UAV Network and IoT in the Sky for Future Smart Cities

Qi, Fei; Zhu, Xiaoyan; Mang, Ge; Kadoch, Michel; Li, Wei

doi:10.1109/mnet.2019.1800250

Cited by 121 publications

(51 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite an abundance of quality research studies with a conceptual (or review-based) character that have tried over time to express, describe and prioritise the diverse, versatile and dynamic dimensions incorporated in each of the themes presented in our paper, including AI (e.g., [96][97][98][99][100]), smart city (e.g., [101][102][103][104][105]), CAVs (e.g., [106][107][108][109]), UAVs and PAVs (e.g., [110][111][112][113]), MaaS (e.g., [114][115][116][117]), IoT (e.g., [118][119][120][121]), PI (e.g., [122][123][124][125]) and Industry 4.0 (e.g., [126][127][128][129]), there is as yet no clear and universally approved set of definitions that critically underpins the nexus of AI, transport and the smart city.…”

Section: Definitions and Conclusionmentioning

confidence: 99%

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

et al. 2020

View full text Add to dashboard Cite

Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

show abstract

Section: Definitions and Conclusionmentioning

confidence: 99%

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Mignardi in [59] discusses the importance of proper trajectory design for UAV while serving NB-IoT nodes. The work in [60] introduces a multilayer UAV network for IoT applications using 5G networks. A practical LoRa deployment measuring the received signal strength (RSS) is presented in [61].…”

Section: Unmanned Aerial Base Stationsmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…At this moment, x value will be K value. Part of the quadratic CWND increase equation is shown in the front part of Equation (1). Calculating K value is: Since CWND goes up every time an ACK arrives, the rise of CWND is discrete event.…”

Section: As Shown Inmentioning

confidence: 99%

Adaptive TCP Transmission Adjustment for UAV Network Infrastructure

Lee

Kim

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

A UAV network composed of multiple UAVs allows a wide operating radius and various tasks to be performed. However, a UAV network mostly suffers from high probability of transmission failure due to interference or mobility. Also, nodes connected to the network often experience connection loss and segment loss caused by frequent node mobility and routing update. Since congestion is not the only cause of data loss in UAV networks, the TCP congestion control should not be run if there is a possibility of transient link instability unless a reduction in transmission speed is required. For this reason, we propose an algorithm to improve the transmission performance of UAV network through TCP with Slow-Start threshold (Ssthresh) value adjusted. The adjustment algorithm is called Adaptive Ssthresh Reviser for flying Ad hoc Network (ASRAN) that quickly restores unnecessary decrease of transmission speed in UAV network. IntroductionWith the development of wireless network devices, it has become possible to collect a wide range of information and form flexible topologies through various small network devices. However, due to the wide range of activities or mobility, it is difficult for all equipment to be directly connected to the Internet's backbone. In environments where it is difficult to connect directly to the Internet, many devices can connect to the Internet with a wireless ad hoc network. There are various techniques based on wireless multi-hop environments; for example, Unmanned aerial vehicle (UAV)-based network system [1-3], multiple Internet of Things (IoT) devices [4,5], 5G network with small mesh networks for fast data transmission speed [6], VANET technology for vehicles [7,8], and MANET technology [9,10]. However, various wireless multi-hop networks including UAV network (e.g., flying ad hoc network) often suffers from segment loss [11]. UAVs have an unstable network because they are difficult to load high-performance device due to limited payload size and moves around frequently to carry out various tasks. As a result, UAV networks often suffer from segment losses due to unstable networks. In this situation, since the formation information and the control message necessary for UAV must be received, the transmission control protocol (TCP), which is a reliable communication protocol, is used to recover the segment loss [12]. Based on this observation, this paper suggests an improvement technique of TCP to enhance the performance of the overall UAV network.If the segment loss occurs, TCP considers the network to be congested and decreases the congestion window (CWND) to reduce the transmission speed. The reason for reducing the transmission speed is that segment losses are caused by heavy traffic exceeding link capacity in most of wired environment. However, in wireless multi-hop environments such as UAV networks, segment losses are caused by not only buffer overflow but also transient link instability. For example, if power

show abstract

UAV Network and IoT in the Sky for Future Smart Cities

Cited by 121 publications

References 10 publications

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

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

Adaptive TCP Transmission Adjustment for UAV Network Infrastructure

Contact Info

Product

Resources

About