Post-Disaster 4G/5G Network Rehabilitation Using Drones: Solving Battery and Backhaul Issues

Selim, Mohamed Y.; Kamal, Ahmed E.

doi:10.1109/glocomw.2018.8644135

Cited by 51 publications

(44 citation statements)

References 11 publications

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“…Generally, in the case that the whole cellular infrastructure is destroyed, only the satellite can provide the available backhaul connection. However, the UAV platform cannot be equipped with satellite transceiver equipment [9]. Inspired by [9], we use a postdisaster tethered backhaul UAV which can connect the core network and charge using a cable.…”

Section: System Model and Problem Formationmentioning

confidence: 99%

“…However, the UAV platform cannot be equipped with satellite transceiver equipment [9]. Inspired by [9], we use a postdisaster tethered backhaul UAV which can connect the core network and charge using a cable. Meanwhile, the other end of the cable is connected to a truck that is equipped with satellite transceiver equipment.…”

Section: System Model and Problem Formationmentioning

confidence: 99%

“…In this way, the UAVs can be connected to the core network. Thus, we assume that the UAVs are connected to each other using hybrid free-space optics/radio frequency (FSO/RF) links and one of the UAVs acquires the backhaul connection from a tethered backhaul UAV installed hundreds of meters from the disaster area [9]. The tethered backhaul UAV provides connectivity to the core network to the whole flying network via hybrid FSO/RF links.…”

Section: System Model and Problem Formationmentioning

confidence: 99%

“…In this paper, we mainly consider the energy consumption caused by hovering, and ignore the energy consumption caused by communication, because this part is much lower than the former [9]. By following the energy consumption model in [9], the hovering energy denoted by E hov j for UAV u j is expressed as…”

Section: System Model and Problem Formationmentioning

confidence: 99%

“…In particular, the strong line-ofsight (LoS) links of UAV mounted BSs can be utilized to quickly recover the communication services in post-disaster scenarios [7]- [12]. In [9], the authors proposed a UAV placement scheme to maximize the cellular connectivity while minimizing the energy consumption in post-disaster networks. Considering the limited transmission distance in large-scale UAV communications, the authors in [10] proposed a UAVassisted multi-hop transmission scheme to extend the wireless coverage in emergency communications.…”

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confidence: 99%

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UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach

Wang

Sun

et al. 2020

IEEE Internet Things J.

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In this paper, we address social-awareness property and unmanned aerial vehicle (UAV) assisted information diffusion in emergency scenarios, where the UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both the UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertexes, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties including global stability, convergence, and complexity. Finally, simulation results shows that our proposed approach can achieve a better trade-off among the information diffusion speed, channel switch cost, and complexity.

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Section: System Model and Problem Formationmentioning

confidence: 99%

Section: System Model and Problem Formationmentioning

confidence: 99%

Section: System Model and Problem Formationmentioning

confidence: 99%

Section: System Model and Problem Formationmentioning

confidence: 99%

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confidence: 99%

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