Realizing mobile node tracking in wireless sensor network based on Kalman filter

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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A Mobile Node Filter Method in Wireless Sensor Network Based on Improved MH Particle

2010 First International Conference on Pervasive Computing, Signal Processing and Applications ◽

10.1109/pcspa.2010.47 ◽

2010 ◽

Author(s):

Sun Liying ◽

Ding Mingli ◽

Liu Zhen ◽

Zhang Zhongda

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Mobile Node ◽

Wireless Sensor ◽

Filter Method

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

Webology ◽

10.14704/web/v18si04/web18208 ◽

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.

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

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v16i7.6454 ◽

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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A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network

Sensor Technology ◽

10.4018/978-1-7998-2454-1.ch071 ◽

2020 ◽

pp. 1522-1537 ◽

Cited By ~ 1

Author(s):

Essa Qasem Shahra ◽

Tarek Rahil Sheltami ◽

Elhadi M. Shakshuki

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Average Distance ◽

Mobile Node ◽

Wireless Sensor ◽

Military Operations ◽

Localization Algorithms ◽

Anchor Nodes ◽

Routing Tables ◽

Range Free

Wireless Sensor Network is deployed in many fields including military operations, mechanical applications, human services, smart homes, etc. However, deploying WSN encounters many challenges. One of the challenges is localizing the node position, especially mobile targets in critical situations. In this paper, the authors compare two types from range-free localization algorithms and one type from range-based algorithms, namely: Received Signal Strength (RSS), Centroid, and Distance Vector Hop (DV-Hops) protocols, using Cooja simulator. RSS localization algorithms require determining values of the RSS from the anchor nodes around the mobile node, to calculate the distance between the unknown mobile and the first three anchor nodes in the mobile range. The centroid localization requires only three anchors to compute the location of the mobile sensor without the need for distance measuring. Lastly, the DV-Hop algorithm uses routing tables of each anchor in the network topology to compute the Average Distance of Hops. The results show that rang-based algorithms are more accurate than range-free.

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

Journal of Sensors ◽

10.1155/2020/6787252 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 1

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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Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments

Sensors ◽

10.3390/s19010215 ◽

2019 ◽

Vol 19 (1) ◽

pp. 215

Author(s):

Carlos Alberto Socarrás Bertiz ◽

Juan Jesús Fernández Lozano ◽

Jose Antonio Gomez-Ruiz ◽

Alfonso García-Cerezo

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Sensory Information ◽

Mobile Node ◽

Urban Environments ◽

Intelligent Vehicles ◽

Wireless Sensor ◽

Physical Systems ◽

Additional Information ◽

Machine Interface

Robots, or in general, intelligent vehicles, require large amounts of data to adapt their behavior to the environment and achieve their goals. When their missions take place in large areas, using additional information to that gathered by the onboard sensors frequently offers a more efficient solution of the problem. The emergence of Cyber-Physical Systems and Cloud computing allows this approach, but integration of sensory information, and its effective availability for the robots or vehicles is challenging. This paper addresses the development and implementation of a modular mobile node of a Wireless Sensor Network (WSN), designed to be mounted onboard vehicles, and capable of using different sensors according to mission needs. The mobile node is integrated with an existing static network, transforming it into a Hybrid Wireless Sensor Network (H-WSN), and adding flexibility and range to it. The integration is achieved without the need for multi-hop routing. A database holds the data acquired by both mobile and static nodes, allowing access in real-time to the gathered information. A Human–Machine Interface (HMI) presents this information to users. Finally, the system is tested in real urban scenarios in a use-case of measurement of gas levels.

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