Inter-UAV Routing Scheme Testbeds

Amponis, George; Λάγκας, Θωμάς; Sarigiannidis, Panagiotis; Vitsas, V.; Fouliras, Panayotis

doi:10.3390/drones5010002

Cited by 15 publications

(13 citation statements)

References 51 publications

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“…For networks with highly mobile devices, routing protocols are not practical or do not provide enough throughputs in VANET. However, the topology of the FANET alternates more frequently than that of VANET or MANET [43,44]. The most essential network technology which is applicable these days for the implementation of FANETs is the IEEE 802.11a and 802.11s for mesh ad hoc network extension.…”

Section: Flying Ad-hoc Network (Fanet)mentioning

confidence: 99%

Moving Ad Hoc Networks—A Comparative Study

Al-Absi

Sain

et al. 2021

Sustainability

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An ad hoc network is a wireless mobile communication network composed of a group of mobile nodes with wireless transceivers. It does not rely on preset infrastructure and is established temporarily. The mobile nodes of the network use their own wireless transceivers to exchange information; when the information is not within the communication range, other intermediate nodes can be used to relay to achieve communication. They can be widely used in environments that cannot be supported by wired networks or which require communication temporarily, such as military applications, sensor networks, rescue and disaster relief, and emergency response. In MANET, each node acts as a host and as a router, and the nodes are linked through wireless channels in the network. One of the scenarios of MANET is VANET; VANET is supported by several types of fixed infrastructure. Due to its limitations, this infrastructure can support some VANET services and provide fixed network access. FANET is a subset of VANET. SANET is one of the common types of ad hoc networks. This paper could serve as a guide and reference so that readers have a comprehensive and general understanding of wireless ad hoc networks and their routing protocols at a macro level with a lot of good, related papers for reference. However, this is the first paper that discusses the popular types of ad hoc networks along with comparisons and simulation tools for Ad Hoc Networks.

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Section: Flying Ad-hoc Network (Fanet)mentioning

confidence: 99%

Moving Ad Hoc Networks—A Comparative Study

Al-Absi

Sain

et al. 2021

Sustainability

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“…Caillouet et al made a trade-off between the altitude of a drone and its charging coverage to ensure good harvesting capabilities for industrial scenarios [36]. For the studies in [37][38][39], the UAV was used to collect data from sensors, and routing protocols were studied to reduce the data loss as well as energy consumption. Although the above work involves energy consumption, UAV flight trajectory, computational performance, and other aspects, most of them use a single UAV as the energy output source, which actually limits the improvement of UAV performance.…”

Section: Related Workmentioning

confidence: 99%

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Qin

Zhao

et al. 2021

Remote Sensing

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Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.

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“…Devices in the IoT include sensors that are often constrained by a limited battery and thus usually broadcast at short ranges. In this context, UAVs can be employed as data-collection stations, which determine the device’s location being out of transmission range from the destination and afterwards establish a connection with the latter in order to re-transmit the collected data [ 5 , 6 ]. UAVs in the role of aerial stations are expected to remarkably enhance IoT infrastructures, through the dynamic provision of stable device connections, as opposed to terrestrial base stations which may often be characterised by a lack of line-of-sight (LoS).…”

Section: Introductionmentioning

confidence: 99%

Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Spyridis

Λάγκας

Sarigiannidis

et al. 2021

Sensors

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Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

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Inter-UAV Routing Scheme Testbeds

Cited by 15 publications

References 51 publications

Moving Ad Hoc Networks—A Comparative Study

Moving Ad Hoc Networks—A Comparative Study

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

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