Distance-based Indoor Localization using Empirical Path Loss Model and RSSI in Wireless Sensor Networks

Suroso, Dwi Joko; Arifin, Muhammad; Cherntanomwong, Panarat

doi:10.18196/jrc.1638

Cited by 21 publications

(20 citation statements)

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“…LoRa propagation loss is measured by the RSSI (received signal strength indicator) parameter. This parameter has been used in many works as a transmission quality indicator [ 36 , 37 , 38 ]. The measurement of received signal strength (RSS) is followed by various metrics ( Table 2 ).…”

Section: Methodsmentioning

confidence: 99%

A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment

Anzum

Habaebi

Islam

et al. 2022

Sensors

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Palm oil is the main cash crop of tropical Asia, and the implementation of LPWAN (low-power wide-area network) technologies for smart agriculture applications in palm oil plantations will benefit the palm oil industry in terms of making more revenue. This research attempts to characterize the LoRa 433 MHz frequency channels for the available spreading factors (SF7-SF12) and bandwidths (125 kHz, 250 kHz, and 500 kHz) for wireless sensor networks. The LoRa channel modeling in terms of path-loss calculation uses empirical measurements of RSS (received signal strength) in a palm oil plantation located in Selangor, Malaysia. In this research, about 1500 LoS (line-of-sight) and 300 NLoS (non-line-of-sight) propagation measurement data are collected for path-loss prediction modeling. Using the empirical data, a prediction model is constructed. The path-loss exponent for LoS propagation of the proposed prediction model is found to be 2.34 and 2.9 for 125–250 kHz bandwidth and 500 kHz bandwidth, respectively. Again, for the NLoS propagation links, the attenuation per trunk is found to be 7.58 dB, 7.04 dB, 5.35 dB, 5.02 dB, 5.01 dB, and 5 dB for SF7-SF12, and the attenuation per canopy is found to be 9.32 dB, 7.96 dB, 6.2 dB, 5.89 dB, 5.79 dB, and 5.45 dB for SF7-SF12. Moreover, the prediction model is found to be the better choice (mean RMSE 2.74 dB) in comparison to the empirical foliage loss models (Weissberger’s and ITU-R) to predict the path loss in palm oil plantations.

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Section: Methodsmentioning

confidence: 99%

A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment

Anzum

Habaebi

Islam

et al. 2022

Sensors

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“…In addition to topological arrangement, other elements that affect WSN node transmission include distance between communicating nodes and their alignment, channel characteristics and signal attenuation and fading, and presence of noise and other interfering signals, such elements are approximated using path loss and shadowing models. WSN topologies are linked to path loss parameters, and better designs can be achieved when values resulting from node communication are obtained using an appropriate path loss model, which also includes routing protocols and WSN nodes life time [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Error Probability for Grid and Random Uniform Topologies in a Variable Noise Environment

Iskandarani

2022

Journal of Computer Networks and Communications

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Investigation of the effect of topology on the communication efficiency of WSN nodes is carried out through simulation of 9 nodes in a 12 by 12 meters area. The obtained data and plots indicate that the grid topology is a more efficient and stable topology to use in comparison to the random uniform topology. The deduction is supported by probability of error as a function of error distribution values. Five different noise levels are used in the simulation (0dBm, −20 dBm, −40 dBm, −60 dBm, −80 dBm, and −100 dBm) with an output power of −10 dBm. The work shows that at −60 dBm redistribution of probability of error as a function of error values started to occur with higher level error values associated with the random uniform topology compared with the grid topology occurring at −60 dBm noise. The work also shows the relationship between received signal strength indicator (RSSI) and probability of error which decreases as RSSI increase in a similar manner as signal to noise ratio (SNR). Both RSSI and SNR are related through the mathematical model presented in the paper which is based on the path loss model. Common features between the error probability model and Gaussian interpolation function are also presented. A simplified 1-D design model is also presented to enable initial topology considerations. Criteria are also established to enable relating SNR, RSSI, topology, and WSN incremental position.

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“…The weather station device designed in this article is used to measure weather conditions and air quality in an open area. With an IoT-based topology, this device can be used telemetry and also for remote observation [14], [15], and [16]. With this IoT technology, devices in the field can connect with other electronic devices wherever they are [17]- [22].…”

Section: Introductionmentioning

confidence: 99%

IoT-based weather station with air quality measurement using ESP32 for environmental aerial condition study

et al. 2021

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This article discusses the design of a weather station device that also functions to measure the concentration of gases in the air. This real-time telemetry device based on the internet of things (IoT) uses the ESP32 board to process measurement data. Some of the weather parameters measured are wind speed, wind direction, humidity, ambient air temperature, air pressure, rainfall, and ultraviolet (UV) index. Meanwhile, the gas concentration parameters in the air are ozone, hydrogen, methane, ammonia, carbon monoxide, and carbon dioxide. The readings from all sensors are processed by the ESP32 board and uploaded to the server. Then a client device will receive the data set and then processed, displayed on the monitor, and stored in the form of a text file. Furthermore, the monitor and the data are used for the analysis of the surrounding air quality and weather conditions.

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Distance-based Indoor Localization using Empirical Path Loss Model and RSSI in Wireless Sensor Networks

Cited by 21 publications

References 0 publications

A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment

A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment

Investigation of Error Probability for Grid and Random Uniform Topologies in a Variable Noise Environment

IoT-based weather station with air quality measurement using ESP32 for environmental aerial condition study

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