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, 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

Citations