Efficient deployment of connected unmanned aerial vehicles for optimal target coverage

Caillouet, Christelle; Razafindralambo, Tahiry

doi:10.1109/giis.2017.8169803

Cited by 30 publications

(40 citation statements)

References 10 publications

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“…The UAV to send to the new computed location is chosen by using a metric that minimizes the total travel distance of UAVs. Caillouet et al [26] tackle the more challenging problem of optimally positioning the minimum number of UAVs, taken from the set of all available UAVs, in order to cover a set of targets on the ground, while maintaining the connectivity of the aerial mesh to a ground base station. An evaluation of the model performed by the authors proves that the algorithm always finds an optimal solution in a reasonable amount of time (1000 s).…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications

Saraereh

Alsaraira

Khan

et al. 2020

Sensors

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In hostile and remote environments, such as mountains, forests or suburban areas, traditional communications may not be available, especially after a disaster, such as a flood, a forest fire or an earthquake. In these situations, the wireless networks may become congested or completely disrupted and may not be adequate to support the traffic generated by rescuers. It is also considered as the key tool in Corona Virus (COVID-19) battle. Moreover, the conventional approaches with fixed gateways may not work either, and this might lead to decoding errors due to the large distance between mobile nodes and the gateway. To avoid the decoding errors and improve the reliability of the messages, we propose to use intermediate Unmanned Aerial Vehicles (UAVs) to transfer messages from ground-based Long Range (LoRa) nodes to the remote base station (BS). Specifically, this UAV-enabled LoRa architecture is based on the ad hoc WiFi network, wherein, UAVs act as relays for the traffic generated between LoRa nodes and BS. To make the architecture more efficient, a distributed topology control algorithm is also proposed for UAVs. The algorithm is based on virtual spring forces and movement prediction technique that periodically updates the UAV topology to adapt to the movement of the ground-based LoRa nodes that move on the surface. The simulation results show the feasibility of the proposed approach for packet reception rate and average delay quality of service (QoS) metrics. It is observed that the mechanisms implemented in a UAV-enabled LoRa network effectively help to improve the packet reception rate with nominal buffer delays.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications

Saraereh

Alsaraira

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…This approach typically produces close to optimal results when the location information is timely and reliable, but suffers from the single point of failure problem and, if the dimension of the problem is big enough, location information dissemination and the amount of computations can generate substantial delays. Mixed integer programming [5], evolutionary algorithms [6,7] or potential fields [8] are typically used. On the other hand, distributed algorithms provide less optimal solutions, but computations are typically simpler, based on local information distributed among the nodes, thus making the network more responsive and resilient in case of unexpected changes.…”

Section: Related Workmentioning

confidence: 99%

LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways

2020

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Immediately after a disaster, such as a flood, wildfire or earthquake, networks might be congested or disrupted and not suitable for supporting the traffic generated by rescuers. In these situations, the use of a traditional fixed-gateway approach would not be effective due to the mobility of the rescuers. In the present work, a double-layer network system named LoRaUAV has been designed and evaluated with the purpose of finding a solution to the aforementioned issues. LoRaUAV is based on a WiFi ad hoc network of Unmanned Aerial Vehicle (UAV) gateways acting as relays for the traffic generated between mobile LoRaWAN nodes and a remote Base Station (BS). The core of the system is a completely distributed mobility algorithm based on virtual spring forces that periodically updates the UAV topology to adapt to the movement of ground nodes. LoRaUAV has been successfully implemented in ns-3 and its performance has been comparatively evaluated in wild area firefighting scenarios, using Packet Reception Ratio (PRR) and end-to-end delay as the main performance metrics. It is observed that the Connection Recovery and Maintenance (CRM) and Movement Prediction (MP) mechanisms implemented in LoRaUAV effectively help improve the PRR, with the only disadvantage of a higher delay affecting a small percentage of packets caused by buffer delays and disconnections.

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“…However, it leads to more deployed UAVs, since the coverage area is smaller. The trade-off between these two antagonistic objectives has been studied in [38] for static targets.…”

Section: Adcp Objectivementioning

confidence: 99%

“…Thus, a minimum weighted connected subset generation either gives a good candidate to add to the set of variables of the master problem, or proves that no such column exists. If the cost computed by the pricing problem is smaller than β (9) , the generated CS is added to the set of considered connected sets S, and the corresponding variables z t S are added for all t. Constraints of this sub-problem define the structure of the CS of UAVs, fulfilling Definition 3.…”

Section: Pricing Programmentioning

confidence: 99%

Efficient data collection and tracking with flying drones

2019

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Data collection is an important mechanism for wireless sensor networks to be viable. This paper addresses the Aerial Data Collection Problem (ADCP) from a set of mobile wireless sensors located on the ground, using a fleet of flying devices. The objective is i) to deploy a set of Unmanned Aerial Vehicles (UAVs) in a 3D space to cover and collect data from all the mobile wireless sensors at each time step through a ground-to-air communication, ii) to send these data to a central base station using multi-hop wireless air-to-air communications through the network of UAVs, iii) while minimizing the total deployment cost (communication and deployment) over time. The Aerial Data Collection Problem (ADCP) is a complex time and space coverage, and connectivity problem. We first present a mixed-integer linear program solving ADCP optimally for small instances. Then, we develop a second model solved by column generation for larger instances, with optimal or heuristic pricing programs. Results show that our approach provides very accurate solutions minimizing the data collection cost. Moreover, only a very small number of columns are generated throughout the resolution process, showing the efficiency of our approach.

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Efficient deployment of connected unmanned aerial vehicles for optimal target coverage

Cited by 30 publications

References 10 publications

Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications

Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications

LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways

Efficient data collection and tracking with flying drones

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