scholarly journals Positioning of Wireless Sensor Network under Emergency Communication Environment

2020 ◽  
Vol 19 (4) ◽  
pp. 273-279
Author(s):  
Ruilin Yuan
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Embedded System ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Initial Value ◽  
Emergency Communication ◽  
Positioning Accuracy ◽  
Communication Environment ◽  
Value Range

With the development of microelectromechanical system (MEMS), embedded system, and wireless communication, it is now feasible to implement and deploy wireless sensor network (WSN) in emergency communication environment. However, the positioning accuracy of WSN nodes needs to be further improved. To solve the problem, this paper improves the initial value calculation method of multi-hop positioning algorithms, which are suitable for emergency communication environment, and puts forward a WSN node positioning algorithm that narrows the initial values of Kalman filter. By narrowing the initial value range of Kalman filter, the specially deployed sensors could accurately derive its position from the known positions of anchor nodes. To prevent error accumulation in the network, distributed computing was performed to solve the global nonlinear optimization problem, and calculate the position of the nodes. Simulation results show that the proposed algorithm can improve the WSN positioning accuracy under emergency communication environment, while greatly saving computing and communication costs. The research further improves the practicability and efficiency of multi-hop positioning algorithms in emergency communication environment.

scholarly journals A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6634
Author(s):  
Long Cheng ◽  
Sihang Huang ◽  
Mingkun Xue ◽  
Yangyang Bi
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Extended Kalman Filter ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Robust Localization

With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.

scholarly journals Enhance the Performance of Wireless Sensor Network based on Software Define Network and Kalman Filter

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1436-1448
Author(s):  
Jumana Suhail ◽  
Dr. Khalida Sh. Rijab
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Average Density ◽  
Wireless Sensor ◽  
Buffer Memory ◽  
Leach Protocol ◽  
Subsequent Step ◽  
Random Node ◽  
Software Define Network

The paper proposes a methodology for estimating packet flowing at the sensor level in SDN-WSN based on the partial congestion controller with Kalman filter. Furthermore, the actual purpose of proposing such methodology for predicting in advance the subsequent step of packet flow, and that will consequently contribute in reducing the congestion that might happen. The model proposed (SDN with Kalman filter) is optimized using congestion controller, the methodology of proposed work, the first step random distributed of random node, the apply the Kmean cluster of select the head cluster node in, the connected the network based on LEACH protocol. in this work proposed SDN with Kalman filter for control on network and reduce error of data, where achieve by add buffer memory for each nodes and head cluster to store the data, and SDN control on transmit ion data and receiver data, before transmit apply the Kalman filter on data to reduce error data. The proposed technique, according to simulation findings, extends the network's lifetime by over 30% more than typical WSNs, the reduce the average density of memory to 20% than traditional WSN, and the increase the average capacity of memory to 20% than traditional WSN.

scholarly journals EEWES: an energy-efficient wireless sensor network embed-ded system to be applied on industrial environments

2015 ◽  
Vol 35 (2) ◽  
pp. 67-73 ◽  
Author(s):  
Felipe Denis Mendonça de Oliveira ◽  
Rodrigo Soares Semente ◽  
Jefferson Doolan Fernandes ◽  
Tálison Augusto Correia de Melo ◽  
Serafim Do Nascimento Júnior ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Embedded System ◽  
Sensor Network ◽  
Energy Efficient ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Maintenance Cost ◽  
Industrial Environments

<p class="Abstractandkeywordscontent"><span lang="EN-US">Nowadays, the vast majority of information monitoring in industrial plants is still carried out by wired technologies, in which the installation and maintenance cost is high. However, in outdoor applications, such as those used in the oil and gas industry, the use of Wireless Sensor Networks (WSN) is increasing due to mobility, reliability, and low cost of the sensor nodes that make up the network. Moreover, this solution reduces the risks of workers in classified areas (regions with high probability of accidents occurrence) to the extent that the equipment maintenance is optimized.  This paper proposes the development of the EEWES, an energy efficient wireless sensor network embedded system, which can be applied on industrial environments. This development approach significantly reduces the energy consumption of the sensor nodes by using a method that alternates sleep periods of the transceiver/sensor set with data transmission/reception periods, which reduces the duty cycle while keeping the desirable parameters of the service quality (QoS). The results presented in this paper will be confirmed by field tests.</span></p>

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.

Person tracking using Kalman Filter in Wireless Sensor Network

ICoAC 2010 ◽  
2010 ◽  
Author(s):  
V Vaidehi ◽  
S. Vasuhi ◽  
K. Sri Ganesh ◽  
C. Theanammai ◽  
N T Naresh Babu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Person Tracking

Induction Motor Conditional Monitoring Using Wireless Sensor Network

2014 ◽  
Vol 626 ◽  
pp. 95-100 ◽  
Author(s):  
P. Shoba ◽  
B. Arivuselvam
Keyword(s):  
Wireless Sensor Network ◽  
Embedded System ◽  
Sensor Network ◽  
Output Voltage ◽  
Wireless Sensor ◽  
Maintenance Cost ◽  
Output Current ◽  
Non Invasive ◽  
Torque Estimation ◽  
The Embedded System

This system is used for monitoring the speed, torque, efficiency, voltage & current by employing ZigBee based wireless sensor network. Embedded system is used for acquiring electrical signals from the motors in a non-invasive way. The speed and torque estimation is done locally. An embedded system is used to control the speed of the motor the values calculated by the embedded system are transmitted to the monitoring unit through ZigBee based wireless sensor network and it can be monitored locally in PC. The main advantages of using ZigBee are low maintenance cost, security, reliability and through output. Using simulation, the characteristic graph for speed, torque, output voltage and output current can be obtained.

scholarly journals A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2348 ◽  
Author(s):  
Yan Wang ◽  
Jinquan Hang ◽  
Long Cheng ◽  
Chen Li ◽  
Xin Song
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Rapid Development ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Localization Method

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

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