UAVs assessment in software-defined IoT networks: An overview

Al‐Turjman, Fadi; Abujubbeh, Mohammad; Malekloo, Arman; Mostarda, Leonardo

doi:10.1016/j.comcom.2019.12.004

Cited by 76 publications

(28 citation statements)

References 114 publications

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“…SDN-based UAV networks have recently attracted the interest of scientific researchers and industry [178]. The SDN architecture aims to flexibly manage UAV networks [179]. The deployment of SDN technology in UAV networks allows them to efficiently perform tasks in a timely manner [180].…”

Section: Sdn-based Uav Networkmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Section: Sdn-based Uav Networkmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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“…In [71], SDN/NFV and microsatellite technology were integrated into the S-IoT concept, whereas SDN/NFV-enabled flying and ground moving networks were considered in [72]. The feasibility of multi-UAV SDN-enabled drone base stations (DBS) for emergency and surveillance monitoring scenarios was also studied in [73] and emphasis was placed on the effectiveness issues and cybersecurity aspects.…”

Section: State Of the Art In Wireless Communication And Networking Tementioning

confidence: 99%

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Michailidis

Potirakis

Kanatas

2020

IoT

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During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods.

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“…The UAVs forms a collaborative network of drones (IoD) [14] to gather and consolidate environment related data such as surveillance data in smart cities or battle field monitoring, the data is further send to the controlling user through some ground center [15], [16]. As per [17]- [19] the prospect of drones as a commercial usage is not far off it has already begun and along with usage in many B2B application, IoD has become one of the most invested technology for business. Currently, IoD is being used as a tool in variety of areas like a package delivery option but are also being used as a tool for police, first-aid vehicles, high-tech photography, wildlife research, search, rescue and many more [17], [18] as shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Securing Smart City Surveillance: A Lightweight Authentication Mechanism for Unmanned Vehicles

et al. 2020

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The significance of the Internet of Drones (IoD) is increasing steadily and now IoD is being practiced in many military and civilian-based applications. IoD facilitates real-time data access to the users especially the surveillance data in smart cities using the current cellular networks. However, due to the openness of communication channel and battery operations, the drones and the sensitive data collected through drones are subject to many security threats. To cope the security challenges, recently, Srinivas et al. proposed a temporal credential based anonymous lightweight authentication scheme (TCALAS) for IoD networks. Contrary to the IoD monitoring framework proposed by Srinivas et al., their own scheme can work only when there is one and only one cluster/flying zone and is not scalable. Moreover, despite their claim of robustness, the investigation in this paper reveals that Srinivas et al.'s scheme cannot resist traceability and stolen verifier attacks. Using the lightweight symmetric key primitives and temporal credentials, an improved scheme (iTCALAS) is then proposed. The proposed scheme while maintaining the lightweightness provides security against many known attacks including traceability and stolen verifier. The proposed iTCALAS extends scalability and can work when there are several flying zone/clusters in the IoD environment. The formal security proof along with automated verification using ProVerif show robustness of proposed iTCALAS. Moreover, the security discussion and performance comparisons show that the iTCALAS provides the known security features and completes authentication in just 2.295 ms.

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UAVs assessment in software-defined IoT networks: An overview

Cited by 76 publications

References 114 publications

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Softwarization of UAV Networks: A Survey of Applications and Future Trends

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Securing Smart City Surveillance: A Lightweight Authentication Mechanism for Unmanned Vehicles

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