A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Rezwan, Sifat; Choi, Wooyeol

doi:10.3390/electronics10040449

Cited by 35 publications

(32 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3D mobility of flying things enabled expanding of the geographical area which helps in surveillance of the large area by a single UAV. Among other limitations, one of the key problems which must be addressed is lack of specialized routing protocols on the industrial level in the field of flying ad hoc networks [ 4 ]. Therefore, classification of aerial networks routing protocols is in the development phase; due to that, FANETs relay on traditional MANET routing protocols [ 5 , 6 ].…”

Section: Literature Surveymentioning

confidence: 99%

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing

Khan

Hassan

Alshehri

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV’s residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing

Khan

Hassan

Alshehri

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Initially, the agent investigates each state of the environment by performing various actions and creating a Q-table for each state-action pair using the Q-function. The agent then begins FIGURE 13: Reinforcement learning (RL) [108].…”

Section: A Reinforcement Learning (Rl)mentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learningbased approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

show abstract

“…This work also compares the FANETs with other ad-hoc concepts in literature. The applications of reinforcement learning algorithms to the FANETs under different scenarios are given in [19]. These scenarios include routing protocols, flight trajectory selection, relaying, and charging.…”

Section: A Scope Of Surveymentioning

confidence: 99%

Aeronautical Networks for In-Flight Connectivity: A Tutorial of the State-of-the-Art and Survey of Research Challenges

et al. 2022

View full text Add to dashboard Cite

The aeronautical networks attract the attention of both industry and academia since Internet access during flights turns to the crucial demand from luxury with the evolving technology. This In-Flight Connectivity (IFC) necessity is currently dominated by the satellite connectivity and Air-to-Ground (A2G) network solutions. However, the high installation/equipment cost and latency of the satellite connectivity reduce its efficiency. The A2G networks are utilized through the 4G/5G ground stations deployed on terrestrial areas to solve these satellites' problems. This terrestrial deployment reduces the coverage area of A2G networks, especially for remote flights over the ocean. The Aeronautical Ad-hoc Networks (AANETs) are designed to provide IFC while solving the primary defects of dominating solutions. The AANET is an entirely novel solution under the vehicular networks since it consists of aircraft with ultra-dynamic and unstructured characteristics. These characteristics separate it from the less dynamic Flying Ad-Hoc Networks (FANETs). Therefore, the environmental and mobility effects cause specific challenges for AANETs. This article presents a holistic review of these open AANET challenges by investigating them in data link, network, and transport layers. Before giving the details of these challenges, this article explores the state-of-the-art literature about satellite and A2G networks for IFC. We then give our specific interest to the AANET by investigating its particular characteristics and open research challenges. The main starting point of this study is that there is a lack of compact research on this exciting topic, although IFC is an inevitable need for the aeronautical industry. Also, the AANET could be underlined by giving all state-ofthe-art about the dominating IFC solutions. Therefore, this is the first work exploring the state-of-the-art for all the existing aeronautical networking technologies under a single comprehensive survey by deeply analyzing specific characteristics and open research challenges of AANETs. Additionally, the AANET is a novel topic and should be separately investigated from the FANETs as given in current literature.

show abstract

A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Cited by 35 publications

References 66 publications

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Aeronautical Networks for In-Flight Connectivity: A Tutorial of the State-of-the-Art and Survey of Research Challenges

Contact Info

Product

Resources

About