A Quantum-Annealing-Based Approach to Optimize the Deployment Cost of a Multi-Sink Multi-Controller WSN

Nikouei, Reihaneh; Rasouli, Nayereh; Tahmasebi, Shirin; Zolfi, Somayeh; Faragardi, Hamid Reza; Fotouhi, Hossein

doi:10.1016/j.procs.2019.08.036

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The findings are presented in the following Table IX. Portfolio 3 [166,194,219] Big data 3 [43,103,105] Hydrology 2 [39,237] Database 2 [55,198] Sensor 2 [178,212] The table shows that eight research including [37], [93], [179], [208], [210], [222], and [228] are categorized under two problem domains. Meanwhile, machine learning was found to be the most investigated topic using the QA approach as indicated by 26.40% and this was followed by graphics with 24.72%, mathematics with 17.98%, routing with 6.74%, scheduling 6.74%, chemistry computation 5.06%, biology computation 3.37%, security 2.25%, portfolio 1.69%, big data 1.69%, hydrology 1.12%, database 1.12%, and sensors with 1.12%.…”

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

“…It was discovered that 75 combinations of metrics and state-of-theart methods were mapped and compared out of which 60 (80%) stated that the QA performed better, 7 (9.33%) stated they have equivalent performance, and 8 (10.67%) showed that QA has lower performance. The most widely used stateof-the-art method for comparison was found to be simulated annealing as indicated by 21 (42%) of the 50 research which include [130], [149], [152], [154], [161], [168], [171], [189], [212], [217], [218], [223], [229], [238], [239], [242], [243], [246], [249], [251], [252]. This is due to the fact that QA is analogous to simulated annealing (classical approach) but in substitution of thermal activation by quantum tunneling.…”

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

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Implementation of Quantum Annealing: A Systematic Review

Yulianti

Surendro

2022

IEEE Access

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Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.

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Section: ) Results Reporting On Rq2mentioning

confidence: 99%

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

Implementation of Quantum Annealing: A Systematic Review

Yulianti

Surendro

2022

IEEE Access

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“…Finally, the last benchmark includes 200 sensors and 30 candidate locations to place controllers and is referred to as WSN4. System parameters used in our experiments are derived from related papers, including [11,52], which are listed in Table 2. The value of SyncLimit and SyncCost Controller that are required to calculate the number of selected controllers for each benchmark in Equation ( 5) is set according to [32].…”

Section: Experimental Setupsmentioning

confidence: 99%

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

Tahmasebi

Safi

Zolfi

et al. 2020

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.

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“…As a classical meta-heuristic algorithm, the simulated annealing (SA) algorithm is widely used in wireless sensor routing protocols. Despite being a random-step process, the algorithm is prone to stay in a local optimum when the objective function has many high obstacles [28]. To overcome this problem, the quantum annealing (QA) algorithm was developed by replacing the temperature parameter of the SA algorithm with the intensity of the tunneling field.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

Wang,

Liu,

Wang

et al. 2024

Sensors

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The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network’s lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.

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A Quantum-Annealing-Based Approach to Optimize the Deployment Cost of a Multi-Sink Multi-Controller WSN

Cited by 8 publications

References 14 publications

Implementation of Quantum Annealing: A Systematic Review

Implementation of Quantum Annealing: A Systematic Review

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

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

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