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Self-adapting maximum flow routing for autonomous wireless sensor networks

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Abstract

Autonomous wireless sensor networks are subject to power, bandwidth, and resource limitations that can be represented as capacity constraints imposed to their equivalent flow networks. The maximum sustainable workload (i.e., the maximum data flow from the sensor nodes to the collection point which is compatible with the capacity constraints) is the maxflow of the flow network. Although a large number of energy-aware routing algorithms for ad-hoc networks have been proposed, they usually aim at maximizing the lifetime of the network rather than the steady-state sustainability of the workload. Energy harvesting techniques, providing renewable supply to sensor nodes, prompt for a paradigm shift from energy-constrained lifetime optimization to power-constrained workload optimization.

This paper presents a self-adapting maximum flow (SAMF) routing strategy which is able to route any sustainable workload while automatically adapting to time-varying operating conditions. The theoretical properties of SAMF routing are formally proved and a simulation model is developed on top of OMNeT++ (http://www.omnetpp.org/) in order to enable simulation-based assessment and design exploration. Simulation results are reported which demonstrate the applicability of the proposed approach.

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Authors and Affiliations

  1. ISTI, University of Urbino, Piazza della Repubblica, 13, 61029, Urbino, Italy

    Alessandro Bogliolo, Saverio Delpriori, Emanuele Lattanzi & Andrea Seraghiti

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  1. Alessandro Bogliolo
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  2. Saverio Delpriori
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  3. Emanuele Lattanzi
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  4. Andrea Seraghiti
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Correspondence to Alessandro Bogliolo.

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Bogliolo, A., Delpriori, S., Lattanzi, E. et al. Self-adapting maximum flow routing for autonomous wireless sensor networks. Cluster Comput 14, 1–14 (2011). https://doi.org/10.1007/s10586-009-0115-x

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  • DOI: https://doi.org/10.1007/s10586-009-0115-x

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