A technique for topography aware dynamic controller placement in SDN

Bhowmik, Chaitali; Gayen, Tirthankar

doi:10.1007/s41870-022-00969-y

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…The control nodes which satisfies the conditions are known as optimal controllers, and they update their position by using Equation (15). In order to improve the routing performance, position of controller position is replaced with updated location of optimal location using Equation (18). In that way, the optimal controllers are placed near the sensor nodes for better data transmission process.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…However, those techniques take too many iterations to obtain optimum global values effectively. Moreover, most works study solving multi‐objective problems, and effective study is highly required for today's IoT applications 18–20 . In addition, clustering is highly required for heterogeneous networks to minimize energy consumption and traffic over the network.…”

Section: Introductionmentioning

confidence: 99%

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SO‐CPP: Sailfish optimization‐based controller placement in IoT‐enabled software‐defined wireless sensor networks

Kumari Rajoriya,

Gupta

2024

Int J Communication

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SummaryOne of the expanding network topologies that is frequently utilized to improve network development by successfully separating the control plane and data plane is software‐defined networking (SDN). In order to function inside complex sensor networks, the SDWSN system frequently relies on centralized controller logic that pulls global network information. In wireless sensor networks (WSNs), using several SDN controllers is known as a promising strategy due to reliability and performance considerations. However, using numerous controllers increases the synchronization overhead between the controllers. Consequently, it is a difficult research challenge to discover the best placement of SDN controllers to enhance the performance of a WSN, subject to the maximum number of controllers calculated based on the synchronization overhead. This research introduces a novel technique to overcome the controller placement problem (CPP) by optimizing multi‐constraints within the sensor networks. For selecting the optimal controllers and placing them in an optimal location, a novel sailfish optimization (SO) strategy is introduced that can enhance the search space and maintain optimal global values throughout the iteration. Then, node clustering is performed using the fuzzy‐C‐means (FCM) clustering technique, which can reduce energy consumption and path delay within the network. The overall latency obtained by the proposed method is about 0.51 and 0.56 ms, and a total run time of 4 ms for both single sink and multi‐sink, respectively. The proposed method is implemented in the MATLAB platform, and different performance metrics are analyzed and compared with existing techniques.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%