LCD: Light‐weight control model for data plane in software‐defined wireless sensor networks

Huang, Meigen; Yu, Bin

doi:10.1002/ett.3557

Cited by 5 publications

(8 citation statements)

References 24 publications

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“…SDN's design is made up of three distinct layers: the application layer, the control layer, and the infrastructure (or data) layer. 22 Software implementations are used for advanced networking configuration and applications. By configuring flow tables on the network's switches, SDN may simply manage how the network behaves, including how data packets are routed.…”

Section: System Modelmentioning

confidence: 99%

“…Using software‐defined networking (SDN) 22 as a management framework for the multiple‐BBU pool architecture is one viable option. SDN's design is made up of three distinct layers: the application layer, the control layer, and the infrastructure (or data) layer 22 . Software implementations are used for advanced networking configuration and applications.…”

Section: System Modelmentioning

confidence: 99%

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Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

Abdelhakam

Elmesalawy

Ibrahim

et al. 2022

Trans Emerging Tel Tech

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Thanks to their mobility, flexibility, and adaptive altitude, unmanned aerial vehicles (UAVs) have been admitted for several applications in wireless communication systems. In particular, UAVs can function as aerial base stations (BSs) to increase cellular network capacity and provide persistent coverage. Due to the line-of-sight channels between UAVs and user equipments (UEs), strong co-channel interference may be generated in the network. In this article, a joint transmission coordinated multi-point technique is considered in the downlink cellular network to effectively manage the interference brought by UAVs. A two-timescale optimization approach is proposed to prevent exchanging absurd messages and channel information between the transmission points. The objective of the proposed method is to maximize the sum rate of the network subject to the constraints on UEs' quality of service as well as per-ground BS and per-UAV total transmitted power. The proposed approach is decomposed into long-term and short-term problems. The long-term problem aims to jointly optimize the UEs' clusters, the UAVs' locations, and the initial transmit beamforming vectors based on large-scale channel gain. This problem is a mixed-integer nonlinear programming optimization problem, which is nondeterministic polynomial hard. Meanwhile, the short-term problem requires obtaining the transmit beamforming vectors according to the overall channel vector. This problem is also a non-convex optimization problem. To address these challenges, two algorithms are proposed based on difference of convex functions programming and successive convex approximation technique to deal with these two problems. Extensive simulations show that the performance of the proposed approach outperforms the comparable algorithms.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

Abdelhakam

Elmesalawy

Ibrahim

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Please note that nodes are divided into certain zones and each zone is under supervision of exactly one controller. Please note that, improvement in operation of SD‐WSN depends on three aspects—suitable controller placement and re‐election, 49 efficient management of Openflow switches, 50 and proper control of data plane 51 Virtualization layer : The key nodes of the sensor network in the PL map to virtual key nodes, in order to form the virtual node layer (virtual layer). Control layer (CL) : This layer consists of SDN controllers which control routing, sleep monitoring, and other services which are components of network management.…”

Section: Network Model Detailsmentioning

confidence: 99%

Minimum energy transmission forest‐based geocast in software‐defined wireless sensor networks

Banerjee

Sufian

Sadiq

et al. 2021

Trans Emerging Tel Tech

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Wireless Sensor Networks (WSNs)‐based geographic addressing and routing have many potential applications. Geocast protocols should be made energy efficient to increase the lifetime of nodes and packet delivery ratio. This technique will increase the number of live nodes, reduce message costs, and enhance network throughput. All geocast protocols in the literature of WSN apply mostly restricted flooding and perimeter flooding, which is why still the redundancy they produce significantly high message transmission costs and unnecessarily eats up immense energy of the nodes. Moreover, perimeter flooding cannot succeed in the presence of holes. The present article models WSN with software‐defined (SD) constructs where the network area is divided into some zones. Energy‐efficient transmission tree(s) are constructed in the geocast area to organize the flow of data packets and their links. Therefore, redundancy in the transmission is eliminated while maintaining network throughput as good as regular flooding. Besides, this study introduces a fuzzy controllers named SELECT‐STARTER and GEOCAST‐PROPAGATOR for intellgent energy tansmission control‐making. The proposed technique significantly reduces energy cost and improves nodes' lifetime to function for higher time duration and produce a higher data packet delivery ratio. To the best of the author's knowledge, this is the first work on geocast in SD‐WSNs.

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“…In this subsection, we provide detailed discussion on comparative analysis of our proposed SeC‐SDWSN. We compare our proposed SeC‐SDWSN with previous research works traffic load minimization algorithm, PYCRO, GA, and LCD . Comparison between these two works is illustrated in Table .…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Here, SDN is adopted for video streaming and improves QoS by GA‐based routing. Thus, utilizing SDN to achieve better QoS will be the best solution for any other systems such as light weight control (LCD)‐based WSN . In the SDN, the load balancing problem is resolved by introducing a meta‐heuristic technique such as ant colony optimization (ACO), GA, and particle swarm optimization (PSO) …”

Section: Introductionmentioning

confidence: 99%

SeC‐SDWSN: Secure cluster‐based SDWSN environment for QoS guaranteed routing in three‐tier architecture

Vishnu¹,

Manjunath²

2019

Int J Communication

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Software defined wireless sensor network (SDWSN) is a recent evolution in networking that improves network performance and scalability. However, Quality of Service (QoS) and security are major the issues in SDWSN due to inefficient route selection (traffic load minimization algorithm) and insecure cryptography scheme (homomorphic algorithm). This paper proposes novel three-tier architecture for secure cluster-based SDWSN (SeC-SDWSN) environment to ensure QoS and security for WSN using SDN. In the first tier, sensor nodes are segregated into multiple clusters by secure hash tree-based clustering (SHTC) algorithm. Within each secure cluster, data transmission is performed through optimal route selected by adaptive spider monkey optimization (ASMO) algorithm in which two new fitness factors ( F 1 , F 2 ) are formulated by multiple QoS metrics. For data security, parallel advanced encryption standard with cipher block chaining (PAES-CBC) algorithm is proposed. Aggregated ciphertext is transmitted to optimal switch in the second tier by using fuzzy weighted technique for order preference by similarity to ideal solution (FW-TOPSIS) algorithm according to selection criteria. Switches forward the data to sink node based on flow rules deployed by SDN controllers in the third tier. SDN controllers provide global view on the entire network and deploy flow rules on switches in accordance to network status and security level. Extensive simulation in ns-3 shows that the proposed three-tier architecture achieves 5% throughput improvement, 7.8% PDR improvement, and 16% energy consumption improvement.

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LCD: Light‐weight control model for data plane in software‐defined wireless sensor networks

Cited by 5 publications

References 24 publications

Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

Minimum energy transmission forest‐based geocast in software‐defined wireless sensor networks

SeC‐SDWSN: Secure cluster‐based SDWSN environment for QoS guaranteed routing in three‐tier architecture

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