The rapid growth of the Internet of Things (IoT) and the emergence of Low Power Wide Area Network (LPWAN) technologies, such as LoRaWAN, have revolutionized how applications and services can leverage sensor and actuator devices. This paper demonstrates the framework with two new enhancements that enable the LoRaWAN system architecture to support edge processing capabilities in a large-scale urban scenario with mobile devices. The demonstration showcases the adaptability and load balancing capabilities of the resulting system, allowing seamless transition handover between edge nodes. The framework operates within the traditional LoRaWAN architecture, ensuring backward compatibility, and introduces an automatic traffic flow management system based on real-time resource monitoring. We use the LoRaMC dataset to highlights the effectiveness, scalability, and robustness of the proposed system, showcasing the integration of edge processing capabilities into the LoRaWAN architecture for improved data processing and system performance in the city-scale scenario.
Frangella L., Milani S., Garlisi D., Chatzigiannakis I. (2024). Demo: Enhancing LoRaWAN Networks with Edge Computing: A Demonstration on a Large-Scale Scenario. In International Conference on Embedded Wireless Systems and Networks. Junction Publishing.
Demo: Enhancing LoRaWAN Networks with Edge Computing: A Demonstration on a Large-Scale Scenario
Garlisi D.;
2024-01-01
Abstract
The rapid growth of the Internet of Things (IoT) and the emergence of Low Power Wide Area Network (LPWAN) technologies, such as LoRaWAN, have revolutionized how applications and services can leverage sensor and actuator devices. This paper demonstrates the framework with two new enhancements that enable the LoRaWAN system architecture to support edge processing capabilities in a large-scale urban scenario with mobile devices. The demonstration showcases the adaptability and load balancing capabilities of the resulting system, allowing seamless transition handover between edge nodes. The framework operates within the traditional LoRaWAN architecture, ensuring backward compatibility, and introduces an automatic traffic flow management system based on real-time resource monitoring. We use the LoRaMC dataset to highlights the effectiveness, scalability, and robustness of the proposed system, showcasing the integration of edge processing capabilities into the LoRaWAN architecture for improved data processing and system performance in the city-scale scenario.File | Dimensione | Formato | |
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Edge2LoRa_mobility_demo.pdf
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