Designing localization algorithms robust to signal strength attacks

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

Distributed Computing in Sensor Systems - Lecture Notes in Computer Science ◽

10.1007/11776178_33 ◽

2006 ◽

pp. 546-563 ◽

Cited By ~ 31

Author(s):

Yingying Chen ◽

Konstantinos Kleisouris ◽

Xiaoyan Li ◽

Wade Trappe ◽

Richard P. Martin

Keyword(s):

Comparative Study ◽

Signal Strength ◽

Localization Algorithms

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Weighted Linear Least Square Localization Algorithms for Received Signal Strength

Wireless Personal Communications ◽

10.1007/s11277-013-1040-0 ◽

2013 ◽

Vol 72 (1) ◽

pp. 747-757 ◽

Cited By ~ 9

Author(s):

Shixun Wu ◽

Dengyuan Xu ◽

Shouyin Liu

Keyword(s):

Signal Strength ◽

Received Signal Strength ◽

Least Square ◽

Localization Algorithms ◽

Linear Least Square

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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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An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

Mathematical Problems in Engineering ◽

10.1155/2021/4325708 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Po-Chih Chiu ◽

Kuo-Wei Su ◽

Tsung-Yin Ou ◽

Chih-Lung Yu ◽

Chen-Yang Cheng ◽

...

Keyword(s):

Fuzzy Inference ◽

Signal Transmission ◽

Signal Strength ◽

Localization Algorithm ◽

Inference System ◽

Accurate Localization ◽

Localization Algorithms ◽

New Type ◽

The Cost ◽

Smart Factories

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

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