Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Low‐power localisation systems are crucial for machine‐to‐machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range‐based technique to estimate the distance of a target node from a LoRa gateway using machine‐learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range‐based distance mapping with trilateration and fingerprint‐based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF‐based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration‐based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint‐based direct location estimation approaches.

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“…Coordinate estimation from RSSI values using Random Neural Networks is proposed in ref. [21]. Ibrahim et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Coordinate estimation from RSSI values using Random Neural Networks is proposed in ref. [21]. Ibrahim et al [22] proposed a Support Vector Regression (SVR)-enhanced fingerprint approach based on RSSI in Jazan City in Saudi Arabia.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning‐based LoRa localisation using multiple received signal features

Islam

Murray

Diepeveen

et al. 2023

IET Wireless Sensor Systems

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“…Similarly, a quantum bird migration optimizer-based node localization (QBMA-NL) [29] technique is presented, which uses RSSI and the Euclidean distance to find out the location of the target nodes. In [30] random neural networks have been used for outdoor LoRa localization. In this method, the fingerprints need to be collected first; then, using those fingerprints, the neural networks have been implemented to predict the location of the end node.…”

Section: Related Workmentioning

confidence: 99%

Experimental Evaluation of Trilateration-Based Outdoor Localization with LoRaWAN

Magsi¹,

Khir²,

Nawi³

et al. 2023

Computers, Materials &Amp; Continua

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Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal's energy below the noise floor with no additional hardware requirements and ease of implementation. This research article offers the technical evaluation of the trilateration technique, its efficiency, and its limitations for the localization using LoRa ESP in a large outdoor populated campus environment. Additionally, experimental evaluations are conducted to determine the effects of frequency hopping, outlier removal, and increasing the number of gateways on localization accuracy. Results obtained from the experiment show the importance of calculating the path loss exponent for every frequency to circumvent the high localization error because of the frequency hopping, thus improving the localization performance without the need of using only a single frequency.

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“…However, one of the disadvantages of this method is that it relies on the necessity of smart phones. Winfred Ingabire et al proposed outdoor node localization method using LoRaWAN Signal Strength [9]. In that method they used LoRa modules signals instead of using GPS to decrease the power consumption.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Outdoor Localization Improvement for IoT Anchor Nodes

Zorkany

Abd-Elrahman

Fahmy

2022

Preprint

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Real-time localization improvement is needed in most Internet of Things (IoT) applications that require accurate knowledge of the physical location of IoT devices, especially in the areas that having a weak GPS signals or in moving objects like smart bike-sharing, robots, and off- roads vehicles. Recently, most outdoor localizations use a hybrid mechanism integrating some IoT devices occupying with GPS (Anchor Nodes) and others IoT devices with wireless sensors nodes like LoRaWAN (without GPS-Unknown node). This hybrid method employed to achieve low power consumption and low cost solution. Our proposed work focuses on determining the Anchor node locations with high accuracy to enhance the overall accuracy of all IoT nodes. To achieve this goal, the installation of low power consummations and low-cost localization sensors modules will be used in the proposed Anchor IoT node. These modules as inertial measuring unit (IMU) will integrate with a low cost Global Positioning System (GPS) module to enhance the locations accuracy of these IoT devices. Then, the proposed method will be implemented on AVR microcontroller (ATMega 128) to develop an IoT enhanced localization unit. The general structure of the proposed method from a mathematical point of view is presented with recursive algorithm update equations. Moreover, this paper will show how the IoT device’s state can be known accurately and at a high update rate (100Hz, 10 milliseconds).

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Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Cited by 20 publications

References 46 publications

Machine learning‐based LoRa localisation using multiple received signal features

Machine learning‐based LoRa localisation using multiple received signal features

Experimental Evaluation of Trilateration-Based Outdoor Localization with LoRaWAN

Real-Time Outdoor Localization Improvement for IoT Anchor Nodes

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