Citation
Abstract
Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified.
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Official URL or Download Paper: https://www.mdpi.com/2504-446X/7/4/260
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Computer Science and Information Technology Institute for Mathematical Research |
DOI Number: | https://doi.org/10.3390/drones7040260 |
Publisher: | Multidisciplinary Digital Publishing Institute |
Keywords: | Drone; Energy efficiency; Information freshness; Internet of things (IoT); Age of information (AoI); Scheduling; Trajectory; Unmanned aerial vehicle (UAV); Wireless sensor networks (WSNs); Industry; Innovation and infrastructure |
Depositing User: | Ms. Che Wa Zakaria |
Date Deposited: | 06 Aug 2024 06:15 |
Last Modified: | 06 Aug 2024 06:15 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/drones7040260 |
URI: | http://psasir.upm.edu.my/id/eprint/106782 |
Statistic Details: | View Download Statistic |
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