scholarly journals An Online Charging Scheme for Wireless Rechargeable Sensor Networks Based on a Radical Basis Function

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 205
Author(s):  
Jia Yang ◽  
Jian-Shuang Bai ◽  
Qiang Xu
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Basis Function ◽  
Consumption Rate ◽  
Rbf Neural Network ◽  
Energy Hole ◽  
Wireless Rechargeable Sensor Networks ◽  
Energy Consumption Rate ◽  
Node Energy

The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes.

scholarly journals A Type of Annulus-Based Energy Balanced Data Collection Method in Wireless Rechargeable Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3150 ◽  
Author(s):  
Chao Sha ◽  
Qin Liu ◽  
Si-Yi Song ◽  
Ru-Chuan Wang
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Energy Hole ◽  
Node Scheduling ◽  
Wireless Rechargeable Sensor Networks ◽  
Energy Consumption Optimization ◽  
Balance Energy ◽  
Simulation Results

With the increasing number of ubiquitous terminals and the continuous expansion of network scale, the problem of unbalanced energy consumption in sensor networks has become increasingly prominent in recent years. However, a node scheduling strategy or an energy consumption optimization algorithm may be not enough to meet the requirements of large-scale application. To address this problem a type of Annulus-based Energy Balanced Data Collection (AEBDC) method is proposed in this paper. The circular network is divided into several annular sectors of different sizes. Nodes in the same annulus-sector form a cluster. Based on this model, a multi-hop data forwarding strategy with the help of the candidate cluster headers is proposed to balance energy consumption during transmission and to avoid buffer overflow. Meanwhile, in each annulus, there is a Wireless Charging Vehicle (WCV) that is responsible for periodically recharging the cluster headers as well as the candidate cluster headers. By minimizing the recharging cost, the energy efficiency is enhanced. Simulation results show that AEBDC can not only alleviate the “energy hole problem” in sensor networks, but also effectively prolong the network lifetime.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

scholarly journals Towards an Optimal Energy Consumption for Unattended Mobile Sensor Networks through Autonomous Sensor Redeployment

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jian Chen ◽  
Jie Jia ◽  
Yingyou Wen ◽  
Dazhe Zhao
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Mathematical Problem ◽  
Mobile Sensor Networks ◽  
Sensor Nodes ◽  
Target Area ◽  
Mobile Sensor ◽  
Energy Hole ◽  
Autonomous Sensor ◽  
Optimal Node

Energy hole is an inherent problem caused by heavier traffic loads of sensor nodes nearer the sink because of more frequent data transmission, which is strongly dependent on the topology induced by the sensor deployment. In this paper, we propose an autonomous sensor redeployment algorithm to balance energy consumption and mitigate energy hole for unattended mobile sensor networks. First, with the target area divided into several equal width coronas, we present a mathematical problem modeling sensor node layout as well as transmission pattern to maximize network coverage and reduce communication cost. And then, by calculating the optimal node density for each corona to avoid energy hole, a fully distributed movement algorithm is proposed, which can achieve an optimal distribution quickly only by pushing or pulling its one-hop neighbors. The simulation results demonstrate that our algorithm achieves a much smaller average moving distance and a much longer network lifetime than existing algorithms and can eliminate the energy hole problem effectively.

scholarly journals Maximization of Energy Efficiency in Wireless ad hoc and Sensor Networks With SERENA

2009 ◽  
Vol 5 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Saoucene Mahfoudh ◽  
Pascale Minet
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ad Hoc ◽  
Idle State ◽  
Time Simulation ◽  
Simulation Results ◽  
Remaining Time ◽  
Slot Reuse ◽  
Node Energy

In wireless ad hoc and sensor networks, an analysis of the node energy consumption distribution shows that the largest part is due to the time spent in the idle state. This result is at the origin of SERENA, an algorithm to SchEdule RoutEr Nodes Activity. SERENA allows router nodes to sleep, while ensuring end-to-end communication in the wireless network. It is a localized and decentralized algorithm assigning time slots to nodes. Any node stays awake only during its slot and the slots assigned to its neighbors, it sleeps the remaining time. Simulation results show that SERENA enables us to maximize network lifetime while increasing the number of user messages delivered. SERENA is based on a two-hop coloring algorithm, whose complexity in terms of colors and rounds is evaluated. We then quantify the slot reuse. Finally, we show how SERENA improves the node energy consumption distribution and maximizes the energy efficiency of wireless ad hoc and sensor networks. We compare SERENA with classical TDMA and optimized variants such as USAP in wireless ad hoc and sensor networks.

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

scholarly journals A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

2019 ◽  
Vol 11 (21) ◽  
pp. 6125
Author(s):  
Lianyan Li ◽  
Xiaobin Ren
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Smart Growth ◽  
Principal Component Regression ◽  
Principal Component ◽  
Self Organizing Map ◽  
Radial Basis

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

Sign in / Sign up

Export Citation Format

Share Document