Proactive Handover Decision for UAVs with Deep Reinforcement Learning

Jang, Younghoon; Raza, Syed M.; Kim, Moonseong; Choo, Hyunseung

doi:10.3390/s22031200

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…A unique crossover choice technique using deep reinforcement learning (DRL) was presented within [13] with the goal of preventing pointless transitions whilst preserving stable communication. The suggested DRL framework creates a received signal strength indicator (RSSI) cantered on an optimization method for the digital training of UAV turnover choice and uses the UAV environment as just an inputs for a localized strategy evolutionary algorithms.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application

2023

IJIES

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Internet of drones (IoD) are commonly constructed with unmanned vehicles, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. These IoDs are interact with zone service providers (ZSPs) to achieve the goal of assisting drones in accessing controlled agriculture services. The utilization of drones in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area by analysing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. objectively and equitably assigns a weight to multiple service scheduling parameters based on maldistributed decision making theory, calculates the serving priority of each service request group, and then serves the service request groups based on the calculated serving priori-ty accordingly Hence, this paper proposes reinforcement learning-based topology-aware routing protocol with priority scheduling (RLTARP) to provide reliable combinations between the source and destination. It also improves the routing decision by considering two-hop neighbour nodes, extending the local view of the network topology. The priority scheduling method adopts maldistributed decision making theory, to find the group of priorities based on service request. The proposed RLTARP is compared with three existing methods such as DroneCOCoNet, Markov decision process (MDP)and deep deterministic policy gradient (DDPG) and hence it produces 46.34% of packet delivery ratio, 67.49% of end to end delay, 16.45% of routing overhead, 13% of energy consumption and 97.6% of network lifetime.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application

2023

IJIES

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“…Deciding on the best beam has become a new factor to consider in the HO management process. The large number of beams which the user must choose from makes the HO technique significantly more difficult [57][58][59][60].…”

Section: Uav Handover Based On Machine/deep Learningmentioning

confidence: 99%

Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Al-Shaibani

Shayea

Çağlar

et al. 2022

Sensors

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The rapid growth of mobile data traffic will lead to the deployment of Ultra–Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)—based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three–dimensions (3D), with dominant of line–of–sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks.

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“…A variety of handover decision-making algorithms are used in cellular networks, such as RSRP, Received Signal Strength Indicator (RSSI) of the Serving Base Station (S-BS), the Signal-to-Interference-Plus-Noise Ratio (SINR), mobile movement speed, distance between the UE and BS, limited capacity of BSs, weight functions, cost functions, fuzzy logic control, and machine with deep learning technology. The same handover decision algorithms can be used with drones, but the performance will differ due to the different characterization of drones [63][64][65][66][67][68][69][70]. Moreover, the requirements of 6G technology will be ultra-high compared to those of the previous mobile systems.…”

Section: Handover Decision Algorithmsmentioning

confidence: 99%

“…In 2019 [63], the authors conducted research on multi-user access control in UAV networks based on deep learning technology. In this work, a deep reinforcement learning method was proposed to provide a centralized control of multiple users in order to enhance the data rate and avoid unnecessary handovers.…”

Section: Deep Learning-based Techniquementioning

confidence: 99%

Handover Management for Drones in Future Mobile Networks—A Survey

Shayea

Dushi

Banafaa

et al. 2022

Sensors

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Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone’s free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones’ characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques.

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Proactive Handover Decision for UAVs with Deep Reinforcement Learning

Cited by 13 publications

References 30 publications

Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application

Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application

Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Handover Management for Drones in Future Mobile Networks—A Survey

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