, Ed. 6, 90128 Palermo, Italy.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

This study introduces a new Unmanned Aerial Vehicle (UAV) path-planning method for Wireless Sensor Networks (WSNs). The UAV actively participates in computation, optimizing flight time and data collection efficiency.

Keywords:

UAV UAV path planning UAV task scheduling UAV-supported IoT systems for smart cities UAV–WSN integration distributed computing

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Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Unmanned Aerial Vehicles (UAVs) are increasingly used for data collection in Wireless Sensor Networks (WSNs).
Current path-planning methods for UAVs in WSNs typically assume data is always ready for retrieval, limiting UAVs to passive data collectors.
This rigid model overlooks the potential for UAVs to actively engage in computation within the network.

Purpose of the Study:

To analyze a novel scenario where a UAV actively participates in distributed computation within multiple WSNs.
To develop and validate a UAV path-planning strategy that integrates computational roles beyond simple data retrieval.
To enhance network performance by leveraging the UAV's computational capabilities.

Main Methods:

A new UAV path-planning approach is proposed, enabling the UAV to initiate distributed computation on sensor nodes during its visits.
The UAV collects computation outcomes on subsequent visits, orchestrating the overall process.
The method was experimentally validated against existing UAV path-planning techniques.

Main Results:

The proposed method significantly optimizes total flight time.
Demonstrated improvements in Average Age of Information (AoI) and Average cluster computation end time.
Achieved reduced Average data collection time compared to adapted prevalent approaches.

Conclusions:

UAVs can transition from passive data collectors to active computational entities within WSNs.
The developed path-planning strategy effectively integrates UAV computation, leading to substantial performance gains.
This research opens new avenues for intelligent UAV-assisted WSN operations.