scholarly journals An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Teijiro Isokawa ◽  
Shun Motomura ◽  
Junya Ohtsuka ◽  
Hironobu Kawa ◽  
Satoshi Nakashima ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Quality Indicator ◽  
Measurement Errors ◽  
Location Estimation ◽  
Link Quality ◽  
Sensor Nodes ◽  
Weighted Averaging ◽  
Hop Count ◽  
Localization Scheme ◽  
Link Quality Indicator

A localization scheme for wireless sensor networks is proposed and its performances are investigated in this paper. The proposed scheme is an anchor-free scheme, in which no geometrical information of sensor nodes is required for their localization. Thus, only local interaction among sensor nodes is used to estimate their locations. This scheme employs the link quality indicator and hop count between sensor nodes for location estimation. A weighted averaging and Kalman filtering schemes are incorporated in order to reduce the effects of the measurement errors in the obtained values of the link quality indicator. We performed experiments by implementing the proposed scheme on ZigBee sensor modules. The results of our experiments indicate that estimation could be successfully performed for networks comprising four sensor nodes.

scholarly journals A Two-Stage Localization Scheme with Partition Handling for Data Tagging in Underwater Acoustic Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2135 ◽  
Author(s):  
Tariq Islam ◽  
Yong Kyu Lee
Keyword(s):  
Sensor Networks ◽  
Location Estimation ◽  
Sensor Nodes ◽  
Communication Overhead ◽  
Underwater Sensor Networks ◽  
Network Partitioning ◽  
Network Location ◽  
Reference Node ◽  
Relative Localization ◽  
Localization Scheme

Knowledge about the geographic coordinates of underwater sensor nodes is of primary importance for many applications and protocols of under water sensor networks (UWSNs) thus making localization of sensor nodes a crucial part of underwater network design. In case of mobile underwater sensor network, location estimation becomes challenging not only due to the need for periodic tracking of nodes, but also due to network partitioning caused by the pseudo-random mobility of nodes. Our proposed technique accomplishes the task of localization in two stages: (1) relative localization of sensor nodes with respect to a reference node at regular intervals during sensing operation. (2) Offline absolute localization of sensor nodes using absolute coordinates of the reference node and relative locations estimated during stage 1. As our protocol deals with mobile underwater sensor networks that may introduce network partitioning, we also propose a partition handling routine to deal with network partitions to achieve high localization coverage. The major design goal of our work is to maximize localization coverage while keeping communication overhead minimum, thus achieving better energy efficiency. Major contributions of this paper are: (1) Two energy efficient relative localization techniques, and (2) A partition handling strategy that ensures localization of partitioned nodes.

scholarly journals Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering

2019 ◽  
Vol 8 (2) ◽  
pp. 24 ◽  
Author(s):  
Tanveer Ahmad ◽  
Xue Jun Li ◽  
Boon-Chong Seet
Keyword(s):  
Kalman Filtering ◽  
Ieee 802.15.4 ◽  
Outer Region ◽  
Measurement Noise ◽  
Sensor Nodes ◽  
Node Degree ◽  
Personal Area Networks ◽  
Extended Kalman Filtering ◽  
Localization Accuracy ◽  
Localization Scheme

Thanks to IEEE 802.15.4 defining the operation of low-rate wireless personal area networks (LR-WPANs), the door is open for localizing sensor nodes using tiny, low power digital radios such as Zigbee. In this paper, we propose a three-dimensional (3D) localization scheme based on well-known loop invariant for division algorithm. Parametric points are proposed by using the reference anchor points bounded in an outer region named as Parametric Loop Division (PLD) algorithm. Similar to other range-based localization methods, PLD is often influenced by measurement noise which greatly degrades the performance of PLD algorithm. We propose to adopt extended Kalman filtering (EKF) to refine node coordinates to mitigate the measurement noise. We provide an analytical framework for the proposed scheme and find the lower bound for its localization accuracy. Simulation results show that compared with the existing PLD algorithm, our technique always achieves better positioning accuracy regardless of network topology, communication radius, noise statistics, and the node degree of the network. The proposed scheme PLD-EKF provides an average localization accuracy of 0.42 m with a standard deviation of 0.26 m.

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