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

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@Article{Bucur:2014:ASC,

• author = "Doina Bucur and Giovanni Iacca and Giovanni Squillero and Alberto Tonda",
• title = "The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation",
• journal = "Applied Soft Computing",
• year = "2014",
• volume = "16",
• pages = "210--222",
• month = mar,
• keywords = "genetic algorithms, genetic programming, genetic improvement, Collection tree protocol (CTP), MultiHopLQI (MHLQI), Wireless sensor networks (WSN), Evolutionary algorithms (EA), Routing protocols, Verification, Energy consumption",
• ISSN = "1568-4946",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494613004213",
• DOI = "doi:10.1016/j.asoc.2013.12.002",
• size = "13 pages",
• abstract = "The analysis of worst-case behaviour 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 analysing 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.",
• notes = "Also known as \cite{Bucur2014210}",

}

Genetic Programming entries for Doina Bucur Giovanni Iacca Giovanni Squillero Alberto Tonda

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