Dual Trajectory Optimization for a Cooperative Internet of UAVs

Zhang, Shuhang; Yang, Jiadi; Zhang, Hongliang; Song, Lingyang

doi:10.1109/lcomm.2019.2913631

Cited by 22 publications

(12 citation statements)

References 5 publications

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“…To complete a sensing task, each UAV needs to collect the sensory data from a specific location, which is defined as the sensing target of this task. To evaluate the sensing performance, we adopt a probabilistic model related to the distance between the location of the UAV and the sensing target [22]. The success of UAV sensing is a necessary condition for the completion of the sensing task.…”

Section: Basics Of U2x Communicationsmentioning

confidence: 99%

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Zhang

Song

2020

IEEE Trans. Veh. Technol.

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118

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In this paper, we consider an Internet of unmanned aerial vehicles (UAVs) over cellular networks, where UAVs work as aerial users to collect various sensory data, and send the collected data to their transmission destinations over cellular links. Unlike the terrestrial users in the conventional cellular networks, different UAVs have various communication requirements due to their sensing applications, and a more flexible communication framework is in demand. To tackle this problem, we propose a UAV-to-Everything (U2X) networking, which enables the UAVs to adjust their communication modes full dimensionally according to the requirements of their sensing applications. In this article, we first introduce the concept of U2X communications, and elaborate on its three communication modes. Afterwards, we discuss the key techniques of the U2X communications, including joint sensing and transmission protocol, UAV trajectory design, and radio resource management. A reinforcement learningbased mathematical framework for U2X communications is then proposed. Finally, the extensions of the U2X communications are presented.2) UAV Transmission: After UAV sensing, the collected data will be transmitted to the corresponding BS or UE, which is defined as the transmission destination. For each UAV, the transmission channel model and frequency band are regulated by the 3GPP [23]. To satisfy the transmission requirements raised by various applications, we provide three communication modes for the UAVs, namely U2N communications, U2U communications, and U2D communications. The communication mode for each link is selected by the UAV according to its location and the communication requirements of the sensing application. The descriptions of these three communication modes will be elaborated on in Section II-B.

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Section: Basics Of U2x Communicationsmentioning

confidence: 99%

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Zhang

Song

2020

IEEE Trans. Veh. Technol.

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118

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“…Since UAVs fly at high altitudes, the line-of-sight (LoS) components usually exist in the transmissions from UAVs to their mobile devices and the BS on the ground [28], [29]. Thus, the channel characteristic of the air-to-ground communications is different from that in traditional terrestrial communications.…”

Section: Uav Transmissionmentioning

confidence: 99%

Cellular UAV-to-Device Communications: Trajectory Design and Mode Selection by Multi-Agent Deep Reinforcement Learning

Zhang

et al. 2020

IEEE Trans. Commun.

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In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum.However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and transmission may influence their trajectories, we study the trajectory design problem for UAVs in consideration of their sensing and transmission. This is a Markov decision problem (MDP) with a large state-action space, and thus, we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space, and then propose a multi-UAV trajectory design algorithm to solve this problem.Simulation results show that our proposed algorithm can achieve a higher total utility than policy gradient algorithm and single-agent algorithm. ). I. INTRODUCTIONWith high mobility and low operational cost, unmanned aerial vehicle (UAV) is recognized as a powerful facility to provide sensing services [2], [3], which has found use in a wide range of sensing applications including traffic monitoring [4], industrial inspection [5], precision agriculture [6], and fire surveillance [7]. In the current unmanned aircraft systems (UASs) for sensing services, UAVs transmit their sensory data to terrestrial mobile devices 1 over the unlicensed spectrum [8]. However, due to the opportunistic channel access at the media access control (MAC) layer, the interference from surrounding terminals is uncontrollable, and the Quality-of-Service (QoS) for sensing services cannot be guaranteed. Therefore, a more reliable network is necessary.As an effective solution, terrestrial cellular networks can be utilized to provide UAV sensing services with guaranteed QoS, which we refer to as the cellular Internet of UAVs [9], [10]. In the cellular Internet of UAVs, UAVs first transmit the sensory data to the BS, after which the BS sends the received data to mobile devices. When UAVs fly far from their sensing targets, they may suffer low sensing quality, while if UAVs are far from the BS, it is difficult to transmit their sensory data back to the BS. Therefore, to ensure the QoS, UAVs should jointly take sensing and transmission into consideration in the designing of their trajectories. However, when UAVs are close to their mobile devices, the data rate can be further improved if UAVs can directly transmit the sensory data to proximal mobile devices, namely the UAV-to-Device (U2D) communications, rather than transmit through cellular communications.In this paper, we propose to enable U2D communications in the cellular Internet of UAVs.We consider a orthogonal frequency division multiple access (OFDMA) cellular Internet of UAVs 2 , where the sensory data ca...

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“…The combination of unmanned aerial vehicles (UAV) and Internet services is already proposed as the concept of Internet of UAV (IoUAV) [ 16 , 17 , 18 ]. The Internet and drones in specific are also related in [ 19 , 20 ] as Internet of Drones (IoD).…”

Section: Introductionmentioning

confidence: 99%

Distributed Architecture for Unmanned Vehicle Services

Ramos

Ribeiro

Safadinho

et al. 2021

Sensors

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The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the unmanned vehicles (UV), from the unmanned factor to the reduced size and costs, we found an opportunity to bring to users a wide variety of services supported by UV, through the Internet of Unmanned Vehicles (IoUV). Current solutions were analyzed and we discussed scalability and genericity as the principal concerns. Then, we proposed a solution that combines several services and UVs, available from anywhere at any time, from a cloud platform. The solution considers a cloud distributed architecture, composed by users, services, vehicles and a platform, interconnected through the Internet. Each vehicle provides to the platform an abstract and generic interface for the essential commands. Therefore, this modular design makes easier the creation of new services and the reuse of the different vehicles. To confirm the feasibility of the solution we implemented a prototype considering a cloud-hosted platform and the integration of custom-built small-sized cars, a custom-built quadcopter, and a commercial Vertical Take-Off and Landing (VTOL) aircraft. To validate the prototype and the vehicles’ remote control, we created several services accessible via a web browser and controlled through a computer keyboard. We tested the solution in a local network, remote networks and mobile networks (i.e., 3G and Long-Term Evolution (LTE)) and proved the benefits of decentralizing the communications into multiple point-to-point links for the remote control. Consequently, the solution can provide scalable UV-based services, with low technical effort, for anyone at anytime and anywhere.

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Dual Trajectory Optimization for a Cooperative Internet of UAVs

Cited by 22 publications

References 5 publications

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Cellular UAV-to-Device Communications: Trajectory Design and Mode Selection by Multi-Agent Deep Reinforcement Learning

Distributed Architecture for Unmanned Vehicle Services

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