LoRa support for long-range real-time inter-cluster communications over Bluetooth Low Energy industrial networks

Leonardi, Luca; Bello, Lucia Lo; Patti, Gaetano

doi:10.1016/j.comcom.2022.05.026

Cited by 19 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…λ-s: Minimizing the MSE Among the potentially many α values included in the starting sequence αS, we aim to assess which ones affect prediction accuracy more. The selection of a small subset of αS, which includes the most relevant EMA weights, has been addressed as a minimization problem of the mean squared error µ e 2 of the COM model (6), constrained by (7) and with the boundaries…”

Section: B λ-S: Linear Combinationmentioning

confidence: 99%

“…In this respect, wireless communication technologies are one of the main enablers, by supporting mobility and automatic (re)configurability of industrial systems [4]. The ever increasing need for wire-free solutions in industries, homes, and cities, implies that specific research activities, aimed at making the related technologies capable to fulfill the very strict requirements, in terms of dependability and end-to-end latency, of industrial networks [5], [6], [7], [8], [9], [10], are more and more demanded. Besides communication, services like localization [11], timesynchronization [12], and roaming [13] have to be also provided for enabling fully wireless industrial systems (required, e.g., when devices are characterized by mobility), by adapting the existing approaches to the specific wireless technology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linear Combination of Exponential Moving Averages for Wireless Channel Prediction

Formis,

Scanzio,

Cena

et al. 2023

2023 IEEE 21st International Conference on Industrial Informatics (INDIN)

View full text Add to dashboard Cite

The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications.In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity.

show abstract

Section: B λ-S: Linear Combinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linear Combination of Exponential Moving Averages for Wireless Channel Prediction

Formis,

Scanzio,

Cena

et al. 2023

2023 IEEE 21st International Conference on Industrial Informatics (INDIN)

View full text Add to dashboard Cite

show abstract

“…The advances in IoT aim to provide real-time monitoring and intelligent services. The innovative range communication systems such as 4G, 5G, and GPS technologies have been widely used to control and regulate the real-time environments [ 17 , 18 , 19 , 20 ]. An outstanding classification of IoT arrangements includes short battery-powered network components such as end nodes, which are equipped with actuators and sensors that wirelessly interact.…”

Section: Related Workmentioning

confidence: 99%

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study

Sarkar

Kavitha

Ali

2022

Sensors

View full text Add to dashboard Cite

Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology’s Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2–20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities.

show abstract

“…In addition, this test was also carried out to support the digitalization of technological innovation where the research conducted was within the frequency radius T HE development of LoRaWAN technology is very important for consideration in planning and implementing IoT (Internet of Things) applications based on several reasons, such as the main advantages of LoRaWAN (Long Range Wide Area Network), which are simple devices that have a long battery life [1], range, gateway position, signal quality, and signal interference [2], [3]. IoT technology can revolutionize the global world because the objects used are equipped with an internet connection, which allows connected devices to collect and exchange information [3], [8]. The scope of urban planning on the surrounding conditions affects the propagation of LoRaWAN ability to reflect signals between buildings, one of which is physical barriers in this case affecting signal strength [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Experimental Visualization of the LoRaWAN Variable Correlation in Jakarta

Andrianingsih,

Wibowo,

Wirawan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This paper presents an experimental study on the performance of LoRaWAN, a wireless communication protocol capable of transmitting data over long distances with low power consumption and low cost. The study aims to understand the relationship between transmission and connectivity in the context of LoRaWAN. Previous studies on LoRaWAN have not explored the impact of variables such as building height, elevation above sea level (MASL), drive test elevation, gateway elevation, drive test distance, signal-to-noise ratio (SNR), and Received Signal Strength Indicator (RSSI) on the coverage status based on free space loss. Therefore, this research conducted experiments using drive test devices (K02188, K02204, and K02205) to investigate the influence of these variables on the performance of LoRaWAN. The experimental results include log data and historical data, demonstrating the reliability of LoRaWAN technology in open space.The RSSI measurements, which represent the signal strength, range from around -127 dB to 60 dB, with an average SNR of 7 dB. The coverage status is classified into different categories, including Excellent (12.55%), Acceptable (16.57%), Good (19.86%), Poor (23.98%), and Very Poor (27.01%). The correlation variable analysis reveals that the coverage status is highly correlated with the independent variables. These findings provide valuable insights into the performance and reliability of LoRaWAN in open spaces, and the correlation variables shed light on the factors influencing the coverage status.

show abstract

LoRa support for long-range real-time inter-cluster communications over Bluetooth Low Energy industrial networks

Cited by 19 publications

References 27 publications

Linear Combination of Exponential Moving Averages for Wireless Channel Prediction

Linear Combination of Exponential Moving Averages for Wireless Channel Prediction

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study

Experimental Visualization of the LoRaWAN Variable Correlation in Jakarta

Contact Info

Product

Resources

About