Title: Statistically-enhancing the diagnosis of packet losses in WSNs

Authors: Antonio Tedeschi; Daniele Midi; Francesco Benedetto; Elisa Bertino

Addresses: Signal Processing for Telecommunications and Economics Lab, University of Roma TRE, Rome, Italy ' Department of Computer Science and CERIAS, Purdue University, West Lafayette, IN, USA ' Signal Processing for Telecommunications and Economics Lab, University of Roma TRE, Rome, Italy ' Department of Computer Science and CERIAS, Purdue University, West Lafayette, IN, USA

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.

Keywords: wireless sensor networks; WSNs; interference; variance; received signal strength indicator; RSSI; link quality indicator; LQI; packet reception rate; PRR; constant false alarm rate; CFAR; false alarm probability; network security; packet losses; statistical modelling.

DOI: 10.1504/IJMNDI.2017.082795

International Journal of Mobile Network Design and Innovation, 2017 Vol.7 No.1, pp.3 - 14

Available online: 08 Mar 2017 *

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