Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring

Rametta, Corrado; Schembra, Giovanni

doi:10.3390/fi9010008

Cited by 59 publications

(31 citation statements)

References 44 publications

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“…The research in [256], proposed drone-cell management framework (DMF) using UAVs act as aerial base stations with a multi-tier drone-cell network to complement the terrestrial cellular network based on NFV and SDN paradigms. In [341], the authors proposed a video monitoring platform as a service (VMPaaS) using swarm of UAVs that form a FANET in rural areas. This platform utilizes the recent NFV and SDN paradigms.…”

Section: Networking Challengesmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

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The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including realtime monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

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Section: Networking Challengesmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

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“…The first two constraints are the same as in (25), where u i and w i yield an objective function similar to (19). The piecewise approximation constraints (31c) and (31d) correspond to (28) and (29), however, (30) is modified to toggle the approximation via w i in (31e). Semi-JSNC method reduces computational complexity by decreasing number of variables and constraints.…”

Section: Semi-joint Spatial Network Configurationmentioning

confidence: 99%

Spatial Configuration of Agile Wireless Networks With Drone-BSs and User-in-the-loop

Bor-Yaliniz

El-Keyi

Yanıkömeroğlu

2019

IEEE Trans. Wireless Commun.

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Agile networking can reduce over-engineering, costs, and energy waste. Towards that end, it is vital to exploit all degrees of freedom of wireless networks efficiently, so that service quality is not sacrificed. In order to reap the benefits of flexible networking, we propose a spatial network configuration scheme (SNC), which can result in efficient networking; both from the perspective of network capacity, and profitability. First, SNC utilizes the drone-base-stations (drone-BSs) to configure access points. Drone-BSs are shifting paradigms of heterogeneous wireless networks by providing radically flexible deployment opportunities. On the other hand, their limited endurance and potential high cost increase the importance of utilizing drone-BSs efficiently. Therefore, secondly, user mobility is exploited via user-in-the-loop (UIL), which aims at influencing users' mobility by offering incentives. The proposed uncoordinated SNC is a computationally efficient method, yet, it may be insufficient to exploit the synergy between drone-BSs and UIL. Hence, we propose joint SNC, which increases the performance gain along with the computational cost. Finally, semi-joint SNC combines benefits of joint SNC, with computational efficiency. Numerical results show that semi-joint SNC is two orders of magnitude times faster than joint SNC, and more than 15% profit can be obtained compared to conventional systems.Index Terms-Unmanned aerial vehicle, user-in-the-loop, agile network, drone-BS. arXiv:1809.05315v1 [cs.NI]

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“…Rametta and Schembra [24] present an SDN/NFV for FANET in a scenario where terrestrial nodes and UAVs are both equipped with cameras for monitoring rural areas. UAVs serve as a backbone network for the platform, where end-users monitor a particular rural area.…”

Section: Related Workmentioning

confidence: 99%

Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network

Cumino

Maciel

Tavares

et al. 2019

Sensors

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Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to establish a Flying Ad-hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapidly deployable systems. In this context, Software-Defined Networking FANET (SDN-FANET ) separates the control and data plane and provides network programmability, which considers a centralized controller to perform all FANET control functions based on global UAV context information, such as UAV positions, movement trajectories, residual energy, and others. However, control message dissemination in an SDN-FANET with low overhead and high performance is not a trivial task due to FANET particular characteristics, i.e., high mobility, failures in UAV to UAV communication, and short communication range. With this in mind, it is essential to predict UAV information for control message dissemination as well as consider hierarchical network architecture, reducing bandwidth consumption and signaling overhead. In this article, we present a Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NEtwork, called CAPONE. Based on UAV contextual information, the controller can predict UAV information without control message transmission. In addition, CAPONE divides the FANET into groups by computing the number of clusters using the Gap statistics method, which is input for a Fuzzy C-means method to determine the group leader and members. In this way, CAPONE reduces the bandwidth consumption and signaling overhead, while guaranteeing the control message delivering in FANET scenarios. Extensive simulations are used to show the gains of the CAPONE in terms of Packet Delivery Ratio, overhead, and energy compared to existing SDN-FANET architectures.Sensors 2020, 20, 67 2 of 18 humans' eyes in the sky, and UAVs must be flying and monitoring events in a coordinated fashion for a long period with reliability [10].In FANET scenarios, UAVs must collaborate, and their behaviors (both the data transmission and the UAV movement) must be effectively controlled to maximize the FANET benefits and application performance [11]. In this way, a decentralized approach [6] to manage FANET include more complexity to synchronize information with all network nodes, requiring more control messages to be transmitted across the FANET, and can increase total power consumption. On the other hand, a centralized UAV controlling system can make optimized decisions based on the global UAV context information [12]. A centralized controller node must deal with the mobility trajectory of UAVs to avoid UAV collisions or improve application performance. It also needs to determine data routing paths, change packet transmission parameters (data rate or transmission power) due to performance or energy reasons. Hence, FANET could be managed by a Software Defined Network (SDN) [13] composed by a group of UAVs with a central controller entity [14]. In turn, implements the concept of SDN into FANET to separate the control and data plane, and to provide network p...

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Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring

Cited by 59 publications

References 44 publications

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Spatial Configuration of Agile Wireless Networks With Drone-BSs and User-in-the-loop

Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network

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