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Abstract:The characterisation of uncertainty and the management of Quality of Service are important issues in mobile communications. In a Wireless Sensor Network, there is a high probability of redundancy, correlation and noise in the sensor features since data is often collected from a large array of densely deployed neighbouring sensors. This article proposes a soft computing approach to manage uncertainty by reasoning over inconsistent, incomplete, and fragmentary information using classical rough set and dominance-based rough set theories. A methodological and computational basis is provided and is illustrated in a real world sensor network application of aquatic biodiversity mapping under uncertainty.

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This paper proposes a soft computing approach to manage uncertainty and rule discovery by reasoning over inconsistent, incomplete and fragmentary information using dominance-based rough set theories. A methodological and computational basis is illustrated in a sensor network application scenario of a forest fire detection system.

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