RSSI

Wi-Fi FTM RSSI Localization dataset

Wi-Fi Fine Time Measurement for positioning / Indoor Localization in 3 different locations and using 8 different APs
 
Custom APs using ESP32C3 and Raw FTM is measured in nanoseconds
 
Data is only measured at the Router Side
 
Data is not measured at client side
 
Has 4 datasets inside the zip folder with over 100,000 data points
 
Contains processed Wi-Fi FTM packets from various routers in:   

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Wi-Fi BLE RSSI SQI Localization dataset

 
Wi-Fi BLE RSSI for positioning / Indoor Localization in 4 different locations and using 18 different APs

Data is only measured at the Router Side

Data is not measured at client side

Has 12 datasets inside the zip folder with over 1,000,000 data points

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457 Views

To illustrate the impact of the obstacles, we consider indoor and outdoor scenarios. We consider the Department of Computer Science and Engineering, IIT(BHU) buildings as indoor buildings and the railway platform as an outdoor scenario. Here, we use single-channel LG in our experiment. The distance between LNs and LG varies from 5 to 50 meters. The floor map illustrates the walls, doors, and windows between LNs and LG. We consider railway stations for the outdoor environment. The outdoor environment did not consist of obstacles between LNs and LG.

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To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area.

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382 Views

We collected data to train the ML module to determine the user’s device's location based on beacon frame characteristics and RSSI values from Wi-Fi APs. To collect the data, we defined a threshold distance of 7 feet as the maximum allowable distance between the user’s devices. We then collected two datasets: one with data collected while the two Raspberry Pis were within 7 feet or less of each other named ”authentic”, and another with data collected while the distance between the two Raspberry Pis was over 7 feet named ”unauthorized”.

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534 Views

⭐ When using this resource, please cite the original publication:

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61221 Views

Nowadays, the advent of science and technology has brought many benefits to people. Positioning technology has also contributed to making lives much more modern and convenient. In recent years, location technology is not only used for major purposes such as military, commerce, transportation, national security, but also to serve normal daily life activities, such as video games, online shopping, or finding fitments lost in the house or a mall, etc.

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155 Views

This file contains VLC RSSI data from the IoRL Measurement campaign. 

The processing files included are developed by Ben Meunier from Brunel University London. 

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258 Views

The Internet of Things (IoT) technology has revolutionized every aspect of everyday life by making everything smarter. IoT became more popular in recent years due to its vast applications in many fields such as smart cities, agriculture, healthcare, ambient assisted living, animal tracking, etc. Localization of a sensor node refers to knowing a sensor node's geographical location in the IoT network.

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232 Views

Abstract— In this research, a model for LTE network performance forecast during the rainy season was developed. During the rainy season, cellular network performance is greatly affected. optimization Engineers find it difficult to ascertain cellular (LTE) network parameters that negatively influences the network performance and make a performance prediction during the rainy season. In achieving this, an experimental approach was used to study network samples collected over the LTE network of MTTN in Lagos during the rainy season for a period of 48weeks.

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399 Views

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