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 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.

scholarly journals Characterization and Modeling of Received Signal Strength and Charging Time for Wireless Energy Transfer

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Uthman Baroudi ◽  
Amin-ud-din Qureshi ◽  
Samir Mekid
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Transfer ◽  
Sensor Networks ◽  
Signal Strength ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Energy Transfer ◽  
Wireless Sensor Nodes ◽  
Charging Time ◽  
Indoor And Outdoor

Wireless sensor networks can provide effective means for monitoring and controlling a wide range of applications. Recently, tremendous effort was directed towards devising sensors powered from ambient sources such as heat, wind, and vibration. Wireless energy transfer is another source that has attractive features that make it a promising candidate for supplying power to wireless sensor nodes. This paper is concerned with characterizing and modeling the charging time and received signal strength indicator for wireless energy transfer system. These parameters play a vital role in deciding the geometry of sensor network and the routing protocols to be deployed. The development of communication protocols for wireless-powered wireless sensor networks is also improved with the knowledge of such models. These two quantities were computed from data acquired at various coordinates of the harvester relative to a fixed position of RF energy source. Data was acquired for indoor and outdoor scenarios using the commercially available PowerCast energy harvester and evaluation board. Mathematical models for both indoor and outdoor environments were developed and analyzed. A few guidelines on how to use these models were suggested. Finally, the possibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes was also investigated.

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.

Empirical path loss models at 433 MHz in Himalayan snow for health monitoring

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Garg ◽  
Surender Kumar Soni
Keyword(s):  
Experimental Data ◽  
Health Monitoring ◽  
Path Loss ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Third Order ◽  
Wireless Sensor Nodes ◽  
Loss Model ◽  
Content Type ◽  
Loss Models

Purpose The purpose of this paper is to investigate the effect of snow on the radio link performance of wireless sensor nodes in Indian Himalayan conditions and to propose empirical path loss models for radio wave propagation. Design/methodology/approach At the remote test site, one source and three listening wireless sensor nodes were deployed at frequency of 433 MHz. The path loss models are derived from experimental data collected during the period of snowfall and clear weather conditions. Linear, exponential, second and third-order polynomials path loss models have been investigated along with experimental data. Findings With the help of curve fitting and goodness-of-fit tests, it is found that path loss can be modelled through third-order polynomial equation during the snowfall period. However, if sensor is buried, the acceptable path loss model is exponential. Similarly, for unified modelling requirement, exponential path loss model over linear can be a preferred choice. Originality/value Results show that path loss can be estimated priori for deciding optimum transmission energy in wireless sensor network. Presented work is usable in extending the lifetime of health monitoring devices buried in snowy environment.

scholarly journals Analysis of Factors Influencing Communication and energy Consumption in Wireless Sensor Networks

TEM Journal ◽  
2020 ◽  
pp. 442-448
Author(s):  
Zlatan Ganev
Keyword(s):  
Wireless Sensor Networks ◽  
Standard Deviation ◽  
Path Loss ◽  
Signal Strength ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Open Area ◽  
Factors Influencing ◽  
Main Factors

The analysis and experiments presented in this paper are connected with propagation of signals between sensor nodes located in open area. The main purpose is to show some factors that affect the accuracy and reliability of the received signal strength (RSS) measurement as well as the determination of The Path Losses (PL), The Path Loss constant (n) and The Standard deviation (σ). The main factors considered here are: position and antenna orientation of sensor nodes and the distance between them.

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