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Journal Articles IEEE Access Year : 2022

Dense Multi-Channel Sniffing in Large IoT Networks


In this article we deal with the issue of network traffic monitoring in large multi-channel wireless IoT networks. Assuming known link conditions on all radio channels, i.e. connectivity matrix defined through Packet Delivery Ratio on all frequencies and all links between nodes, we propose two methods for defining the number and the positions of sniffer devices, with the goal to maximize the capture of network traffic. Method I is based on probabilistic theory and assumes brute-force search over the connectivity matrix for defining the optimal positions of a given number of sniffers, or, for a given percentage of the traffic to be captured as the input parameter, this method determines number of sniffers and their locations. Due to the computational complexity of brute-force search of the connectivity matrix, we complement Method I and propose Method II. Method II is based on graph theory and uses the minimal Packet Delivery Ratio on each link as the input parameter for defining the number and position of sniffers. We input traffic traces from an experimental testbed into the network to examine and compare both methods. Results show that the Method I outperforms Method II in the percentage of captured network traffic, for a given number of deployed sniffers. However, Method II complements Method I in scenarios where there are a large number of sniffers, due to lower computational complexity.
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Dates and versions

hal-03783828 , version 1 (19-10-2022)





Jelena Crnogorac, Jovan Crnogorac, Malisa Vucinic, Enis Kočan, Thomas Watteyne. Dense Multi-Channel Sniffing in Large IoT Networks. IEEE Access, 2022, ⟨10.1109/ACCESS.2022.3210966⟩. ⟨hal-03783828⟩


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