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.

scholarly journals A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ou Yong Kang ◽  
Cheng Long
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Time Of Arrival ◽  
Multiple Model ◽  
Localization Algorithm ◽  
Extend Kalman Filter

Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

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 A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

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 A NOVEL APPROACH OF CLUSTERING FOR ENHANCED LIFETIME IN WIRELESS SENSOR NETWORK

2017 ◽  
Vol 16 (7) ◽  
pp. 7031-7039
Author(s):  
Chamanpreet Kaur ◽  
Vikramjit Singh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Level ◽  
Research Work ◽  
Cluster Head ◽  
Mobile Node ◽  
Separation Distance ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor

Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. Our research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. The cluster head election mechanism will include various parameters like maximum residual energy of a node, minimum separation distance and minimum distance to the mobile node. Each CH will create a TDMA schedule for the member nodes to transmit the data. Nodes will have various level of power for signal amplification. The three levels of power are used for amplifying the signal. As the member node will send only its own data to the cluster head, the power level of the member node is set to low. The cluster head will send the data of the whole cluster to the mobile node, therefore the power level of the cluster head is set to medium. High power level is used for mobile node which will send the data of the complete sector to the base station. Using low energy level for intra cluster transmissions (within the cluster) with respect to cluster head to mobile node transmission leads in saving much amount of energy. Moreover, multi-power levels also reduce the packet drop ratio, collisions and/ or interference for other signals. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, multiple experiments have been conducted using different values of initial energy.

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