Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management

Nguyen, Dinh Dung; Rohács, József; Rohács, Dániel

doi:10.3390/ijgi10050338

Cited by 48 publications

(20 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…e concept of safe drone use in a city environment and the potential for secure integration of drone traffic into the overall intelligent urban transportation system. In fact, the application of this notion necessitates more practically toemploy a diverse range of methodologies [18].…”

Section: Related Workmentioning

confidence: 99%

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Refaai

Dattu

Murthy³

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Drones, the Internet of Things (IoT), and Artificial Intelligence (AI) could be used to create extraordinary responses to today’s difficulties in smart city challenges. A drone, which would be effectively a data-gathering device, could approach regions that become complicated, dangerous, or even impossible to achieve for individuals. In addition to interacting with one another, drones must maintain touch with some other ground-based entities, including IoT sensors, robotics, and people. Throughout this study, an intelligent approach for predicting the signal power from a drone to IoT applications in smart cities is presented in terms of maintaining internet connectivity, offering the necessary quality of service (QoS), and determining the drone’s transmission range offered. Predicting signal power and fading channel circumstances enables the adaptable transmission of data, which improves QoS for endpoint users/devices while lowering transmitting data power usage. Depending on many relevant criteria, an artificial neural network (ANN)-centered precise and effective method is provided to forecast the signal strength from such drones. The signal strength estimations are also utilized to forecast the drone’s flight patterns. The results demonstrate that the proposed ANN approach has an excellent correlation with the verification data collected through computations, with the determination of coefficient R2 values of 0.97 and 0.98, correspondingly, for changes in drone height and distances from a drone. Furthermore, the finding shows that signal distortions could be considerably decreased and strengthened.

show abstract

Section: Related Workmentioning

confidence: 99%

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Refaai

Dattu

Murthy³

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Attention-based convolution neural networks with long short-term memory (CNN-LSTM) may improve traffic flow forecasts. In [21], it incorporates spatial and time-based information into the model by utilizing CNN and LSTM networks to gather traffic data. Attention-based CNN-LSTM predictions were more accurate during the week and weekend, and at peak and nonpeak periods.…”

Section: Related Workmentioning

confidence: 99%

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

2022

Mobile Information Systems

View full text Add to dashboard Cite

Smart cities make better use of space and have less traffic, cleaner air, and more efficient municipal services, improving people’s quality of life. The vast number of vehicles continually seeking to reach crowded spots in smart cities complicates acquiring a public parking space. It presents challenges for both traffic and residents. With such vast populations, road congestion is a serious challenge. It wastes vital resources such as fuel, money, and, most importantly, time. Finding a good location to park is one of the reasons for traffic congestion on the highway. This paper proposes a deep learning-based economic forecasting model (DL-EFM) for long-term economic growth in smart cities. Traffic management is vital for cities to guarantee that people and products can move freely across the city. Many automobiles attempting to reach crowded areas in smart cities make getting a public parking place difficult. It is inconvenient for both drivers and residents. Different traffic management authorities have implemented an artificial neural network (ANN) to resolve the issue, and modern vehicle systems have been coupled with intelligent parking solutions. The experimental outcome of the deep learning-based economic forecasting model improves traffic estimation, accuracy prediction in traffic flow, traffic management, and smart parking when compared to existing methods.

show abstract

“…The proposed model makes use of free flow and queuing-up movements of vehicles to suggest an alternate route for the driver which is free from traffic. Another model for calculating and managing the trajectories of drones in UAVs is proposed in Nguyen et al (2021). Here, the drone routes are estimated via the global GPS reference system with supporting GIS mapping, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options.…”

Section: Mobility Management As a Servicementioning

confidence: 99%

Cloud-Enabled High-Altitude Platform Systems: Challenges and Opportunities

et al. 2021

View full text Add to dashboard Cite

Augmenting ground-level communications with flying networks, such as the high-altitude platform system (HAPS), is among the major innovative initiatives of the next generation of wireless systems (6G). Given HAPS quasi-static positioning at the stratosphere, HAPS-to-ground and HAPS-to-air connectivity frameworks are expected to be prolific in terms of data acquisition and computing, especially given the mild weather and quasi-constant wind speed characteristics of the stratospheric layer. This paper explores the opportunities stemming from the realization of cloud-enabled HAPS in the context of telecommunications applications and services. The paper first advocates for the potential physical advantages of deploying HAPS as flying data-centers, also known as super-macro base stations. The paper then describes various cloud services that can be offered from the HAPS and the merits that can be achieved by this integration, such as enhancing the quality, speed, and range of the offered services. The proposed services span a wide range of fields, including satellites, Internet of Things (IoT), ad hoc networks (such as sensor; vehicular; and aerial networks), gaming, and social networks. For each service, the paper illustrates the methods that would be used by cloud providers to offload the service data to the HAPS and enable the cloud customers to consume the service. The paper further sheds light on the challenges that need to be addressed for realizing practical cloud-enabled HAPS, mainly, those related to high energy, processing power, quality of service (QoS), and security considerations. Finally, the paper discusses some open issues on the topic, namely, HAPS mobility and message routing, HAPS security via blockchain and machine learning, artificial intelligence-based resource allocation in cloud-enabled HAPS, and integration with vertical heterogeneous networks.

show abstract

Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management

Cited by 48 publications

References 52 publications

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

Cloud-Enabled High-Altitude Platform Systems: Challenges and Opportunities

Contact Info

Product

Resources

About