Elsevier

Ad Hoc Networks

Volume 83, February 2019, Pages 68-77
Ad Hoc Networks

Software-defined unmanned aerial vehicles networking for video dissemination services

https://doi.org/10.1016/j.adhoc.2018.08.023Get rights and content

Abstract

Unmanned Aerial Vehicles (UAVs) empower people to reach endangered areas under emergency situations. By collaborating with each other, multiple UAVs forming a UAV network (UAVNet) could work together to perform specific tasks in a more efficient and intelligent way than having a single UAV. UAVNets pose special characteristics of high dynamics, unstable aerial wireless links, and UAV collision probabilities. To address these challenges, we propose a Software-Defined UAV Networking (SD-UAVNet) architecture, which facilitates the management of UAV networks through a centralized SDN UAV controller. In addition, we introduce a use case scenario to evaluate the optimal UAV relay node placement for life video surveillance services with the proposed architecture. In the SD-UAVNet architecture, the controller considers the global UAV relevant context information to optimize the UAVs’ movements, selects proper routing paths, and prevents UAVs from collisions to determine the relay nodes deployment and guarantee satisfactory video quality. The experimental results show that the proposed SD-UAVNet architecture can effectively mitigate the challenges of UAVNet and it provides suitable Quality of Experience (QoE) to end-users.

Introduction

Unmanned Aerial Vehicles (UAVs) are autonomous aircrafts without the need of a pilot to be on board during the flight. UAV usage started with military applications, but now UAVs are getting more and more popular for public, civil, and even personal applications [1]. In military use cases, UAVs are deployed to execute military operations, such as battlefield inspection, geographic mapping of inaccessible terrain, border control surveillance, etc. On the other hand, in public and civil use cases, UAVs can be used by individuals or companies for aerial photography, video surveillance, etc. Multiple UAVs might collaborate in order to form a UAV network (UAVNet) [2], [3] to perform tasks in a more efficient and economic way than having only a single UAV [4]. Compared to a single UAV system, a UAVNet is more robust and can cover a larger area of interest. In this context, video dissemination over an UAVNet enables a large class of multimedia applications such as disaster recovery, environmental monitoring, safety & security, and others [1]. Hence, multimedia data plays an important role to provide rich visual information to help the ground rescue teams to take appropriate decisions [5]. However, video dissemination over UAVNets with Quality of Experience (QoE) support is a hard task due to frequent topology changes caused by UAV mobility [6], [7]. For instance, UAVs movements break plenty of communication links, which increase the packet loss during video transmissions.

UAVs can be used for autonomous flights, either by following preprogrammed flight plans or through the help of more complex dynamic automation systems. Therefore, an intelligent inter-UAV coordination protocol must be defined to control the behaviors of multiple UAVs, maximizing the benefits of a UAVNet. This UAV controlling protocol manages both the inter-UAV communication (i.e., routing protocols), as well as the UAV movement trajectory. In this context, Software-Defined Networking (SDN) [8], [9], [10] is a promising approach for this multi-UAV controlling task, since it introduces complete network programmability by separating the control plane and data plane. SDN also considers a centralized SDN controller responsible for all control functions such as UAVs placement, collision avoidance, and other services.

SDN has been successfully applied in wired networks, such as data center networks or backbone networks. Specifically, these networks have a centralized architecture, where a control center is responsible for network management [11]. The SDN controller is deployed in the control center, and it is aware of the global network conditions to optimize the network operations, by dynamically configuring the data plane routes. For SDN deployments in wired networks with fixed infrastructure topology, programmability means that the control plane adaptively adjusts the data path, and the data plane follows the instructions to forward the packet through different interfaces. On the other hand, SDN applied to UAVNets, the UAV programmability means to control the mobility trajectory of the UAVs in order to avoid UAV collisions or to improve the application performance, to determine the data routing paths, to change the packet transmission parameters (data rate or transmission power) due to performance or energy reasons, and others. UAVNets are mostly deployed to perform specific tasks, such as assisting disaster recovery communications, surveillance monitoring, remote sensing/searching, etc. In these tasks, UAVNet nodes must collaborate with each other and their behaviors (both the data transmission and the UAV movement) should be controlled by a centralized entity, i.e., a controller.

In this article, we propose a Software-Defined UAVNet architecture (SD-UAVNet), which implements the concept of SDN into UAVNets to separate the control and data plane, as well as to provide network programmability by controlling UAVs’ operation parameters. With the proposed SD-UAVNet architecture, we focus on the problem of optimal UAV relay node placement for real-time video services, which reduces the impact of node mobility on the QoE of delivered videos. The SD-UAVNet controller considers the global UAV context information to prevent UAV collisions, optimize the UAVs’ movements, and establish a routing path that determines the relay node deployment to provide video transmission with QoE support. Specifically, the controller considers multiple UAV context information for routing and UAV placement decisions, namely, UAV positions, UAV movement trajectories, and residual energy.

