scholarly journals Neural network algorithm for clustering wireless sensor network nodes based on Kohonen self-organizing maps

2018 ◽  
Vol 21 (4) ◽  
pp. 87-91
Author(s):  
A. V. Parashchinec ◽  
◽  
A. E. Efremova ◽  
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Self Organizing Maps ◽  
Network Nodes ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Self Organizing

scholarly journals Approach to Anomaly Detection in Self-Organized Decentralized Wireless Sensor Network for Air Pollution Monitoring

2021 ◽  
Vol 346 ◽  
pp. 03002
Author(s):  
Alexey Meleshko ◽  
Vasily Desnitsky ◽  
Igor Kotenko
Keyword(s):  
Air Pollution ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Human Life ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
Network Nodes ◽  
Private Monitoring ◽  
Air Pollution Monitoring ◽  
Self Organizing

The paper reveals the essence and features of the proposed approach to detecting anomalies in a self-organizing decentralized wireless sensor network (WSN). As a basis for detecting anomalies, the used WSN is intended to monitor atmospheric air pollution near industrial facilities and human life objects. The distinctive features of such a network are the decentralized nature of its structure and services, the autonomy and mobility of the network nodes, as well as the possibility of non-deterministic physical movement of nodes in space. The spontaneous nature of the dynamic formation of the network topology as well as the assignment of roles and private monitoring functions between the available network nodes determines such networks are subject to attacks that exploit the properties of network decentralization and its self-organization. The proposed approach to detecting anomalies is based on the collection and analysis of data from sensors and is designed to increase the security of self-organizing decentralized WSN by identifying anomalies that are critical in the context of the monitoring purposes.

Design and Development of Detection Node in Wireless Sensor Network Based on Neural Network

2014 ◽  
Vol 1022 ◽  
pp. 292-295 ◽  
Author(s):  
Shu Min Duan
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Bp Neural Network ◽  
Wireless Sensor ◽  
Design And Development ◽  
Network Nodes ◽  
Feed Forward Network ◽  
Input Layer ◽  
Hidden Layer

Multilayer BP neural network is a one-way transmission of feed forward network and it has three or more than three layers of neural network, including input layer, hidden layer and output layer. Wireless sensor network is composed in certain region has a plurality of wireless communication, sensing, data processing capabilities of network nodes. The paper presents design and development of detection node in wireless sensor network based on neural network. The simulation results prove the validity of the adopted BP neural network to build wireless sensor network node.

scholarly journals Improving Wireless Sensor Network Lifetime Using Self-Organizing Protocol

2019 ◽  
pp. 330-338
Author(s):  
Charan Mangali
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Life Time ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Network Node ◽  
Network Nodes ◽  
Balancing Energy ◽  
Self Organizing

This paper proposes a E-cient Tree-based Self-organizing Protocol to improving wireless sensor network lifetime. all nodes are divided into two kinds: network nodes and non-network nodes Network nodes broadcast packets to select child nodes and non-network nodes collect packets to join in network. During the self-organization process we take hop, residual energy, number of child node and communication distance into account to calculate the weight of available sink nodes, and then select the node with max weight as sink node. After a non-network node joins the network successful it will work as a network node to searching child nodes. A tree network can be constructed one layer by one layer. For balancing energy consumption and prolonging network lifetime we adjust the topology dynamic. All experiments were done with NS2 Furthermore, the success rate of packet and Life time is much higher compared with LBT.

States analysis and Agent modeling for wireless sensor network nodes

2011 ◽  
Vol 30 (12) ◽  
pp. 3155-3157
Author(s):  
Zhi CHEN ◽  
Jie SHI ◽  
Ying KONG ◽  
Yun ZHANG
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Agent Modeling ◽  
Network Nodes

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

scholarly journals Transmission Protocol Simulation Framework For The Resource-Constrained Wireless Sensor Network

2015 ◽  
Vol 22 (2) ◽  
pp. 221-228
Author(s):  
Marek Wójcikowski
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Transmission ◽  
Wireless Sensor ◽  
Transmission Protocol ◽  
Simulation Framework ◽  
Wireless Data ◽  
Network Nodes ◽  
Wireless Data Transmission ◽  
Network Software

Abstract In this paper a prototype framework for simulation of wireless sensor network and its protocols are presented. The framework simulates operation of a sensor network with data transmission, which enables simultaneous development of the sensor network software, its hardware and the protocols for wireless data transmission. An advantage of using the framework is converging simulation with the real software. Instead of creating a model of the sensor network node, the same software is used in real sensor network nodes and in the simulation framework. Operation of the framework is illustrated with examples of simulations of selected transactions in the sensor network.

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