RSSI

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

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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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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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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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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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RSSI-Dataset

The RSSI-Dataset provides a comprehensive set of Received Signal Strength Indication (RSSI) readings from within two indoor office buildings. Four wireless technologies were used:

  • Zigbee (IEEE 802.15.4),
  • WiFi (IEEE 802. 11),
  • Bluetooth Low Energy (BLE) and
  • Long Range Area-Wide Network (LoRaWAN).

For experimentation Arduinos Raspberry Pi, XBees, Gimbal beacons Series 10 and Dragino LoRa Shield were also used.  

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