This article can be seen as a starting effort to explore the benefits of using SDN to control a UAVNet system to perform specific tasks in smart environments. As a first step, we focus on applying the proposed SD-UAVNet architecture to assist the video surveillance services in disaster recovery scenarios. The contribution of the article can be summarized as follows:

  • We propose the SD-UAVNet architecture, which includes a SD-UAVNet controller that collects the global context information of UAV nodes, e.g., remaining energy, location information, mobility trajectory, speed, and others. It makes the routing decisions and decides how the UAV nodes should move.

  • We focus on a video surveillance application, where the SD-UAVNet controller determines the locations of the UAV relay nodes by considering their current locations and residual energy for collision avoidance during their movements.

  • The SD-UAVNet architecture contains a detailed energy model of UAV nodes, which includes the energy consumption for both the data transmission and UAV movements.

  • An extensive simulation study validates our system performance, which shows that the SD-UAVNet architecture can effectively mitigate the challenges related to topology changes caused by UAV mobility in order to provide video dissemination with satisfactory QoE.

The article is organized as follows. Section 2 discusses existing work on applying SDN in MANETs or VANETs, and UAVNets for video dissemination services. Section 3 presents the SD-UAVNet architecture and its components. Section 4 describes the simulation settings for the performance evaluation in a video dissemination scenario and also discusses the results. Section 5 concludes the article and foresees future work.

Section snippets

Related work

Using small size UAVs in public and civilian applications are getting more popular. UAVs can be deployed in areas of disaster management, network capacity enhancement, etc. Merwaday and Guvenc [12] proposed to use UAVs as aerial base stations (UABSs) to assist public safety communications during natural disasters, as soon as parts of the communication infrastructure become damaged and dysfunctional. They showed that the deployment of UABSs at optimized locations can improve the throughput gains

SD-UAVNet system for video dissemination services

This section describes the SD-UAVNet architecture, including the SD-UAVNet controller function, the communication protocol between controller and UAV relay nodes, the routing and relay placement algorithms. With the proposed SD-UAVNet architecture, we focus on a disaster recovery scenario using a UAVNet to disseminate real-time videos. The SD-UAVNet controller is responsible for managing the topology of UAV networking and controls the locations and mobility models of all UAV nodes for ensuring

Evaluation

This section presents the methodology and metrics applied to evaluate the SD-UAVNet architecture for video surveillance applications with a relay placement mechanism in a disaster recovery scenario. We evaluated the impact of different UAV speeds on the maintenance of the number of route failures, as well as QoE.

Conclusions

This article proposed the Software-Defined UAV networking architecture, called SD-UAVNet. In our framework, the controller collects the UAV networking topology information, and optimizes UAV nodes’ locations by considering the global UAV contextual information of UAV energy level, overall UAV movement distance, UAV collision avoidance, etc. With the proposed framework, we focus on the problem of using SD-UAVNet to provide live video surveillance under the disaster recovery scenario. The

Acknowledgment

This work is supported by CNPq (431474/2016-8) and EU COST Action CA 15127.

Zhongliang Zhaoreceived his Ph.D. degree from the University of Bern in 2014. Since 2014, he holds an appointment of a Senior Researcher with the University of Bern. He was appointed as a work package leader on several work packages of the EU FP7 MCN project and a Co-Primary Investigator (PI) of the Sino-Swiss Science and Technology Cooperation (SSSTC) project M3WSN. Currently, he is the Technical Coordinator of the SNSF SwissSenseSynergy project and the co-PI of the Orange-funded industry

References (30)

  • D. Rosário et al.

    A beaconless opportunistic routing based on a cross-layer approach for efficient video dissemination in mobile multimedia IoT applications

    Comput. Commun.

    (2014)
  • R. Magán-Carrión et al.

    Optimal relay placement in multi-hop wireless networks

    Ad Hoc Netw.

    (2016)
  • O.B. Maia et al.

    A concise review of the quality of experience assessment for video streaming

    Comput. Commun.

    (2015)
  • Y. Zeng et al.

    Wireless communications with unmanned aerial vehicles: opportunities and challenges

    IEEE Commun. Mag.

    (2016)
  • S. Morgenthaler et al.

    UAVNet: A mobile wireless mesh network using unmanned aerial vehicles

    Globecom Workshops (GC Wkshps), 2012 IEEE

    (2012)
  • L. Pimentel et al.

    Context-aware adaptation mechanism for video dissemination over flying ad-hoc networks

    Proceedings of the IFIP Wireless Days (WD)

    (2014)
  • L. Gupta et al.

    Survey of important issues in uav communication networks

    IEEE Comm. Sur. Tutor.

    (2016)
  • D. Rosário et al.

    Trends in human-centric multimedia networking scenarios

    Proceedings of the 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)

    (2016)
  • W. Lobato et al.

    Platoon-based driving protocol based on game theory for multimedia transmission over VANET

    proceedings of the IEEE Global Communications Conference (GLOBECOM)

    (2017)
  • J.A. Wickboldt et al.

