Unmanned Aerial Vehicles for Internet of Things (IoT). Группа авторов
Vehicle (UAV) Drones. https://www.equinoxsdrones.com/blog/10-major-pros-cons-of-unmanned-aerial-vehicle-uav-drones. Accessed on 31 March 2021.
31. Yaacoub, J.P., Noura, H., Salman, O., Chehab, A., Security analysis of drones systems: Attacks, limitations, and recommendations. Internet of Things, 11, 100218. https://doi.org/10.1016/j.iot.2020.100218
32. NATO Parliamentary Assembly, Unmanned Aerial Vehicles: Opportunities and Challenges for the Alliance, p. 11, 12, Special Report, Pierre Claude Nolin (Canada), Special Rapporteur, International Secretariat, November 2012.
33. Pfeifer, C., Barbosa, A., Osama M., Hans-Ulrich, P., Marie-Charlott, R., and Alexander B. Using Fixed-Wing UAV for Detecting and Mapping the Distribution and Abundance of Penguins on the South Shetlands Islands, Antarctica. Drones, 3, 2, 39, 2019. https://doi.org/10.3390/drones3020039.
34. Connect ESCs and Motors. https://ardupilot.org/copter/docs/connect-escs-and-motors.html Accessed on 31 March 2021.
35. Yinka-Banjo, C. and Ajayi, O., Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture, Autonomous Vehicles, 2019.
36. V-22-osprey-fleet-tops-40000-flight-hours. 19 Dec. 2017. www.helicopter-industry.com, 19 Dec’2017, www.helicopter-industry.com/2017/12/19/v-22-osprey-fleet-tops-40000-flight-hours/.
37. RQ-11B Raven Small Unmanned Aircraft Systems (SUAS). U.S. Army, November 4, 2014. https://www.army.mil/article/137604/rq_11b_raven_small_unmanned_aircraft_systems_suas Accessed on 31 March 2021.
38. Saldivar, J., Grand Forks AFB Airmen welcome Global Hawk. Af.mil, U.S Air Force, www.af.mil/News/Photos/igphoto/2000250415/, 26 May 2011.
39. Sakharkar, A., New software for improved and accurate drone mapping. 22 May 2020, https://www.techexplorist.com/new-software-improved-accurate-drone-mapping/32444/
40. Characteristics of unmanned aircraft systems and spectrum requirements to support their safe operation in non-segregated airspace, Report ITU-R M.2171 (12/2009). https://www.itu.int/en/ITU-R/space/snl/Documents/R-REP-M.2171-2009-PDF-E.pdf. Accessed on 31 March 2021.
1 *Corresponding author: [email protected]
2
Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope
Jolly Parikh* and Anuradha Basu *
ECE Department, Bharati Vidyapeeth’s College of Engineering, GGSIP University, New Delhi, India
Abstract
Unmanned aerial vehicles (UAVs) form an important part of the wireless communication systems. Compared to the terrestrial communication systems, these on-demand UAV networks need to be critically designed. The article highlights, the state of art, challenges encountered and the open research issues in designing UAV-aided wireless communication networks. The dense heterogenous wireless network scenarios of the present era, poses various challenges to the deployment of UAV-assisted communication networks. Few of the challenges identified here include best method of 3D deployment of drones, allocation of resources (computational and wireless), optimization of the flight time and trajectory of UAV, handover management, channel modelling of highly dynamic UAV channels in various scenarios of UAV-assisted networks, interference management, effects of higher doppler shifts in mmWave networks, on-board energy availability of UAV devicies, etc. The article also aims to give an insight to the future scopes in the designing of UAV-assisted networks. The budding researchers will be able to identify the open research area of their interest in further development of this technology and thereby contribute to the advancement of wireless communication systems.
Keywords: Unmanned aerial vehicles, UAV, channel modeling of UAV, trajectory optimization, cellular assisted UAV, UAV mmWave communication
2.1 Introduction
On account of their high mobility and their capability of being deployed easily, on-demand UAVs have been used in a wide range of applications like in the military, telecommunication, surveillance and monitoring, rescue operations, and so on [1, 2]. They have played a vital role in numerous applications spanning over various areas of human life. UAVs have been envisioned to support various applications in 5G wireless networks [3–5]. Over the past 40 years, UAV-centric research has focused on a wide range of issues in UAV assisted wireless networks. Work is still continuing and new applications with their own challenges and solutions are being explored daily. Compared to the terrestrial communication systems, these on-demand UAV networks have to be critically designed considering the non-stationary channels, high mobility of the UAV-user equipment, and the UAV-base station, energy and altitude constraints, and the various environmental factors affecting system performance. UAV-assisted wireless communication is a promising application for the next generation networks which are looking forward to the Internet of Things (IoT) era. As we move towards a heterogeneous communication network, the complexity in designing such networks is increasing by leaps and bounds. Here, in this article, effort has been made to bring forth the state of art and the challenges posed in designing UAV-assisted networks. Some of the open research areas identified are channel modeling of A2G links considering the communication over water bodies and highy urban scenarios, effects of higher doppler shifts in A2A links of UAV-mmWaves network, better effective interference mitigation techniques to deal with UAV-BS channel in UAV-cellular network, efficient spectrum sharing schemes for increasing network throughput and spectral efficiency of UAV-mmWave communication network, trajectory optimization, on-board energy requirements of UAVs and multidimensional UAV channel modeling. Need arises to explore more areas of this cutting edge technology of next generation communication networks. As work progresses in this area, researchers will come across more challenges to deal with.
2.2 Technical Challenges
While using UAVs in the Drone-Base Station scenario, we need to consider the network performance characterization, best method of 3D deployment of drones, allocation of both wireless as well as computational resources, optimization of flight time, and planning of UAV network. Operation in Drone-UE scenario should take into consideration issues like channel modeling, handover management, low latency control, interference management and 3D localization. To use UAVs for specific applications of wireless communication networks, factors like capability of UAV, flight constraints of UAV, energy constraints of UAV, the flying altitudes of UAV must be taken into account. Since UAV communication has distinctive channel characteristics of its own, accurate channel characterization is essential for optimum performance and efficient designing. The propagation characteristics of the highly dynamic UAV channels have not yet been properly explored, in terms of the spatial and temporal variations induced in the non-stationary channels of UAV networks. Designing of a generic channel model for UAV air to ground communications (A2G), demands for simulations and measurements to be carried out comprehensively in various environments, taking into consideration the altitudes at which the UAV is flying, it’s antenna movements, shadowing caused by UAV’s body, etc. Airframe shadowing caused due to structural design and maneuvering of small rotary UAVs is yet to be explored in detail, though preliminary studies were carried out by Sun et