Efficient whale optimisation algorithm-based SDN clustering for IoT focused on node density

Al-Janabi, Thair; Al-Raweshidy, Hamed S.

doi:10.1109/medhocnet.2017.8001651

Cited by 25 publications

(19 citation statements)

References 16 publications

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“…The Whale Optimization Algorithm, which considers the residual energy, communication cost and nodes density, is proposed in [33] to enhance the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things

et al. 2021

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Software Defined Networking (SDN) provides a new perspective for the Internet of Things (IoT), since, with the separation of the control from the data planes, it is viable to optimise the traditional networks operation management. In particular, the SDN Controller has a global vision of the network of sensors/actuators domain, allowing real-time network nodes and data flows reconfiguration. As a consequence, devices, usually facing limited communications and computing resources, are relieved of the route selection task in a distributed and, thus, suboptimal way. This paper proposes a SDN-IoT architecture, specifically focusing on the Controller design, which dynamically optimises in real time the end-to-end flows delivery. In particular, the dynamic routing policy adaptation is based on the real-time estimation of the network status and it allows jointly minimising the end-to-end latency and energy consumption and, consequently, to improve the network life time. The performance of the proposed approach is analysed in terms of the average latency, energy consumption and overhead, pointing out a better behaviour in comparison with the existing distributed approaches.

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“…The Whale Optimization Algorithm, which considers the residual energy, communication cost and nodes density, is proposed in [33] to enhance the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things

et al. 2021

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“…Although this routing switching environment is a valuable contribution for SDN-based WSNs solutions, this scheme just considering the existing state-of-the-art routing protocols such as AODV, DSDV, LSR, and DLSR, this solution lacks the proposal of novelty routing protocol; meanwhile, AODV, DSDV, LSR, and DLSR routing protocols are not designed for SDN-based WSNs, so the required performance is still a challenge for this solution. Non-linear Weight Particle Swarm Optimization (NWPSO) algorithm-based routing protocol: In [ 27 ], the authors proposed a routing protocol based upon Non-linear Weight Particle Swarm Optimization (NWPSO) algorithm to implement centralized multi-tasking for SDN-based WSNs (SDWSNs). This protocol develops the clustering mechanism of selecting suitable controllers from the sensor nodes with maximum residual energy, and these controller nodes are responsible for collecting reports at the intra-cluster communication level.…”

Section: Related Workmentioning

confidence: 99%

“…Non-linear Weight Particle Swarm Optimization (NWPSO) algorithm-based routing protocol: In [ 27 ], the authors proposed a routing protocol based upon Non-linear Weight Particle Swarm Optimization (NWPSO) algorithm to implement centralized multi-tasking for SDN-based WSNs (SDWSNs). This protocol develops the clustering mechanism of selecting suitable controllers from the sensor nodes with maximum residual energy, and these controller nodes are responsible for collecting reports at the intra-cluster communication level.…”

Section: Related Workmentioning

confidence: 99%

Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)

Shafique

Cao

Aslam

et al. 2020

Sensors

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The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.

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“…A large variety of meta-heuristic algorithms such as ACO [183][184][185][186][187][188][189][190][191], EAs [192][193][194], GAs [195][196][197][198][199][200][201][202][203], PSO [204][205][206][207][208][209], SA [210][211][212], bee colony optimisation-based [213,214], whale optimisation [215,216], FFO [217], BA [85], TLBO [87] and GWO [207] were used in SDN.…”

Section: Meta-heuristic Algorithms Used In Sdnmentioning

confidence: 99%

“…WOA also has been used for achieving clustering‐based routing for heterogeneous, randomly distributed and dense IoT networks [216]. The proposed approach consisted of two stages: set‐up stage and transmission stage.…”

Section: Artificial Intelligence In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

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Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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Efficient whale optimisation algorithm-based SDN clustering for IoT focused on node density

Cited by 25 publications

References 16 publications

Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things

Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things

Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)

Artificial intelligence enabled software‐defined networking: a comprehensive overview

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