Fibonacci Tree

2007 ◽  
Keyword(s):  
Fibonacci Tree

Embedding of Fibonacci Tree into Hyper-Star Network

2018 ◽  
Vol 13 (3) ◽  
pp. 1-6 ◽  
Author(s):  
Jong-Seok Kim ◽  
◽  
Hyung Jae Chang ◽  
Keyword(s):  
Star Network ◽  
Fibonacci Tree

scholarly journals Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks using Fibonacci Tree Optimization Algorithm

2021 ◽  
Author(s):  
Liangshun Wu ◽  
Hengjin Cai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Average Energy ◽  
Wireless Sensor ◽  
Scheduling Strategy ◽  
Adaptive Sensing ◽  
Tree Optimization ◽  
Fibonacci Tree

Wireless sensor networks are attractive largely because they need no wired infrastructure. But precisely this feature makes them energy constrained. Recent studies find that sensing behaviors that are otherwise deemed efficient consume comparable energy with communication. The duty cycle scheduling is perceived as contributing to achieving energy efficiency of sensing. Because of different research assumptions and objectives, various scheduling schemes have various emphases. This paper designed an adaptive sensing scheduling strategy. The objective function of the scheduling strategy includes minimizing average energy expenditure and maximizing sensing coverage (reducing event miss-rate), and it requires relatively loose assumptions. We determine the functional relationship between the variables of the objective function and the step-size parameters of the proposed strategy through the numerical fitting. We found that the objective function aggregated by the fitting functions is a bivariate multi-peak function that favors the Fibonacci tree optimization algorithm. Once the optimization of parameters is done, the strategy can be easily deployed and behaves consistently in the coming hours. We name the proposed strategy as “FTOS”. The experimental results show that the Fibonacci tree optimization algorithm gets a better optimistic effect than the comprehensive learning particle swarm optimization (CLPSO) algorithm and differential evolution (DE) algorithm. The FTOS strategy is superior to the fixed time scheduling strategy in achieving the scheduling objectives. It also outperforms other strategies with the same scheduling objectives such as LDAS, BS, DSS and PECAS.

scholarly journals Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5002
Author(s):  
Liangshun Wu ◽  
Hengjin Cai
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Adaptive Sensing ◽  
Tree Optimization ◽  
Fibonacci Tree

Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.

scholarly journals Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hong Lai ◽  
Jun Zhang ◽  
Ming-Xing Luo ◽  
Lei Pan ◽  
Josef Pieprzyk ◽  
...  
Keyword(s):  
Secret Sharing ◽  
Quantum Secret Sharing ◽  
Secret Sharing Scheme ◽  
Sharing Scheme ◽  
Fibonacci Tree ◽  
Quantum Secret Sharing Scheme

scholarly journals Edge-Disjoint Fibonacci Trees in Hypercube

2014 ◽  
Vol 2014 ◽  
pp. 1-4
Author(s):  
Indhumathi Raman ◽  
Lakshmanan Kuppusamy
Keyword(s):  
Data Structure ◽  
Binary Tree ◽  
Fibonacci Numbers ◽  
Recursive Definition ◽  
Communication Latency ◽  
Edge Disjoint ◽  
Fibonacci Tree ◽  
Hypercube Network ◽  
Rooted Binary Tree

The Fibonacci tree is a rooted binary tree whose number of vertices admit a recursive definition similar to the Fibonacci numbers. In this paper, we prove that a hypercube of dimensionhadmits two edge-disjoint Fibonacci trees of heighth, two edge-disjoint Fibonacci trees of heighth-2, two edge-disjoint Fibonacci trees of heighth-4and so on, as subgraphs. The result shows that an algorithm with Fibonacci trees as underlying data structure can be implemented concurrently on a hypercube network with no communication latency.

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