    Software-Defined networking: management requirements and challenges

    IEEE Commun. Mag.

    (2015)
  • L. Tartarini, M.A. Marotta, E. Cerqueira, J. Rochol, C.B. Both, M. Gerla, P. Bellavista, Software-defined handover...
  • Z. Zhao et al.

    Autonomic communications in software-driven networks

    IEEE J. Sel. Areas Commun.

    (2017)
  • H. Kim et al.

    Improving network management with software defined networking

    IEEE Commun. Mag.

    (2013)
  • A. Merwaday et al.

    UAV assisted heterogeneous networks for public safety communications

    2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

    (2015)
  • V. Sharma et al.

    UAV-assisted heterogeneous networks for capacity enhancement

    IEEE Commun. Lett.

    (2016)
  • Cited by (0)

    Zhongliang Zhaoreceived his Ph.D. degree from the University of Bern in 2014. Since 2014, he holds an appointment of a Senior Researcher with the University of Bern. He was appointed as a work package leader on several work packages of the EU FP7 MCN project and a Co-Primary Investigator (PI) of the Sino-Swiss Science and Technology Cooperation (SSSTC) project M3WSN. Currently, he is the Technical Coordinator of the SNSF SwissSenseSynergy project and the co-PI of the Orange-funded industry project Context Awareness Engine.

    Pedro Cuminograduated in Computer Engineering at the Federal University of Pará, Brazil. Currently, he is doing a Master degree in Computer Science at the Federal University of Pará, Brazil. His current research interests include the following: Smart Cities, Internet of Things, FANET, Quality of Experience, and Software Defined Network.

    Arnaldo Souzagraduated in Information Systems at the Federal University of Pará, Brazil. Currently, he is doing a Master degree in Computer Science at the Federal University of Pará, Brazil. His current research interests include the following: FANET, Quality of Experience, and Software Defined Network.

    Denis Rosárioreceived his PhD degree in Electrical Engineering at the Federal University of Pará, Brazil with joint supervision undertaken by the Institute of Computer Science and Applied Mathematics of University of Bern, Switzerland in 2014. Currently, he is a Professor at Federal University of Pará. His current research interests include the following topics: Multimedia adaptation, Wireless Networks, FANET, VANET, Mobility, Quality of Experience, and Software Defined Network.

    Torsten Braungot his Ph.D. degree from University of Karlsruhe (Germany) in 1993. From 1994 to 1995, he was a guest scientist at INRIA Sophia-Antipolis (France). From 1995 to 1997, he worked at the IBM European Networking Centre Heidelberg (Germany) as a project leader and senior consultant. Since 1998, he is a full professor of Computer Science at University of Bern. Currently, he holds an appointment of a vice president of the SWITCH (Swiss Research and Education Network Provider) Foundation since 2011. He was a Director of the Institute of Computer Science and Applied Mathematics at University of Bern between 2007 and 2011. He is serving as Deputy Dean of the Faculty of Science, University of Bern since 2017. He received best paper awards from LCN 2001, WWIC 2007, EE-LSDS 2013, WMNC 2014, and the ARMS-CC-2014 Workshop as well as the GI-KuVS Communications Software Award in 2009. In the scope of EU funded projects, he was leading WPs of FP6-EUQOS and FP7-MCN. Moreover, he coordinated national projects such as SNSF SwissSenseSynergy and SNSF CONTACT.

    Eduardo Cerqueirareceived his Ph.D. in Informatics Engineering from the University of Coimbra, Portugal (2008). He is an associate professor at the Faculty of Computer Engineering of the Federal University of Pará (UFPA) in Brazil, as well as invited researcher at the Network Research Lab at UCLA-USA and Centre for Informatics and Systems of the University of Coimbra-Portugal. His publications include 5 edited books, 5 book chapters, 4 patents and over than 180 papers in national/international refereed journals/conferences. He has been serving as a Guest Editor for 6 special issues of various peer-reviewed scholarly journals. His research involves Multimedia, Future Internet, Quality of Experience, Mobility and Ubiquitous Computing.

    Mario Gerlais a Professor in the Computer Science at UCLA. He holds an Engineering degree from Politecnico di Milano, Italy and the Ph.D. degree from UCLA. He became IEEE Fellow in 2002 is a recipient of the 2018 IEEE INFOCOM Achievement Award. At UCLA, he was part of the team that developed the early ARPANET protocols under the guidance of Prof. Leonard Kleinrock. At Network Analysis Corporation, New York, from 1973 to 1976, he helped transfer ARPANET technology to Government and Commercial Networks. He joined the UCLA Faculty in 1976. At UCLA he has designed and implemented network protocols including ad hoc wireless clustering, multicast (ODMRP and CodeCast) and Internet transport (TCP Westwood). His publications include over than 720 papers in national/international refereed journals/conferences. His current research projects cover: design and performance evaluation of protocols and control schemes for wireless networks; cloud computing; software defined networks; routing, congestion control and bandwidth allocation in wide area network.

    View full text