SD-NFV as an Energy Efficient Approach for M2M Networks Using Cloud-Based 6LoWPAN Testbed

Al-Kaseem, Bilal R.; Al-Raweshidyhamed, Hamed S.

doi:10.1109/jiot.2017.2704921

Cited by 30 publications

(11 citation statements)

References 23 publications

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“…In [4], in order to quickly respond to the challenges brought by flexible deployment of the Internet of Things, the author combined NFV technology with SDN technology and proposed a general SDN-IoT architecture. In [29], to verify and test the 6LoWPAN testbed, a customized Software Defined-Network Functioning Virtualisation (SD-NFV) is proposed.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

et al. 2019

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Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

et al. 2019

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“…Network function virtualization is used to reduce the cost of the network, the combination of SDN and NFV is promising in IoT systems. SD-NFV based 6LoWPAN [25], [26] is an energy-efficient mechanism to enhance the lifetime of IoT nodes.…”

Section: A Sdn and Iotmentioning

confidence: 99%

LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoT

Ota

Dong

2019

IEEE J. Select. Areas Commun.

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Internet of Things (IoT) becomes a very important area for providing various services on connected smart devices. For isolation of different services in IoT, software defined networking (SDN) based virtual networks will be a scalable and flexible solution. However, in a large-scale IoT, as smart devices will move long distance between different positions, virtual network management becomes very difficult in providing network services. In this paper, we propose LS-SDV, an efficient virtual network management framework in large-scale software defined IoT (SDIoT). In this framework, we design a two-layer distributed control plane to manage devices and virtual networks in a large-scale environment. To the best of our knowledge, LS-SDV is the first work to apply distributed control plane for virtual network management in softwarized networks. Moreover, based on the novel structure of LS-SDV, we also provide a solution for network flow scheduling through network analysis. We evaluate the performance of our framework and virtual network management by extensive simulation and experiment in an open SDN framework.

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“…A large number of published studies sought to investigate the effectiveness of cloud computing and programmable network concepts in WSN. To the best of our knowledge, none of this research involved investigating the incorporation of SDN, NFV, and cloud computing in IoT, except for that of Bizanis et al [15] and Al-Kaseem et al [29]. The works presented by Yang et al [30] and Li and Chen [31] are relevant to both SDN and virtualization in wireless and mobile networks.…”

Section: Related Workmentioning

confidence: 99%

“…From the published studies in the literature explored previously, it would appear that there has been limited research that integrates SDN and NFV with available 6LoWPAN hardware. In contrast to these proposed solutions, our published work in [29] comes up with a new architecture to enhance the performance of 6LoWPAN network and analyze the network from energy efficiency perspectives. The proposed approach comprises of a tailored SDN controller and NFV technology, where these technologies being deployed in 6LoWPAN network with cloud connectivity.…”

Section: Related Workmentioning

confidence: 99%

“…When designing 6LoWPAN testbed with node re-tasking, there are some systematic and situational challenges need to be considered, these include: a) The cost and type of network infrastructure that support different new technologies; b) Available software tools and hardware platforms to build and manage the network; c) The risk of shifting to a new technology to enhance the performance of the existing network; d) The ability of deploying new protocols, procedures and applications that support other technologies. It is therefore, this paper distinguishes from our previous work [29] [34] was released and this was revised in 2006 [35]. This standard defines the radio communication at 868 MHz, 915 MHz and 2.4 GHz for low-power, short range and low-data rate wireless embedded devices.…”

Section: Related Workmentioning

confidence: 99%

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End-to-End Delay Enhancement in 6LoWPAN Testbed Using Programmable Network Concepts

Al-Kaseem

Al-Dunainawi

Al-Raweshidy

2019

IEEE Internet Things J.

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This paper introduces a proof-of-concept 6LoWPAN testbed to study the integration of programmable network technologies in relaxed throughput and low-power IoT devices. Open source software and hardware platforms are used in the implemented testbed to increase the possibility of future network extension. The proposed architecture offers end-to-end connectivity via the 6LoWPAN gateway to integrate IPv6 hosts and the low data rate devices directly. Nowadays, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are the most promising technologies for dealing with the massive increase in M2M devices and achieving agile traffic. The developed approach in this paper is entitled tailored Software Defined-Network Function Virtualization (SD-NFV), which is aimed at reducing the end-to-end delay and improving the energy depletion in sensor nodes. Experimental scenarios of the implemented testbed are conducted using a simple sensing application and the obtained results indicate that the introduced approach is appropriate for constrained IoT devices. By utilizing SD-NFV scheme in 6LoWPAN network, the data delivery ratio increased by 5-14%, the node operational time prolonged by 70%, the end-to-end latency for gathering sensor data minimized by ≈160%, and the latency for transmitting control messages to a specified node diminished by ≈63% when compared to a traditional (non SDN-enabled) 6LoWPAN network.

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SD-NFV as an Energy Efficient Approach for M2M Networks Using Cloud-Based 6LoWPAN Testbed

Cited by 30 publications

References 23 publications

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoT

End-to-End Delay Enhancement in 6LoWPAN Testbed Using Programmable Network Concepts

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