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Smart city article details
| Title | Lorawan Optimization Using Optimized Auto-Regressive Algorithm, Support Vector Machine And Temporal Fusion Transformer For Qos Ensuring |
|---|---|
| ID_Doc | 35646 |
| Authors | Elbsir H.; Kassab M.; Bhiri S.; Bedoui M.H.; Castells-Rufas D.; Carrabina J. |
| Year | 2022 |
| Published | International Conference on Wireless and Mobile Computing, Networking and Communications, 2022-October |
| DOI | http://dx.doi.org/10.1109/WiMob55322.2022.9941538 |
| Abstract | The number of LoRaWAN networks have grown worldwide last years, offering a solution for the integration of the Internet of Things in rural and urban areas. After years of development, several performance issues and scalability limitations require to be enhanced for LoRa such as high collision rates and duty cycle limitations. Machine learning offers a chance for LoRaWAN to rise as the reference communication technology that offers the adequate communication performances for IoT. In this paper, our goal is to optimize the LoRaWAN network performances using detection mechanism and artificial intelli-gence to predict its behavior. first, we evaluate the full potential of the LoRaWAN factory setting, and we introduced a Quality of Service demanding application. Second, we constructed our proper database using available application criteria, we included a quality of service mechanism to simulate the effect of a new application connecting to a stable network and the perturbing causes. Then, we used two different methods one for classification, the second for prediction, and then the optimization. For clas-sification using Auto-Regressive and optimization it using burg algorithm and firefly algorithm, then we used the support vector machine for traffic classification, results are very promising, we were able to detect normal traffic, a normal surge, and an abnormal surge of network traffic with up to 99% accuracy. For prediction, we used a new algorithm developed by google Temporal Fusion Transformer. We were able to predict the network behaviour ahead with 14 days with 95% accuracy and up to 30 days with 80% accuracy. We were able to optimize the network to absorb the abnormal surge and return to normal in less than 60% of the normal time, uplifting the packet delivery ratio for uplink traffic by 20% and downlink traffic by 50%. © 2022 IEEE. |
| Author Keywords | channel occupancy; cluster analysis; LoRaWAN; Machine Learning; network optimization; prediction analysis; smart city |
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