Packet losses, an important class of adverse events in wireless sensor networks, can be caused by either misbehaving nodes, or attacks focused on the wireless links. Understanding the underlying cause is critical for effective response measures to restore network functionality. Midi et al. (2015) proposed an approach for fine-grained analysis (FGA) of packet losses that profiles the wireless links between the nodes using resident metrics, such as the received signal strength indicator (RSSI) and the link quality indicator (LQI), to accurately diagnose the root causes of the losses. In our work, we design an approach that enhances such previous approach by leveraging a statistical model for determining optimal system thresholds based on the variances of RSSI and LQI, and also supporting individual per-link thresholds. Our validation through real sensor data shows that our model is accurate and leads to an optimal fine-grained analysis of the underlying causes of packet losses.

Tedeschi, A., Midi, D., Benedetto, F., Bertino, E. (2017). Statistically-enhancing the diagnosis of packet losses in WSNs. INTERNATIONAL JOURNAL OF MOBILE NETWORK DESIGN AND INNOVATION, 7(1), 3-14 [10.1504/IJMNDI.2017.082795].

Statistically-enhancing the diagnosis of packet losses in WSNs

TEDESCHI, ANTONIO;BENEDETTO, FRANCESCO;
2017-01-01

Abstract

Packet losses, an important class of adverse events in wireless sensor networks, can be caused by either misbehaving nodes, or attacks focused on the wireless links. Understanding the underlying cause is critical for effective response measures to restore network functionality. Midi et al. (2015) proposed an approach for fine-grained analysis (FGA) of packet losses that profiles the wireless links between the nodes using resident metrics, such as the received signal strength indicator (RSSI) and the link quality indicator (LQI), to accurately diagnose the root causes of the losses. In our work, we design an approach that enhances such previous approach by leveraging a statistical model for determining optimal system thresholds based on the variances of RSSI and LQI, and also supporting individual per-link thresholds. Our validation through real sensor data shows that our model is accurate and leads to an optimal fine-grained analysis of the underlying causes of packet losses.
Tedeschi, A., Midi, D., Benedetto, F., Bertino, E. (2017). Statistically-enhancing the diagnosis of packet losses in WSNs. INTERNATIONAL JOURNAL OF MOBILE NETWORK DESIGN AND INNOVATION, 7(1), 3-14 [10.1504/IJMNDI.2017.082795].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/313936
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