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Shirin Tahmasebi
Sharif University of Technology, Tehran, Iran

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Journal article
Published: 11 December 2020 in Computer Networks
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Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve better fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network’s state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously. In this paper, we first formulate the controller placement problem as a multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the Cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs.

ACS Style

Shirin Tahmasebi; Nayereh Rasouli; Amir Hosein Kashefi; Elmira Rezabeyk; Hamid Reza Faragardi. SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs. Computer Networks 2020, 185, 107727 .

AMA Style

Shirin Tahmasebi, Nayereh Rasouli, Amir Hosein Kashefi, Elmira Rezabeyk, Hamid Reza Faragardi. SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs. Computer Networks. 2020; 185 ():107727.

Chicago/Turabian Style

Shirin Tahmasebi; Nayereh Rasouli; Amir Hosein Kashefi; Elmira Rezabeyk; Hamid Reza Faragardi. 2020. "SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs." Computer Networks 185, no. : 107727.

Journal article
Published: 06 June 2020 in Sensors
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Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

ACS Style

Shirin Tahmasebi; Mohadeseh Safi; Somayeh Zolfi; Mohammad Reza Maghsoudi; Hamid Reza Faragardi; Hossein Fotouhi. Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs. Sensors 2020, 20, 3231 .

AMA Style

Shirin Tahmasebi, Mohadeseh Safi, Somayeh Zolfi, Mohammad Reza Maghsoudi, Hamid Reza Faragardi, Hossein Fotouhi. Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs. Sensors. 2020; 20 (11):3231.

Chicago/Turabian Style

Shirin Tahmasebi; Mohadeseh Safi; Somayeh Zolfi; Mohammad Reza Maghsoudi; Hamid Reza Faragardi; Hossein Fotouhi. 2020. "Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs." Sensors 20, no. 11: 3231.