SD-MIoT: A Software-Defined Networking Solution for Mobile Internet of Things

Theodorou, Tryfon; Mamatas, Lefteris

doi:10.1109/jiot.2020.3027427

Cited by 35 publications

(21 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have lately used ML techniques to tackle mobility in WSNs through SDN. Theodorou et al [144] proposed SD-MIoT, which is an SDN-based solution for mobile low-power IoT applications. To detect connectivity changes, a square transition matrix is calculated at two subsequent adjacent matrices as follows:…”

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

View full text Add to dashboard Cite

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Theodorou et al [36] proposed SD-MIoT, in which RPL is assisted by an SDN controller. The SDN controller is responsible to detect the mobility of the nodes by maintaining an adjacency graph.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Theodorou et al [131] proposed SD-MIoT, which is an SDN-based solution for mobile IoT applications. SD-MIoT aims to reduce the control overhead by detecting the mobility behaviour of sensor nodes.…”

Section: A Mobilitymentioning

confidence: 99%

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

Lasso¹,

Marchegiani²,

Jurado³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper is aimed to present a comprehensive survey of relevant research over the period 2012-2021 of Software-Defined Wireless Sensor Network (SDWSN) proposals and Machine Learning (ML) techniques to perform network management, policy enforcement, and network configuration functions. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSNs, mainly the current state-of-art, machine learning techniques, and open issues.

show abstract

SD-MIoT: A Software-Defined Networking Solution for Mobile Internet of Things

Cited by 35 publications

References 44 publications

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

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

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

Contact Info

Product

Resources

About