Harvesting WiFi Received Signal Strength Indicator (RSSI) for Control/Automation System in SOHO Indoor Environment with ESP8266

Localization and tracking technology based on received signal strength indicator (RSSI) is one of the most popular topics because of its low demand on hardware and cost. But the complexity of the indoor environment, leads to the uncertainty of the radio propagation which can seriously affect the positioning accuracy based on the received signal strength. Focused on the wall reflection in the indoor environment, the radio propagation characteristic based on ray-tracing model is analyzed and one strategy for the near wall localization is presented. The actual hardware platform and experimental test results show the applicability of the empirical logarithmic path loss model for localization and the effect of the wall reflection.

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Indoor localization system based on virtual access points with filtering schemes

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719866135 ◽

2019 ◽

Vol 15 (7) ◽

pp. 155014771986613 ◽

Cited By ~ 2

Author(s):

Dong Myung Lee ◽

Boney Labinghisa

Keyword(s):

Kalman Filter ◽

Indoor Environment ◽

Performance Testing ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Indoor Environments ◽

Received Signal Strength Indicator ◽

Access Points ◽

Error Distance

In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

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Estimation of the number of people in an indoor environment based onWiFi received signal strength indicator

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i3.3038 ◽

2021 ◽

Vol 10 (3) ◽

pp. 1475-1483

Author(s):

Hakam Marwan Zaidan ◽

Emad Ahmed Mohammed ◽

Dheyaa Hussein Alhelal

Keyword(s):

Indoor Environment ◽

Polynomial Regression ◽

Signal Strength ◽

Received Signal Strength ◽

Line Of Sight ◽

Concrete Wall ◽

Received Signal Strength Indicator ◽

The People ◽

Two Stages ◽

Non Line Of Sight

WiFi access points are widely spread everywhere in all our daily life routines. Using these devices to provide services other than the Internet is becoming familiar nowadays.This paper conducts an experimental study to estimate the number of people in an indoor environment through two system setups, line of sight, and non-line of sight. Relationship modeling between WiFi received signal and the number of people uses polynomial regression. The experiment comprised of two stages: first is the data collection from a controlled number of people. Then, the collected data used to train the system through polynomial regression. The second is testing the system’s effectiveness by applying it to an uncontrolled environment. Testing results revealed efficiency in using WiFi received signal strength to do the people counting (up to 60) because of the accuracy achievements of 93.17% in the line of sight system. The non-line of sight system disclosed randomness in the received signal strength indicator regardless of the change in the number of people. The randomness is mainly caused by the fading effect of the concrete wall. Therefore it is inefficient to use the non-line of sight system in concrete buildings.

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Location detection of the mobile sensor with fingerprinting‐based cascade artificial neural network model using received signal strength indicator in 3D indoor environment

International Journal of Communication Systems ◽

10.1002/dac.5000 ◽

2021 ◽

Author(s):

Taner Tuncer ◽

Ebubekir Erdem

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Indoor Environment ◽

Signal Strength ◽

Received Signal Strength ◽

Received Signal Strength Indicator ◽

Location Detection

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Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning

Sensors ◽

10.3390/s18103419 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3419 ◽

Cited By ~ 3

Author(s):

Yitang Peng ◽

Xiaoji Niu ◽

Jian Tang ◽

Dazhi Mao ◽

Chuang Qian

Keyword(s):

Indoor Environment ◽

Signal Strength ◽

Indoor Positioning ◽

Received Signal Strength ◽

Kriging Interpolation ◽

Unmanned Ground Vehicle ◽

Received Signal Strength Indicator ◽

Ground Vehicle ◽

Fingerprint Database ◽

Signals Of Opportunity

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

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Particle swarm optimization–based minimum residual algorithm for mobile robot localization in indoor environment

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417729277 ◽

2017 ◽

Vol 14 (5) ◽

pp. 172988141772927 ◽

Cited By ~ 2

Author(s):

Yunzhou Zhang ◽

Hang Hu ◽

Wenyan Fu ◽

Hao Jiang

Keyword(s):

Particle Swarm Optimization ◽

Mobile Robot ◽

Indoor Environment ◽

Particle Swarm ◽

Signal Strength ◽

Received Signal Strength ◽

Robot Localization ◽

Received Signal Strength Indicator ◽

Swarm Optimization ◽

Minimum Residual

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

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KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana ◽

10.29122/alami.v3i2.3731 ◽

2019 ◽

Vol 3 (2) ◽

pp. 88

Author(s):

Riski Fitriani

Keyword(s):

Early Warning ◽

Early Warning System ◽

Warning System ◽

Signal Strength ◽

Received Signal Strength ◽

Received Signal Strength Indicator ◽

Landslide Early Warning ◽

Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

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