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Autonomic and Coevolutionary Sensor Networking

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Autonomic Communication

Abstract

(WSNs) applications are often required to balance the tradeoffs among conflicting operational objectives (e.g., latency and power consumption) and operate at an optimal tradeoff. This chapter proposes and evaluates a architecture, called BiSNET/e, which allows WSN applications to overcome this issue. BiSNET/e is designed to support three major types of WSN applications: , and hybrid applications. Each application is implemented as a decentralized group of, which is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding network conditions and adaptively invoking behaviors such as pheromone emission, reproduction, migration, swarming and death. Each agent has its own behavior policy, as a set of genes, which defines how to invoke its behaviors. BiSNET/e allows agents to evolve their behavior policies (genes) across generations and autonomously adapt their performance to given objectives. Simulation results demonstrate that, in all three types of applications, agents evolve to find optimal tradeoffs among conflicting objectives and adapt to dynamic network conditions such as traffic fluctuations and node failures/additions. Simulation results also illustrate that, in hybrid applications, data collection agents and event detection agents coevolve to augment their adaptability and performance.

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Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, Boston, USA

    Pruet Boonma & Junichi Suzuki

Authors
  1. Pruet Boonma
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  2. Junichi Suzuki
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Correspondence to Pruet Boonma .

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Editors and Affiliations

  1. Dept. Computer &, University of Western Macedonia, Agios Dimitrios Park, Kozani, 501 00, Greece

    Athanasios V. Vasilakos

  2. Electrical & Computer Engineering Dept., Rutgers University, Brett Road 94, Piscataway, 08854-8058, U.S.A.

    Manish Parashar

  3. Corporate Research Centre, SAP AG, Vincent-Prießnitz-Str. 1, Karlsruhe, 76131, Germany

    Stamatis Karnouskos

  4. Dept. Electrical & Computer Engineering, University of Alberta, 116 Street 9107, Edmonton, T6G 2V4, Canada

    Witold Pedrycz

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Boonma, P., Suzuki, J. (2009). Autonomic and Coevolutionary Sensor Networking. In: Vasilakos, A., Parashar, M., Karnouskos, S., Pedrycz, W. (eds) Autonomic Communication. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09753-4_14

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  • DOI: https://doi.org/10.1007/978-0-387-09753-4_14

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09752-7

  • Online ISBN: 978-0-387-09753-4

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