UAV IoT Framework Views and Challenges: Towards Protecting Drones as “Things”

Λάγκας, Θωμάς; Argyriou, Vasileios; Bibi, Stamatia; Sarigiannidis, Panagiotis

doi:10.3390/s18114015

Cited by 151 publications

(70 citation statements)

References 78 publications

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“…A final survey on drones integrated with IoT can be found in [17], which considers multi-robot systems and utilization of technologies such as 4G/5G networks, cameras, sensors, and GNSS receivers. This work supports some of the proposals of the SwarmCity project, such as the communications of the fleet (e.g., drone-drone, station-drone, and drone-station), and the sensors integrated with the robots (e.g., cameras and environmental sensors).…”

Section: Related Workmentioning

confidence: 99%

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

Roldán‐Gómez

Garcia-Aunon

Mazariegos

et al. 2020

Pers Ubiquit Comput

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Smart cities have emerged as a strategy to solve problems that current cities face, such as traffic, security, resource management, waste, and pollution. Most of the current approaches are based on deploying large numbers of sensors throughout the city and have some limitations to get relevant and updated data. In this paper, as an extension of our previous investigations, we propose a robotic swarm to collect the data of traffic, pedestrians, climate, and pollution. This data is sent to a base station, where it is treated to generate maps and presented in an immersive interface. To validate these developments, we use a virtual city called SwarmCity with models of traffic, pedestrians, climate, and pollution based on real data. The whole system has been tested with several subjects to assess whether the information collected by the drones, processed in the base station, and represented in the virtual reality interface is appropriate. Results show that the complete solution, i.e., fleet control, data fusion, and operator interface, allows monitoring the relevant variables in the simulated city.

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Section: Related Workmentioning

confidence: 99%

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

Roldán‐Gómez

Garcia-Aunon

Mazariegos

et al. 2020

Pers Ubiquit Comput

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“…This concept is commonly known as Unmanned Aerial Base Station (UABS) when a base station (BS) is mounted on a drone to support ground communications. The applications of this model range from providing service to users in disaster scenarios, to data acquisition from IoT devices [3], [14], [38]- [42]. Mozaffari et al [3] presents a comprehensive review of the usage of UAV, providing connectivity to ground devices.…”

Section: Unmanned Aerial Base Stationsmentioning

confidence: 99%

System Assessment of WUSN Using NB-IoT UAV-Aided Networks in Potato Crops

et al. 2020

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Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors' battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor's density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance. INDEX TERMS Precision agriculture, NB-IoT, unmanned aerial vehicles, wireless underground sensor networks.

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“…It is of great importance for such systems to be protected against cyber-attacks. An unauthorized entry of a malicious individual in the system may cause great damage to the cultivation process or even acquire the farmers personal information [47]. In the pursuit of safeguarding a smart farm monitoring system the confidentiality of data should be met.…”

Section: Security Mechanismsmentioning

confidence: 99%

Precision Agriculture: A Remote Sensing Monitoring System Architecture †

2019

Self Cite

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Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

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UAV IoT Framework Views and Challenges: Towards Protecting Drones as “Things”

Cited by 151 publications

References 78 publications

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

System Assessment of WUSN Using NB-IoT UAV-Aided Networks in Potato Crops

Precision Agriculture: A Remote Sensing Monitoring System Architecture †

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