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

作     者：Tahmasebi, Shirin Safi, Mohadeseh Zolfi, Somayeh Maghsoudi, Mohammad Reza Faragardi, Hamid Reza Fotouhi, Hossein 

作者机构：Sharif Univ Technol Dept Comp Engn Tehran *** Iran Tech & Vocat Univ Shariaty Tech Coll Tehran *** Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Zand Inst Higher Educ Shiraz *** Iran KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden Malardalen Univ Sch Innovat Design & Engn S-72123 Vasteras Sweden 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2020年第20卷第11期

页      面：3231.-3231.页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Swedish Foundation for Strategic Research via the FiC project Swedish Research Council (Vetenskapsradet) through the MobiFog starting grant Swedish Knowledge Foundation (KKS) through the FlexiHealth Prospekt EU Celtic_Next/Vinnova project, Health5G (Future eHealth powered by 5G) 

主　　题：wireless sensor networks software defined networks controller node placement Cuckoo optimization algorithm synchronization cost 

摘      要：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.