Performance of distance based and path loss based weighted centroid localization algorithms for video capsule endoscope

We propose scattered path loss based weighted centroid localization (WCL) algorithm for wireless video capsule endoscope (VCE). The main challenge in this approach is the random deviation in the measured received signal strength indicator (RSSI) caused by multipath propagation and shadowing effects of human body channel which in turn increases the localization error. To address this issue, we propose local parameter dependent path loss representation in the training phase and apply adaptive least square error (LSE) method to extract the parameters. Then, in the test phase, we estimate distance using the extracted parameters and the randomly scattered path loss. The position of capsule is estimated using non-degree based WCL followed by a calibration process. We propose suboptimal method of estimating the calibration coefficient and also compute the optimal value of coefficient analytically to set the benchmark. We develop a simulation platform using MATLAB to present the results and to verify the performance. We gradually increase the number of sensors and place them in different topologies using different dimensions. The obtained accuracy by our proposed suboptimal method of WCL is very close to the optimal benchmark for all cases. Our proposed approach also outperforms existing works in terms of localization accuracy.

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Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Sensors ◽

10.3390/s20226582 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6582

Author(s):

SeYoung Kang ◽

TaeHyun Kim ◽

WonZoo Chung

Keyword(s):

Kalman Filter ◽

Path Loss ◽

Target Position ◽

Signal Strength ◽

Received Signal Strength ◽

Target Localization ◽

Wireless Sensor ◽

Transmit Power ◽

Path Loss Exponent ◽

Localization Algorithms

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

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Factorial Design Analysis for Localization Algorithms

Applied Sciences ◽

10.3390/app8122654 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2654

Author(s):

Joaquin Mass-Sanchez ◽

Erica Ruiz-Ibarra ◽

Ana Gonzalez-Sanchez ◽

Adolfo Espinoza-Ruiz ◽

Joaquin Cortez-Gonzalez

Keyword(s):

Statistical Analysis ◽

Noise Level ◽

Performance Metrics ◽

Extreme Values ◽

Path Loss ◽

Propagation Model ◽

Path Loss Exponent ◽

Localization Algorithms ◽

Accuracy And Precision ◽

The Impact

Localization is a fundamental problem in Wireless Sensor Networks, as it provides useful information regarding the detection of an event. There are different localization algorithms applied in single-hop or multi-hop networks; in both cases their performance depends on several factors involved in the evaluation scenario such as node density, the number of reference nodes and the log-normal shadowing propagation model, determined by the path-loss exponent (η) and the noise level (σdB) which impact on the accuracy and precision performance metrics of localization techniques. In this paper, we present a statistical analysis based on the 2k factorial methodology to determine the key factors affecting the performance metrics of localization techniques in a single-hop network to concentrate on such parameters, thus reducing the amount of simulation time required. For this proposal, MATLAB simulations are carried out in different scenarios, i.e., extreme values are used for each of the factors of interest and the impact of the interaction among them in the performance metrics is observed. The simulation results show that the path-loss exponent (η) and noise level (σdB) factors have the greatest impact on the accuracy and precision metrics evaluated in this study. Based on this statistical analysis, we recommend estimating the propagation model as close to reality as possible to consider it in the design of new localization techniques and thus improve their accuracy and precision metrics.

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A Localization Algorithm for Wireless Sensor Network Based on the Weighted Correction in Geometric Measurement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.740.823 ◽

2015 ◽

Vol 740 ◽

pp. 823-829

Author(s):

Meng Long Cao ◽

Chong Xin Yang

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Signal Strength ◽

Received Signal Strength ◽

Wireless Sensor ◽

Localization Algorithm ◽

Received Signal Strength Indicator ◽

Localization Algorithms ◽

Weighted Centroid Localization ◽

Geometric Measurement

Firstly, the characteristics of regular Zigbee localization algorithms-the received signal strength indicator algorithm (RSSI) and the weighted centroid localization algorithm are introduced. Then, the factors of the errors existing in the aforementioned algorithms are analyzed. Based on these above, the improved RSSI algorithm-correction geometric measurement based on weighted is proposed. Finally, utilizing this algorithm to design and implement the localization nodes, which have the CC2431 wireless microcontroller on them. The simulation and experimental results show that the accuracy of this localization algorithm improved about 2%, comparing with the regular algorithms.

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Research on Underground Personnel Positioning Method Based on PSO-GSA Optimization

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2016100102 ◽

2016 ◽

Vol 8 (4) ◽

pp. 21-74

Author(s):

Ye Liu ◽

Tianze Li ◽

Tao Gao ◽

Yuhan Wang ◽

JiaHui Chen

Keyword(s):

Search Algorithm ◽

Gravitational Search Algorithm ◽

Path Loss ◽

Localization Algorithm ◽

Positioning Accuracy ◽

Experiment Platform ◽

Positioning Method ◽

Unknown Node ◽

Weighted Centroid Localization ◽

On Chip

In the case of coal mine accidents, in order to ensure timely rescue of the suffering people in a complex environment of underground localization, focusing on Received Signal Strength Indicator (RSSI) in underground personnel positioning accuracy is low and the problem of dynamic tracing parameters changes. Therefore, using an improved gravitational search algorithm (GSA) for the weighted centroid localization that based on RSSI. Utilizing the log distance path loss model gets the distance between the beacon nodes and unknown nodes, and then through the weighted centroid localization algorithm perform the unknown node positioning. Finally, the improved GSA-PSO optimizes the preliminary location results and parameters. Proposed solutions to establish simulation model is verified in MATLAB, and use the on-chip system CC2430 chips experiment platform is established. Experimental results show the proposed method can improve both the positioning accuracy effectively and the adaptive ability of changeful environment.

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Calibration and Measurement of Signal Strength for Sensor Localization

Localization Algorithms and Strategies for Wireless Sensor Networks ◽

10.4018/978-1-60566-396-8.ch005 ◽

2009 ◽

pp. 122-145 ◽

Cited By ~ 6

Author(s):

Neal Patwari ◽

Piyush Agrawal

Keyword(s):

Parameter Estimation ◽

Path Loss ◽

Signal Strength ◽

Sensor Nodes ◽

Wireless Sensor ◽

Sensor Localization ◽

Wireless Sensor Nodes ◽

Present Measurement ◽

Localization Algorithms ◽

Negative Impacts

A number of practical issues are involved in the use of measured received signal strength (RSS) for purposes of localization. This chapter focuses on device effects and modeling problems which are not well covered in the literature, such as transceiver device manufacturing variations, battery effects on transmit power, nonlinearities in RSSI circuits, and path loss model parameter estimation. The authors discuss both the negative impacts of these effects and inaccuracies, and adaptations used by particular localization algorithms to be robust to them, without discussing any algorithm in detail. The authors present measurement methodologies to characterize these effects for wireless sensor nodes, and report the results from several calibration experiments to quantify each discussed effect and modeling issue.

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Characterization of the Log-Normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks

Journal of Sensor and Actuator Networks ◽

10.3390/jsan9010012 ◽

2020 ◽

Vol 9 (1) ◽

pp. 12 ◽

Cited By ~ 1

Author(s):

José Vallet García

Keyword(s):

Empirical Studies ◽

Path Loss ◽

Signal Strength ◽

Received Signal Strength ◽

Wireless Sensor ◽

Model Parameters ◽

Normal Model ◽

Path Loss Exponent ◽

Localization Algorithms ◽

Log Normal

Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model.

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