Intelligent Analogy on Wireless Communication Link Performance of Industry Wireless Sensor Networks

2011 ◽  
Vol 317-319 ◽  
pp. 366-369
Author(s):  
Guang Zhu Chen ◽  
Cheng Ming Luo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fuzzy Neural Network ◽  
Environmental Parameters ◽  
Wireless Sensor ◽  
Neural Network Modeling ◽  
Communication Link ◽  
Key Factor ◽  
Fuzzy Neural

The received signal strength indication (RSSI) is the key factor in the communication link for industry wireless sensor networks, while it is very difficult to model the value of RSSI to the distance of two communication nodes. This paper presented a fuzzy neural network modeling method to solve the shortcoming of the theoretical modeling. After the value of RSSI and the distance value of two communication nodes are fuzzed by Gaussian membership function, a fuzzy controlling rule is also presented, and then the output value of fuzzy neural network, namely the error distance of two communication nodes can be attained. Finally, simulation results show that without correcting the environmental parameters, the estimated error value of the distance of two communication nodes through RSSI in fuzzy neural network model is less than in quadratic fit method. So, the method presented by this paper can provide precise data support for wireless sensor networks for industry environment.

scholarly journals Energy-Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rajesh Kumar Varun ◽  
Rakesh C. Gangwar ◽  
Omprakash Kaiwartya ◽  
Geetika Aggarwal
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fuzzy Neural Network ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Fuzzy Neural

In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy-efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next-hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state-of-the-art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.

scholarly journals A New Method for Reliability Evaluation of Wireless Sensor Networks Based on Fuzzy Neural Network

2018 ◽  
Vol 14 (10) ◽  
pp. 180
Author(s):  
Jianjun Xu
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Reliability Evaluation ◽  
Wireless Sensor ◽  
Performance Indexes ◽  
Fuzzy Neural

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">In this paper, a reliability evaluation model based on fuzzy neural network is proposed to evaluate the reliability of wireless sensor networks without a unified standard. Firstly, the reliability is analyzed from the point of view of topology structure, protocol stack structure and reliability mechanism of wireless sensor network, and the performance indexes that affect the reliability are extracted. Secondly, some performance indexes are screened out, and the standard value matrix of reliability evaluation for index fuzzy quantization is established. The sample data is generated by interpolation, and the reliability evaluation model based on fuzzy neural network is established. The neural network model takes the selected index values as input, and outputs are the reliability of the wireless sensor network. The simulation results show that the evaluation model is basically consistent with the actual situation, and it can evaluate the wireless sensor network from the system level.</span>

scholarly journals Evaluation and Analysis for Maximum Lifespan of Wireless Sensor Networks by Energy-Efficient Design

2021 ◽  
Vol 9 (4) ◽  
pp. 01-05
Author(s):  
Ms.Tejashri H. Mohite, Prof. Dr. Noorullah Shariff
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Relay Network ◽  
Wireless Sensor ◽  
Communication Link ◽  
Environment Monitoring ◽  
Amplify And Forward ◽  
Decode And Forward

Wireless Sensor Networks (WSNs) have used worldwide in the past few years and are now being used in health monitoring ,disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay.

Design of Environment Monitoring System Based on CAN Bus and Wireless Sensor Networks for Greenhouse

2014 ◽  
Vol 1049-1050 ◽  
pp. 1163-1166
Author(s):  
Bo Chang ◽  
Xin Rong Zhang ◽  
Li Hong Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Can Bus ◽  
Environmental Parameters ◽  
Wireless Sensor ◽  
Area Network ◽  
Environment Monitoring ◽  
Effective Solution ◽  
Greenhouse Crops ◽  
Backbone Network

In order to accurately collect the environmental parameters (such as temperature, humidity, illumination, etc.), which influence growth of greenhouse crops, the paper proposed a design for greenhouse environment monitoring based on CAN bus and wireless sensor networks (WSNs). The communication network of the system consists of two parts: the backbone network being constructed by CAN bus and area network being constructed by WSNs. At the same time, the designed of hardware and software about the system is illustrated in detail. System architecture indicates that the system is an effective solution for greenhouse environment monitoring.

Intelligent Prediction on the Quality of Cementing

2011 ◽  
Vol 84-85 ◽  
pp. 373-377
Author(s):  
Wei Zhang Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Influential Factor ◽  
Software Systems ◽  
Neural Network Modeling ◽  
Quality Prediction ◽  
Modeling Tools ◽  
Fuzzy Neural ◽  
Neural Network System

The present solutions of well cementing are mostly designed by designers’ experience and calculation which can not predict the engineering quality after application of the designs. Meanwhile some questions in the designs can not be solved before construction. On the basis of detailed evaluation of every influential factor according to construction and environmental conditions, this article provides cementing fuzzy neural network model by means of 2nsoftEditor neural network modeling tools, and the stable software systems with the combination of artificial neural network and fuzzy logic rules are expected to improve the credibility of cementing quality prediction. Construction practice shows that cementing quality prediction with application of fuzzy neural network system before cementing can greatly reduce the cementing costs and improve the cementing success ratio.

scholarly journals Wireless Sensor Networks for Indoor Environment Monitoring Using LabVIEW

2011 ◽  
pp. 87-90
Author(s):  
Mrutyunjay Rout ◽  
Dr. Harish Kumar Verma ◽  
Subhashree Das
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Indoor Environment ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Environment Monitoring ◽  
Wireless Sensor Nodes ◽  
Local Decision ◽  
Mems Technology

Wireless sensor networks (WSNs) have gained worldwide attention in recent years, particularly with the rapid progress in Micro-Electro-Mechanical Systems (MEMS) technology which has facilitated the development of smart sensors. These sensors are small, with limited processing and computing resources, and they are inexpensive compared to traditional sensors. These sensor nodes can sense, measure, and gather information from the environment and, based on some local decision process, they can transmit the sensed data to the user. WSNs are large networks made of a numerous number of sensor nodes with sensing, computation, and wireless communication capabilities. In present work we provide a brief summary of the state-ofthe- art in wireless sensor networks, investigate the feasibility of indoor environment monitoring using crossbow wireless sensor nodes. Here we used nesC programming language and TinyOS operating system for programming Crossbow sensor nodes and LabVIEW GUI is used for displaying different indoor environmental parameters such as temperature, humidity and light acquired from different Wireless sensor nodes. These sensor readings can help building administrators to monitor the physical conditions of the environment in a building for creating optimized energy usage.

Optimization of wireless sensor network deployment for electromagnetic situation monitoring

2018 ◽  
Vol 10 (7) ◽  
pp. 746-753 ◽  
Author(s):  
Krzysztof Malon ◽  
Paweł Skokowski ◽  
Jerzy Lopatka
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Environmental Parameters ◽  
Probability Of Detection ◽  
Wireless Sensor ◽  
Electromagnetic Spectrum ◽  
Radio Receivers ◽  
Effective Performance ◽  
Performance Area

Wireless sensor networks are an increasingly popular tool for monitoring various environmental parameters. They can also be used for monitoring the electromagnetic spectrum. Wireless sensors, due to their small size, typically have simplified radio receivers with reduced sensitivity and use small antennas. As a result, their effective performance area is similarly limited. This is especially important in urban areas where there are various kinds of adverse propagation phenomena related to area coverage. The aim of this paper is to present the phenomena in the wireless sensor networks and propose criteria and methods to optimize their deployment to ensure maximizing the probability of detection of emissions, minimization of unmonitored areas, and to provide the necessary hardware redundancy in the priority areas. Influence of detection parameters, number of sensors and range constraints between sensors on received outcomes are also presented.

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