2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud) (2015)
Aug. 24, 2015 to Aug. 26, 2015
Packet losses are an important class of adverse events in Wireless Sensor Networks (WSNs), they can be caused by either a compromised or misbehaving node, or an attack focused on the wireless links of the network. Understanding the underlying cause is critical for the deployment of effective response measures aimed at restoring the network functionality. Shebaro et al.  proposed an initial approach for fine-grained analysis (FGA) of packet losses, and implemented and evaluated a tool based on it. Such approach profiles the wireless links between the nodes using resident metrics, such as the received signal strength indicator (RSSI) and the link quality indicator (LQI) for every packet, in order to achieve an accurate diagnosis of their root causes. The accuracy of their approach relies on the correct choice of some system parameters and thresholds, and empirically-determined values such as those proposed in their work might not always be optimal. Moreover, to reduce the burden on the network administrator, their approach uses a single threshold value set for the entire WSN, which can be suitable for some neighborhoods but not appropriate for others. In this work, we design an approach that builds a statistical model for determining optimal system thresholds by exploiting the variances of RSSI and LQI. Our model also has the advantage of allowing the setting of an individual threshold for each link. We have validated our approach through extensive MATLAB simulations based on real sensor data, showing that our model is accurate and its system parameters lead to an optimally-accurate fine-grained analysis of the underlying causes of packet losses.
Interference, Wireless sensor networks, Mathematical model, Packet loss, Testing
D. Midi, A. Tedeschi, F. Benedetto and E. Bertino, "Statistically-Enhanced Fine-Grained Diagnosis of Packet Losses," 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud)(FICLOUD), Rome, Italy, 2015, pp. 748-753.