Knowledge-based duty cycle estimation in wireless sensor networks: Application for sound pressure monitoring

Wireless sensor networks comprise an important research area and a near future for industry and communications. Wireless sensor networks contain resource-constrained sensor nodes that are powered by small batteries, limited process and memory and wireless communication. These features give sensors their versatility and drawbacks, such as their limited operating lifetimes. To feasibly deploy wireless sensor networks with isolated motes, several approaches and solutions have been developed; the most common, apart from using alternative power sources such as solar panels, are those that put sensors to sleep for time periods established by the application. We thus propose a fuzzy rule-based system that estimates the next duty cycle, taking the magnitude being tested and battery charge as input. To show how it works, we compare an analytical delta system to our contribution. As an application to test both systems, a sound pressure monitoring application is presented. The results have shown that the fuzzy rule-based system better predicts the evolution of the magnitude by which errors committed by idle periods decrease. This work also shows that application-oriented duty cycle control can be an alternative for measuring systems, thus saving battery and improving sensor node lifetime, with a reasonable loss of precision.

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► Use of a multi-agent architecture for wireless sensors.
► Duty time scheduling based on a fuzzy rule based system inside an intelligent agent.
► The duty time is adjusted by the results of the FRBS inference engine that takes variables from the real measures of the surrounding environment.
► This system can extend lifetime of battery for unattended remote measurement systems.