Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs
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, o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/11/3231 |
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| author | Shirin Tahmasebi Mohadeseh Safi Somayeh Zolfi Mohammad Reza Maghsoudi Hamid Reza Faragardi Hossein Fotouhi |
| author_facet | Shirin Tahmasebi Mohadeseh Safi Somayeh Zolfi Mohammad Reza Maghsoudi Hamid Reza Faragardi Hossein Fotouhi |
| author_sort | Shirin Tahmasebi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:20:20Z |
| format | Article |
| id | doaj.art-e06705bb97e84fd3ab56469c8b04bef5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:20:20Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e06705bb97e84fd3ab56469c8b04bef52023-11-20T03:02:32ZengMDPI AGSensors1424-82202020-06-012011323110.3390/s20113231Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNsShirin Tahmasebi0Mohadeseh Safi1Somayeh Zolfi2Mohammad Reza Maghsoudi3Hamid Reza Faragardi4Hossein Fotouhi5Department of Computer Engineering, Sharif University of Technology, Tehran 11365-11155, IranShariaty Technical College, Technical and Vocational University, Tehran 13114-16846, IranSchool of Computer Engineering, University of Science and Technology, Tehran 16851-18918, IranZand Institute of Higher Education, Shiraz 71887-73489, IranSchool of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44 Stockholm, SwedenSchool of Innovation, Design, and Engineering, Mälardalen University, 721 23 Västerås, SwedenDue 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.https://www.mdpi.com/1424-8220/20/11/3231wireless sensor networkssoftware defined networkscontroller node placementCuckoo optimization algorithmsynchronization cost |
| spellingShingle | 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 wireless sensor networks software defined networks controller node placement Cuckoo optimization algorithm synchronization cost |
| title | Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs |
| title_full | Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs |
| title_fullStr | Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs |
| title_full_unstemmed | Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs |
| title_short | Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs |
| title_sort | cuckoo pc an evolutionary synchronization aware placement of sdn controllers for optimizing the network performance in wsns |
| topic | wireless sensor networks software defined networks controller node placement Cuckoo optimization algorithm synchronization cost |
| url | https://www.mdpi.com/1424-8220/20/11/3231 |
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