SDN-(UAV)ISE: Applying Software Defined Networking to Wireless Sensor Networks with Data Mules

Mertens, J.S.; Milotta, Giuseppe Massimiliano; Nagaradjane, Prabagarane; Morabito, Giacomo

doi:10.1109/wowmom49955.2020.00061

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…In addition, SDN permits directly programmable network control by unbundling the data, control, and application layers. Deploying an SDN is a natural way to enhance service quality [43,44] as more and more UAV applications depend on real-time video streaming. With SDN, the controller can keep a close eye on data traffic and prevent many attacks that would otherwise be possible on a UAV despite its limited resources.…”

Section: A Software-defined Networking (Sdn)mentioning

confidence: 99%

Design and Development of a Secure Data Transmission Protocol for Uav

2023

ecb

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Rapid technological advancements have made it possible for unmanned aerial vehicles (UAVs) to be used in a variety of applications, and since they are so mobile, UAV systems may work together to complete a wide variety of missions. UAVs have a variety of applications in both military and commercial sectors. UAVs can also be deployed in civil sectors like search and rescue operations. In recent years, UAVs were used illegally. These attacks are becoming more common and can have far-reaching consequences. As a result, associated industries and standards agencies are investigating potential solutions for protecting UAV infrastructure. Researchers have been looking for robust and secure protocols to protect UAVs from cybercriminals. However, hackers can easily exploit many vulnerabilities in the existing protocols. Therefore, it is essential to research and examines the current security protocols employed in UAVs to identify and fix their flaws. The goal of this survey is to uncover more about the secure protocol for UAV data transmission. The survey examines the existing methods described in the literature to solve the security issues that arise during data transmission between UAVs and base stations. Each protocol's advantages and disadvantages are detailed, along with suggestions for future research that could improve UAV security. Researchers and professionals in the area will find this survey a valuable resource, which provides a brief description of the secure data transmission protocols used in UAVs.

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Section: A Software-defined Networking (Sdn)mentioning

confidence: 99%

Design and Development of a Secure Data Transmission Protocol for Uav

2023

ecb

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“…The decision module based on ML is placed in the application plane of the SDWSN architecture. SDN-(UAV)ISE is introduced in [145] for WSNs with data mules. The network architecture, shown in Fig.…”

Section: A Mobilitymentioning

confidence: 99%

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

et al. 2022

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“…Mertens et al [41] proposed SDN-(UAV)ISE in which a drone acts as a mobile sink (a.k.a mule). The SDN controller, which is based on SDN-WISE applies a decision tree learning algorithm on the data from sensors to predict the position of the drone.…”

Section: Sdn-enabled Iot Network Architecturesmentioning

confidence: 99%

“…Infrastructure Metric Predictive Mechanism mRPL [13] fixed and mobile ETX and RSSI average RSSI/SNR mRPL+ [20] fixed and mobile ETX and RSSI average RSSI/SNR (overhearing) ARMOR [26] all can be mobile Time To Reside Relative velocity RMA-RP [27] fixed and mobile Time To Stay 2 consecutive RSSI values D-trickle [23] all can be mobile ETX,ELT,RSSI,distance -Kalman-RPL [7] fixed and mobile predicted ETX Kalman Filter EKF-RPL [9] fixed and mobile position of MN Extended Kalman Filter EKF-LOADng [8] fixed and mobile position of MN Extended Kalman Filter DAO projection [33] No mobility priority for projected routes -Coral SDN [35] fixed and mobile OF and trickle set by controller -SD-MIoT [36] fixed and mobile link quality proactive route installation SDN-UAise [41] Mobile sink not RPL-based Decision tree MobiFog [42] fixed and mobile ETX and RSSI Average RSSI MMF-SDN [40] fixed and mobile not RPL-based -FTS-SDN [39] fixed and mobile not RPL-based -BRPL [18] all can be mobile backlog drift plus ETX Lyapunov Optimization GTM-RPL [25] fixed and mobile ETX Nash Equilibrium RL-Probe [24] all can be mobile ETX (same as RPL) epsilon-greedy learning…”

Section: Mobility Solutionsmentioning

confidence: 99%

SDMob: SDN-Based Mobility Management for IoT Networks

Rabet

Selvaraju

Fotouhi

et al. 2022

JSAN

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Internet-of-Things (IoT) applications are envisaged to evolve to support mobility of devices while providing quality of service in the system. To keep the connectivity of the constrained nodes upon topological changes, it is of vital importance to enhance the standard protocol stack, including the Routing Protocol for Lossy Low-power Networks (RPL), with accurate and real-time control decisions. We argue that devising a centralized mobility management solution based on a lightweight Software Defined Networking (SDN) controller provides seamless handoff with reasonable communication overhead. A centralized controller can exploit its global view of the network, computation capacity, and flexibility, to predict and significantly improve the responsiveness of the network. This approach requires the controller to be fed with the required input and to get involved in the distributed operation of the standard RPL. We present SDMob, which is a lightweight SDN-based mobility management architecture that integrates an external controller within a constrained IoT network. SDMob lifts the burden of computation-intensive filtering algorithms away from the resource-constrained nodes to achieve seamless handoffs upon nodes’ mobility. The current work extends our previous work, by supporting multiple mobile nodes, networks with a high density of anchors, and varying hop-distance from the controller, as well as harsh and realistic mobility patterns. Through analytical modeling and simulations, we show that SDMob outperforms the baseline RPL and the state-of-the-art ARMOR in terms of packet delivery ratio and end-to-end delay, with an adjustable and tolerable overhead. With SDMob, the network provides close to 100% packet delivery ratio (PDR) for a limited number of mobile nodes, and maintains sub-meter accuracy in localization under random mobility patterns and varying network topologies.

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SDN-(UAV)ISE: Applying Software Defined Networking to Wireless Sensor Networks with Data Mules

Cited by 14 publications

References 19 publications

Design and Development of a Secure Data Transmission Protocol for Uav

Design and Development of a Secure Data Transmission Protocol for Uav

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

SDMob: SDN-Based Mobility Management for IoT Networks

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