The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation

• We propose an efficient evolutionary methodology for analyzing the performance of wireless sensor networks routing protocols.
• We applied the methodology to two ad hoc collection tree protocols: MHLQI and CTP.
• We ran extensive experiments and obtained large sets of topologies (of up to 50 nodes) showing abnormally high traffic.
• We empirically extracted predictive topological metrics that were shown to statistically correlate with high network traffic.
• We verified with a posteriori experiments that these metrics are also a sufficient cause of high traffic.

The analysis of worst-case behavior in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analyzing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of “how interesting” such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols’ poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here – that is, the new topological metrics and the causal explanation – can be fruitfully reused in different contexts, even beyond wireless sensor networks.

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