Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 11; p. 3231
• Main Authors: Tahmasebi, Shirin, Safi, Mohadeseh, Zolfi, Somayeh, Maghsoudi, Mohammad Reza, Faragardi, Hamid Reza, Fotouhi, Hossein
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 06.06.2020; MDPI
• Subjects: Business metrics; Computer control; Controller node placement; Controllers; Cuckoo optimization algorithm; Energy consumption; Failure; Fault tolerance; Genetic algorithms; Heuristic algorithms; Integer Linear Programming; Integer programming; Linear programming; Meta heuristic algorithm; Multiple controllers; Network performance; Optimization; Optimization algorithms; Optimization problems; Quality of service; Quantum theory; Sensors; Simulated annealing; Software defined networking (SDN); Software defined networks; Software reliability; State-of-the-art methods; Synchronization; Synchronization cost; Wireless networks; Wireless sensor network (WSNs); Wireless sensor networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20113231

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.