Nodes Deployment Optimization Algorithm Based on Improved Evidence Theory

2018 ◽  
pp. 84-89 ◽  
Author(s):  
Xiaoli Song ◽  
Yunzhan Gong ◽  
Dahai Jin ◽  
Qiangyi Li ◽  
Hengchang Jing
Keyword(s):  
Optimization Algorithm ◽  
Evidence Theory ◽  
Deployment Optimization

scholarly journals An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1368 ◽  
Author(s):  
Luoheng Yan ◽  
Yuyao He ◽  
Zhongmin Huangfu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Balance ◽  
Optimization Algorithm ◽  
Growth Ring ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Connected Network ◽  
Deployment Optimization

The underwater wireless sensor networks (UWSNs) have been applied in lots of fields such as environment monitoring, military surveillance, data collection, etc. Deployment of sensor nodes in 3D UWSNs is a crucial issue, however, it is a challenging problem due to the complex underwater environment. This paper proposes a growth ring style uneven node depth-adjustment self-deployment optimization algorithm (GRSUNDSOA) to improve the coverage and reliability of UWSNs, meanwhile, and to solve the problem of energy holes. In detail, a growth ring style-based scheme is proposed for constructing the connective tree structure of sensor nodes and a global optimal depth-adjustment algorithm with the goal of comprehensive optimization of both maximizing coverage utilization and energy balance is proposed. Initially, the nodes are scattered to the water surface to form a connected network on this 2D plane. Then, starting from sink node, a growth ring style increment strategy is presented to organize the common nodes as tree structures and each root of subtree is determined. Meanwhile, with the goal of global maximizing coverage utilization and energy balance, all nodes depths are computed iteratively. Finally, all the nodes dive to the computed position once and a 3D underwater connected network with non-uniform distribution and balanced energy is constructed. A series of simulation experiments are performed. The simulation results show that the coverage and reliability of UWSN are improved greatly under the condition of full connectivity and energy balance, and the issue of energy hole can be avoided effectively. Therefore, GRSUNDSOA can prolong the lifetime of UWSN significantly.

scholarly journals Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 6 ◽  
Author(s):  
Min Huang ◽  
Zhen Liu
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Evidence Theory ◽  
Fault Prediction ◽  
Swarm Optimization ◽  
Sensing Data ◽  
Vibration Sensing

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model’s ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

An Evolutionary Algorithm Based Approach to Design Optimization Using Evidence Theory

2013 ◽  
Vol 135 (8) ◽  
Author(s):  
Rupesh Kumar Srivastava ◽  
Kalyanmoy Deb ◽  
Rupesh Tulshyan
Keyword(s):  
Evolutionary Algorithm ◽  
Design Optimization ◽  
Optimization Algorithm ◽  
Evidence Theory ◽  
Evidence Based ◽  
Processing Unit ◽  
Optimal Solutions ◽  
Objective Evidence ◽  
Conflicting Objectives ◽  
Classical Optimization

For problems involving uncertainties in design variables and parameters, a bi-objective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some interests in classical optimization literature, this is the first attempt to use evidence theory with an EA. Due to EA's flexibility in modifying its operators, nonrequirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidence-based design optimization using an EA is promising. Results on a test problem and two engineering design problems show that the modified evolutionary multi-objective optimization algorithm is capable of finding a widely distributed trade-off frontier showing different optimal solutions corresponding to different levels of plausibility failure limits. Furthermore, a single-objective evidence-based EA is found to produce better optimal solutions than a previously reported classical optimization algorithm. Furthermore, the use of a graphical processing unit (GPU) based parallel computing platform demonstrates EA's performance enhancement around 160–700 times in implementing plausibility computations. Handling uncertainties of different types are getting increasingly popular in applied optimization studies and this EA based study is promising to be applied in real-world design optimization problems.

scholarly journals Design and Application of Residential Area Noise Monitoring Based on Wireless Sensor

2017 ◽  
Vol 13 (07) ◽  
pp. 14
Author(s):  
Qiuling Zheng
Keyword(s):  
Optimization Algorithm ◽  
Residential Area ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Font Size ◽  
Deployment Optimization ◽  
The City ◽  
Design And Application

<p><span style="font-family: 宋体; font-size: medium;">A new node deployme</span><span style="font-size: medium;"><span style="font-family: 宋体;">nt optimization algorithm is proposed to solve deployment problems of the monitoring terminal. Based on wireless sensor, a kind of deployment optimization algorithm of sensor nodes location is put forward, and OMNET++ simulation is used for verification. The experimental results show that this deploy method can get good applications in different areas of the city and achieve higher deployment results. Therefore, it is concluded that the proposed algorithm is feasible and effective.</span></span></p>

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