scholarly journals Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 150527-150538 ◽  
Author(s):  
Zainab Munadhil ◽  
Sadik Kamel Gharghan ◽  
Ammar Hussein Mutlag ◽  
Ali Al-Naji ◽  
Javaan Chahl
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Wireless Sensor ◽  
Patient Localization

Design and Application of the Indoor Environment Monitoring & Control System at Tianjin First Central Hospital

2013 ◽  
Vol 380-384 ◽  
pp. 635-638
Author(s):  
Chen Chen
Keyword(s):  
Control System ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Communication Protocol ◽  
Automatic Monitoring ◽  
Wireless Sensor ◽  
Environment Monitoring ◽  
Human Beings ◽  
Central Hospital

With advance of our human beings science and technology and enhance of the living standards, more and more people have addressed higher requirements on the environmental conditions in a hospital, therefore, the traditional and no-intelligent monitoring devices are being replaced by the automated and networked monitoring systems gradually. In this case, application of the wireless sensor network just fits this need. This paper proposes the Tianjin First Central Hospital indoor environment monitoring & control system of distributed acquisition and execution, and centralized management by focusing on the needs for the technical indicators of the hospital indoor environment. During design of the system, an universal design concept was put forward, and also a non-standard communication protocol for the wireless sensor network designed independently in combination with the OSI open standard. In this paper, realization of the communication protocol among the nodes with embedded software and the operation mechanism of the modes themselves are discussed, also a console panel has been developed for the data center. Several software design algorithms are proposed with respect to the network layout. This paper also describes the test platform of the Tianjin First Central Hospital indoor environment monitoring & control system established with the network components designed, and provides the test and verification results, including the monitored data of the various gases, corresponding automatic control functions, and underlay BER analysis. The results show that this system can basically realize automatic monitoring on the Tianjin First Central Hospital indoor environment. At present, the sensitive gases include CO, CO2, O2, NH3 and formaldehyde, sensitive environments temperature, humidity and light intensity, and controlled targets ventilation and lighting. This paper offers an optional solution for environment monitoring and has certain theoretical value and engineering significance.

scholarly journals A Research Cluster based Scheme for Node Positioning in Indoor Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 79-83
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Cluster Head ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Wireless Sensor ◽  
Localization Error ◽  
Network Efficiency

Location estimation in Wireless Sensor Network (WSN) is mandatory to achieve high network efficiency. Identifying the positions of sensors is an uphill task as monitoring nodes are involved in estimation and localization. Clustered Positioning for Indoor Environment (CPIE) is proposed for estimating the position of the sensors using a Cluster Head (CH) based mechanism. The CH estimates the number of neighbor nodes in each floor of the indoor environment. It sends the requests to the cluster members and the positions are estimated based on the Received Signal Strength Indicators (RSSIs) from the members of the cluster. The performance of the proposed scheme is analyzed for both stable and mobile conditions by varying the number of floors. Experimental results show that the propounded scheme offers better network efficiency and reduces delay and localization error

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

A Data Fusion Scheme in Wireless Sensor Network Based on Optimizing Parameters of Neural Network

2021 ◽  
pp. 315-323
Author(s):  
Thi-Kien Dao ◽  
Trong-The Nguyen ◽  
Van-Dinh Vu ◽  
Truong-Giang Ngo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Data Fusion ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Fusion Scheme

scholarly journals A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yan Wang ◽  
Yang Yan ◽  
Zhengjian Li ◽  
Long Cheng
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Detection Threshold ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Polynomial Fitting

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

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