scholarly journals A Coverage Optimization Method for WSNs Based on the Improved Weed Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5869
Author(s):  
Fang Zhu ◽  
Wenhao Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Coverage Rate ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Invasive Weed ◽  
Coverage Optimization

Wireless sensor networks (WSNs) is a multi-hop wireless network composed of a group of static or mobile sensor nodes in the form of self-organization. Uneven distribution of nodes often leads to the problem of over coverage and incomplete coverage of monitoring areas. To solve this problem, this paper establishes a network coverage optimization model and proposes a coverage optimization method based on an improved hybrid strategy weed algorithm (LRDE_IWO). The improvement of the weed algorithm includes three steps. Firstly, the standard deviation of normal distribution based on the tangent function is used as the seed’s new step size in the seed diffusion stage to balance the ability of the global search and local search of weed algorithm. Secondly, to avoid the problem of premature convergence, a disturbance mechanism combining enhanced Levy flight and the adaptive random walk strategy is proposed in the process of seed breeding. Finally, in competition of invasive weed stage, the differential evolution strategy is introduced to optimize the competition operation process and speed up convergence. The improved weed algorithm is applied to coverage optimization of WSNs. The simulation results show that the coverage rate of LRDE_IWO is increased by about 1% to 6% compared with the original invade weed algorithm (IWO) and the differential evolution invasive weed optimization algorithm (DE_IWO), and the coverage rate of the LRDE_IWO algorithm is increased by 4.10%, 2.73% and 1.19%, respectively, compared with the antlion optimization algorithm (ALO), the fruit fly optimization algorithm (FOA) and the gauss mutation weed algorithm (IIWO). The results prove the superiority and validity of the improved weed algorithm for coverage optimization of wireless sensor networks.

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

scholarly journals A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Li Cao ◽  
Yinggao Yue ◽  
Yong Cai ◽  
Bo Hang
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Coverage Optimization ◽  
Grey Wolf Algorithm

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

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).

Proficient Clustering algorithm for Wireless Sensor Networks

2017 ◽  
pp. 345-357
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).

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