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Cuckoo-PC : An evolutionary synchronization-aware placement of SDN controllers for optimizing the network performance in WSNs

Tahmasebi, S. (författare)
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran,Sharif Univ Technol, Dept Comp Engn, Tehran 1136511155, Iran.
Safi, M. (författare)
Shariaty Technical College, Technical and Vocational University, Tehran, Iran,Tech & Vocat Univ, Shariaty Tech Coll, Tehran 1311416846, Iran.
Zolfi, S. (författare)
School of Computer Engineering, University of Science and Technology, Tehran, Iran,Univ Sci & Technol, Sch Comp Engn, Tehran 1685118918, Iran.
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Maghsoudi, M. R. (författare)
Zand Institute of Higher Education, Shiraz, Iran,Zand Inst Higher Educ, Shiraz 7188773489, Iran.
Faragardi, Hamid Reza, 1987- (författare)
KTH,Skolan för elektroteknik och datavetenskap (EECS),KTH Royal Institute of Technology
Fotouhi, Hossein (författare)
Mälardalens högskola,Inbyggda system,Mälardalen Univ, Sch Innovat Design & Engn, S-72123 Västerås, Sweden.
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Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Sharif Univ Technol, Dept Comp Engn, Tehran 1136511155, Iran (creator_code:org_t)
2020-06-06
2020
Engelska.
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:11, s. 1-19
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Controller node placement
Cuckoo optimization algorithm
Software defined networks
Synchronization cost
Wireless sensor networks
Controllers
Heuristic algorithms
Integer programming
Network performance
Quantum theory
Simulated annealing
Software reliability
Synchronization
Integer Linear Programming
Meta heuristic algorithm
Multiple controllers
Optimization algorithms
Optimization problems
Software defined networking (SDN)
State-of-the-art methods
Wireless sensor network (WSNs)
Computer control

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