The Robustness of Localization Algorithms to Signal Strength Attacks: A Comparative Study

2006 ◽  
pp. 546-563 ◽  
Author(s):  
Yingying Chen ◽  
Konstantinos Kleisouris ◽  
Xiaoyan Li ◽  
Wade Trappe ◽  
Richard P. Martin
Keyword(s):  
Comparative Study ◽  
Signal Strength ◽  
Localization Algorithms

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

Sensors ◽  
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.

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

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.

Calibration and Measurement of Signal Strength for Sensor Localization

2009 ◽  
pp. 122-145 ◽  
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.

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

2020 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
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.

Ranging Technology for Accurate Localization in Short-Range Wireless Networks – A Comparative Study

2011 ◽  
Vol 467-469 ◽  
pp. 713-717
Author(s):  
Yuan Zhang ◽  
Shu Tang Liu ◽  
Yue Liu
Keyword(s):  
Wireless Networks ◽  
Comparative Study ◽  
Radio Frequency ◽  
Short Range ◽  
Early Stage ◽  
Ultra Wideband ◽  
Localization System ◽  
Accurate Localization ◽  
Stage Of Development ◽  
Localization Algorithms

Localization capability is usually required and designed for wireless networks. Although many localization algorithms have been proposed, the refinement issue of guaranteeing the location accuracy is still in its early stage of development. This paper compares different ranging technologies for localization measurement. Specifically, we analyze infrared, ultra sonic, radio frequency and ultra wideband as different choices. After a comparative study the paper recommends ultra wideband as the best candidate for accurate range-based localization system in the short-range wireless network scenario.

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