Received Signal Strength-Based Robust Cooperative Localization With Dynamic Path Loss Model

2016 ◽  
Vol 16 (5) ◽  
pp. 1265-1270 ◽  
Author(s):  
Chen Liang ◽  
Fuxi Wen
Keyword(s):  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Cooperative Localization ◽  
Loss Model ◽  
Path Loss Model ◽  
Strength Based ◽  
Dynamic Path

Genetic Algorithm for Path Loss Model Selection in Signal Strength Based Indoor Localization

2021 ◽  
pp. 1-1
Author(s):  
Byeong-ho Lee ◽  
Doyoung Ham ◽  
Jeongsik Choi ◽  
Seong-Cheol Kim ◽  
Yong-Hwa Kim
Keyword(s):  
Genetic Algorithm ◽  
Model Selection ◽  
Indoor Localization ◽  
Path Loss ◽  
Signal Strength ◽  
Loss Model ◽  
Path Loss Model ◽  
Strength Based

scholarly journals An indoor Wi-Fi access points localization algorithm based on improved path loss model parameter calculation method and recursive partition

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771985203
Author(s):  
Wenyan Liu ◽  
Xiangyang Luo ◽  
Qing Mu ◽  
Yimin Liu ◽  
Fenlin Liu
Keyword(s):  
Path Loss ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Localization Algorithm ◽  
Loss Model ◽  
Localization Accuracy ◽  
Access Points ◽  
Path Loss Model ◽  
Recursive Partition

The localization accuracy of the existing methods for indoor Wi-Fi access points ranging-based localization depends on the accuracy of the received signal strength measured. Because the existing ranging-based methods are interfered by various indoor environmental factors, it is difficult to accurately measure the received signal strength, which leads to the problem of low localization accuracy of the indoor Wi-Fi access points. An indoor Wi-Fi access points localization algorithm based on improved path loss model parameter calculation method and recursive partition is proposed in this article. The algorithm recursively partitions the region where the target Wi-Fi access points are located according to the idea of quadtree partition, and partitions it into same sub-grids, which is sequentially performed until the sub-grids are smaller than the set threshold. The detection device is used at the detection location of the grids to measure the received signal strength, which is from the detection points to the target access point, the grid center point is used as the location of the candidate target access point, the parameters in the path loss model are calculated by using the signal strength differences between the detection points, and then the distances between the detection points and the target access point are calculated by using the signal strength values from the detection points to the target access point. Finally, the location of the target access point is estimated by executing a localization algorithm, and the location of the grid center point closest to the target access point is taken as the location of the target access point. The experimental results show that under the premise that the target access point can be found, the proposed algorithm reduces the use of the device and improves the localization accuracy compared with the typical localization method.

scholarly journals Step Length Measurements Using the Received Signal Strength Indicator

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 382
Author(s):  
Zanru Yang ◽  
Le Chung Tran ◽  
Farzad Safaei
Keyword(s):  
Path Loss ◽  
Signal Strength ◽  
Step Length ◽  
Received Signal Strength ◽  
Body Parts ◽  
Received Signal Strength Indicator ◽  
Loss Model ◽  
Path Loss Model ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments

In this paper, portable transceivers with micro-controllers and radio frequency modules are developed to measure the received signal strength, path loss, and thus the distance between the human ankles for both indoor and outdoor environments. By comparing the experimental results and the theoretical model, a path loss model between transceivers attached to the subject’s ankles is derived. With the developed experimental path loss model, the step length can be measured relatively accurately, despite the imperfections of hardware devices, with the distance errors of a centimeter level. This paper, therefore, helps address the need for a distance measurement method that has fewer health concerns, is accurate, and is less affected by occlusions and confined spaces. Our findings possibly lay a foundation for some important applications, such as the measurement of gait speed and localization of the human body parts, in wireless body area networks.

scholarly journals Determination of an Outdoor Path Loss Model and Signal Penetration Level in Some Selected Modern Residential and Office Apartments in Ogbomosho, Oyo State, Nigeria

2018 ◽  
pp. 1-25
Author(s):  
V. O. A. Akpaida ◽  
F. I. Anyasi ◽  
S. I. Uzairue ◽  
A. I. Idim
Keyword(s):  
Building Materials ◽  
Path Loss ◽  
Signal Strength ◽  
Base Stations ◽  
Propagation Path ◽  
Loss Model ◽  
Path Loss Model ◽  
Path Loss Exponent ◽  
Received Power

This article involves the site specific determination of an outdoor path loss model and Signal penetration level in some selected modern residential and office apartments in Ogbomosho, Oyo State. Measurements of signal strength and its associated location parameters referenced globally were carried out. Propagation path loss characteristics of Ogbomosho were investigated using three different locations with distinctively different yet modern building materials. Consequently, received signal strength (RSS) was measured at a distance d in meters, from appropriate base stations for various environments investigated. The data were analyzed to determine the propagation path loss exponent, signal penetration level and path loss characteristics. From calculations, the average building penetration losses were, 5.93dBm, 6.40dBm and 6.1dBm outside the hollow blocks B1, solid blocks B2 and hollow blocks mixed with pre cast asbestos B3, buildings respectively with a corresponding path loss exponent values of, 3.77, 3.80 and 3.63. Models were developed and validated, and used to predict the received power inside specific buildings. Moreover, the propagation models developed for the different building types can be used to predict the respective signal level within the building types, once the transmitter – receiver distance is known. The readings obtained from the developed models were compared with both the measured values and values computed using some existing models with satisfactory results obtained.

Survey of Radio Propagation Models for Acoustic Applications in Underwater Wireless Sensor Network

2019 ◽  
Vol 09 ◽  
Author(s):  
Preeti Saini ◽  
Rishi Pal Singh ◽  
Adwitiya Sinha
Keyword(s):  
Acoustic Waves ◽  
Path Loss ◽  
Signal Strength ◽  
Propagation Model ◽  
Propagation Loss ◽  
Radio Propagation ◽  
Propagation Models ◽  
Loss Model ◽  
Path Loss Model ◽  
Radio Propagation Model

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.

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