The IoT based automate landing system of a drone for the round-the-clock surveillance solution

Chae, Heeseo; Park, JunHo; Song, HoNam; Kim, YongHwan; Jeong, HaeWook

doi:10.1109/aim.2015.7222767

Cited by 21 publications

(6 citation statements)

References 1 publication

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“…Drones can communicate to the cloud using the Internet to perform the exchange of data and information and processing. The use of drones for surveillance can overcome the weakness of limited power, increasing its navigation time [25] and enabling it to be a round-the-clock surveillance system [26].…”

Section: Discussionmentioning

confidence: 99%

A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

Ubina

Cheng

Chen

et al. 2021

Drones

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This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results.

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Section: Discussionmentioning

confidence: 99%

A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

Ubina

Cheng

Chen

et al. 2021

Drones

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“…However, the power transfer efficiency is only about 75 % when the receiver and transmitter coils are precisely aligned and it significantly degrades even more as the misalignment increases. Several works have sought to address the issues of alignment and poor power transfer efficiency [11]. For example, the authors in [10] design a wireless charging station that uses ultrasonic sensors for identifying the quadrotor's position after landing.…”

Section: Related Workmentioning

confidence: 99%

“…Various solutions have been proposed for extending the flight time of quadrotors, from battery expansion and battery replacement methods [2]- [7], wireless charging [8]- [11], contact charging [12]- [15], and tethered charging [16]- [18]. However, these approaches do not meet all the requirements of the ideal autonomous charging system for quadrotors, but trade-off efficiency, portability, universality, and robustness.…”

Section: Introductionmentioning

confidence: 99%

AutoCharge: Autonomous Charging for Perpetual Quadrotor Missions

Saviolo,

Mao,

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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“…A solution to utilize a drone as the round-the-clock surveillance system is suggested in [5]. The solution deals with the active landing-field which can support automatic charging and landing, the IoT-based command system, and the drone-user interaction application of the mobile device.…”

Section: State Of the Artmentioning

confidence: 99%

Scheduling Uav Fleets for the Persistent Operation of Uav-Enabled Wireless Networks During NPP Monitoring

Kliushnikov¹,

Fesenko

Kharchenko

2020

reks

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Motivation. During nuclear power plant (NPP) monitoring, unmanned aerial vehicles (UAVs) can be used as an affordable and cost-efficient tool to deploy a UAV-enabled wireless network (UEWN) for providing the crisis centre (CrS) needed monitoring data from monitoring stations (MSs) of the automated radiation monitoring system in case of damage of wired networks. However, because of the high electrical power requirement, the normal operation time of a UAV of a multi-rotor type (MUAV) is just 20 to 30 min, limiting the operation time of a UEWN during NPP monitoring missions. The subject matter of the paper is the process of ensuring the persistent operation of a UEWN. The aim of this paper is to develop a queuing theory based approach to scheduling MUAVs for persistent operation of a UEWN during NPP monitoring. The objectives of the paper are: to propose a scheme of deployment of a UEWN to connect a MS with the CrS of Zaporizhzhia (ZNPP) in case of damage of the wired network between the MS and the CrS; to introduce the main parameters characterizing the automatic battery charging station (ABCS) as a multi-channel queuing system; to develop and describe in detail a queuing theory based approach to scheduling MUAVs of the UEWN for persistent NPP monitoring. The following results were obtained. A scheme of deployment of a UEWN, consisting of LoRaWAN and WiFi segments, to connect a MS with the CrS of ZNPP in case of damage of the wired network between the MS and the CrS was proposed and described. A shift schedule for 3 MUAV fleets to ensure the persistent operation of the UEWN during ZNPP post-accident monitoring missions was built. It was shown that the ABCS can be considered as a multi-channel queuing system, in which two or more channels (battery charge points at the ABCS) are available to handle arriving MUAVs. A queuing theory based approach to scheduling MUAV fleets of the UEWN for persistent NPP monitoring is developed and described in detail. It was evaluated and illustrated by means of plots how the number of occupied channels of the ABCS depends on: 1) the battery charging time for the MUAV at the ABCS, and 2) the flight distance for the MUAV between its location point in the WiFi segment and the ABCS. The next research steps can cover issues regarding to: 1) scheduling MUAV fleets for numerous UEWNs, 2) developing models of a UEWN operation using a LiFi segment for transmission of monitoring data, and 3) developing reliability/survivability models of a UEWN taking into account UAV failures or/and wireless signal interference.

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The IoT based automate landing system of a drone for the round-the-clock surveillance solution

Cited by 21 publications

References 1 publication

A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

AutoCharge: Autonomous Charging for Perpetual Quadrotor Missions

Scheduling Uav Fleets for the Persistent Operation of Uav-Enabled Wireless Networks During NPP Monitoring

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