Application of Fruit Fly Optimization Algorithm for Single-Path Routing in Wireless Sensor Network for Node Capture Attack

2019 ◽  
pp. 129-136
Author(s):  
Ruby Bhatt ◽  
Priti Maheshwary ◽  
Piyush Shukla
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Node Capture Attack ◽  
Node Capture ◽  
Single Path

scholarly journals Optimized and Executive Survey of Physical Node Capture Attack in Wireless Sensor Network

2014 ◽  
Vol 6 (11) ◽  
pp. 26-34
Author(s):  
Bhavana Butani ◽  
◽  
Piyush Kumar Shukla ◽  
Sanjay Silakari ◽  
◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Node Capture Attack ◽  
Node Capture ◽  
Physical Node

Proficient Clustering algorithm for Wireless Sensor Networks

2020 ◽  
pp. 249-261
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

scholarly journals Wireless Sensor Network Coverage Optimization Based on Fruit Fly Algorithm

2018 ◽  
Vol 14 (06) ◽  
pp. 58 ◽  
Author(s):  
Ren Song ◽  
Zhichao Xu ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fruit Fly ◽  
Effective Radius ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Improved Algorithm

<p class="0abstract"><span lang="EN-US">To solve the defect of traditional node deployment strategy, the improved <a name="_Hlk502130691"></a>fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. </span><span lang="EN-US">Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA)</span><span lang="EN-US">was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition.</span><span lang="EN-US">Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically.</span><span lang="EN-US">Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network.</span><span lang="EN-US">The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved.</span><span lang="EN-US">The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks.</span></p>

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