Experimental data set analysis of RSSI‐based indoor and outdoor localization in LoRa networks

Goldoni, Emanuele; Prando, Luca; Vizziello, Anna; Savazzi, Pietro; Gamba, Paolo

doi:10.1002/itl2.75

Cited by 56 publications

(39 citation statements)

References 6 publications

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“…One of the studies performed a simulation of localization using a combination of TDoA and AoA techniques [ 8 ], but did not test these in-field. Another article compares the outdoor versus indoor localization using RSSI [ 9 ]. Further improvements have been suggested in [ 10 ].…”

Section: Current State Of Geolocation In Lorawan Technologymentioning

confidence: 99%

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice

Pospisil

Fujdiak

Mikhaylov

2020

Sensors

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The paper deals with the localization in a low-power wide-area-network (LPWAN) operating long-range wide-area-network (LoRaWAN) technology. The LoRaWAN is, today, one of the most widely used connectivity-enabling technologies for the battery-powered smart devices employed in a broad range of applications. Many of these applications either require or can benefit from the availability of geolocation information. The use of global positioning system (GPS) technology is restrained by the bad propagation of the signal when the device is hidden indoors, and by energy consumption such a receiver would require. Therefore, this paper focuses on an alternative solution implying the use of the information readily available in the LoRaWAN network and application of the time difference of arrival (TDoA) method for the passive geolocation of end-devices in the network. First, the limits of geolocation services in networks that use narrow-band communication channels are discussed, as well as the relevant challenges faced by the TDoA approach. Then, we select five classic TDoA algorithms and evaluate their performance using simulation. Based on these results, we select the two providing the best accuracy (i.e., Chan’s and Foy’s). These algorithms were tested by the field measurements, using the specially designed low-cost gateways and test devices to estimate their real-life performance.

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Section: Current State Of Geolocation In Lorawan Technologymentioning

confidence: 99%

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice

Pospisil

Fujdiak

Mikhaylov

2020

Sensors

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“…The algorithm that compared is trilateration, min-max, and maximum likelihood. The experiment is in indoor and outdoor environments (Goldoni et al, 2019). Tested on indoor area and number of noises created from one source, made the data already fixed on several point tested.…”

Section: Literature Reviewmentioning

confidence: 99%

Non-Line of Sight LoRa –Based Localization using RSSI-Kalman-Filter and Trilateration

Anugrah

Rakhmatsyah

Wardana

2020

ijoict

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<span>The method that analyzes in this research is the combination of the Received Signal Strength Indicator (RSSI) with the Trilateration Method. This research also filtered the RSSI value using the Kalman filter method for smoothing data. The localization system traditionally based on Global Positioning System (GPS) device. However, GPS technology not working well in Non-line-of-sight (NLOS) like an indoor location or mountain area. The other way to implement the localization system is by using LoRa technology. This technology used radio frequency to communicate with each other node. The radiofrequency has a measurement value in the form of signal strength. These parameters, when combined with the trilateration method, can be used as a localization system. After implementation and testing, the system can work well compared with the GPS system for localization. RMSE is used to calculate error distance on these methods, the result from three methods used, the value from RSSI with Kalman filter have a close result to actual position, then value GPS follows with close result from Kalman filter, and the last one is RSSI without Kalman filter.</span>

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“…The most intensely researched areas in indoor localization have involved the application of indoor localization such as the detection of people indoors, detection of patients in a hospital setting, and tracking blind individuals inside a building [14]. Several technologies such as ZigBee, Bluetooth, LoRa [15], and Wi-Fi [16] have proved useful in indoor localization. Among them, ZigBee appears to be the best way of implementing a localization system to monitor patients compared with other technologies [17] due to low power consumption [18], ease of use, cost-effectiveness, no requirement for external hardware and suitable communication distance.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

et al. 2020

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The number of older adults with Alzheimer's disease is increasing every year. The associated memory problems cause many difficulties for Alzheimer's patients and their caretakers; patients may even become lost in familiar surroundings. In this paper, a proposed localization system based on a wireless sensor network (WSN) and backpropagation based artificial neural network (BP-ANN) was practically implemented to detect and determine the position of an Alzheimer's patient in an indoor environment. The proposed system consisted of four ZigBee-based XBee S2C anchor nodes and one mobile node carried by the Alzheimer's patient. The received signal strength indicator (RSSI) of the anchor nodes was collected by the mobile node using a laptop supported by X-CTU software. The obtained RSSI values were used as input for training, testing, and validation processes of the BP-ANN, while two-dimension (2D) locations (x and y) were used as the output of the ANN. The results showed that the obtained mean localization errors were 0.964 and 0.921m for validation and testing phases, respectively, after applying the ANN. Based on a comparison with state-ofthe-art technology, we deduced that the proposed ANN method outperformed other techniques in previous studies in terms of mean localization error.

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Experimental data set analysis of RSSI‐based indoor and outdoor localization in LoRa networks

Cited by 56 publications

References 6 publications

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice

Non-Line of Sight LoRa –Based Localization using RSSI-Kalman-Filter and Trilateration

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

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