Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science
Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s enviro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/11/1/7 |
| _version_ | 1797240645964791808 |
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| author | Mariana Ávalos-Arce Heráclito Pérez-Díaz Carolina Del-Valle-Soto Ramon A. Briseño |
| author_facet | Mariana Ávalos-Arce Heráclito Pérez-Díaz Carolina Del-Valle-Soto Ramon A. Briseño |
| author_sort | Mariana Ávalos-Arce |
| collection | DOAJ |
| description | Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments. |
| first_indexed | 2024-04-24T18:10:44Z |
| format | Article |
| id | doaj.art-d75528cd1de747c9a1112886d0a9a564 |
| institution | Directory Open Access Journal |
| issn | 2227-9709 |
| language | English |
| last_indexed | 2024-04-24T18:10:44Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj.art-d75528cd1de747c9a1112886d0a9a5642024-03-27T13:46:49ZengMDPI AGInformatics2227-97092024-01-01111710.3390/informatics11010007Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data ScienceMariana Ávalos-Arce0Heráclito Pérez-Díaz1Carolina Del-Valle-Soto2Ramon A. Briseño3Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, JA, MexicoFacultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, JA, MexicoFacultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, JA, MexicoCentro Universitario de Ciencias Económico Administrativas, Universidad de Guadalajara, Zapopan 45180, JA, MexicoWireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.https://www.mdpi.com/2227-9709/11/1/7packet losspacket sniffer databinary classification interferencewireless communications |
| spellingShingle | Mariana Ávalos-Arce Heráclito Pérez-Díaz Carolina Del-Valle-Soto Ramon A. Briseño Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science Informatics packet loss packet sniffer data binary classification interference wireless communications |
| title | Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science |
| title_full | Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science |
| title_fullStr | Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science |
| title_full_unstemmed | Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science |
| title_short | Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science |
| title_sort | uncovering the limitations and insights of packet status prediction models in ieee 802 15 4 based wireless networks and insights from data science |
| topic | packet loss packet sniffer data binary classification interference wireless communications |
| url | https://www.mdpi.com/2227-9709/11/1/7 |
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