Work

Integrating machine learning with a positioning mechanism for managing interference and power consumption in a multilayer fleet of unmanned aerial platforms

Year: 2025

Type: article

Abstract: The soaring global demand for ubiquitous wireless connectivity, which epitomizes the digital era, can only be fulfilled with heterogenous networks that, increasingly, need to include aerial platform fleets for a more holistic approach. However, deploying aerial platforms to serve as a fleet would inevitably result in interference, especially for high frequency bands and increased power consumption. This work presents a framework that integrates Machine Learning with a fleet positioning mechanism to mitigate interference and reduce power consumption in a multilayer fleet of aerial platforms. In turn, this optimizes flight time in the short run and the sustainability of the holistic connectivity approach in the long run. Assessment of the post-optimisation Received Signal Strength Index reveals a 16% improvement to pre-optimisation. The work is validated with a proof-of-concept for smart agriculture.

Source: Cluster Computing

DOI: 10.1007/s10586-024-05044-8

Authors: Faris A. Almalki, Marios C. Angelides

Institutions: Taif University, Brunel University of London

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,190