The development of a wireless sensor network sensing node utilising adaptive self-diagnostics

Abstract

In Wireless Sensor Network (WSN) applications, sensor nodes are often deployed in harsh environments. Routine maintenance, fault detection and correction is difficult, infrequent and expensive. Furthermore, for long-term deployments in excess of a year, a node's limited power supply tightly constrains the amount of processing power and long-range communication available. In order to support the long-term autonomous behaviour of a WSN system, a self-diagnostic algorithm implemented on the sensor nodes is needed for sensor fault detection. This algorithm has to be robust, so that sensors are not misdiagnosed as faulty to ensure that data loss is kept to a minimum, and it has to be light-weight, so that it can run continuously on a low power microprocessor for the full deployment period. Additionally, it has to be self-adapative so that any long-term degradation of sensors is monitored and the self-diagnostic algorithm can continuously revise its own rules to accomodate for this degradation. This paper describes the development, testing and implementation of a heuristically determined, robust, self-diagnostic algorithm that achieves these goals. © Springer-Verlag Berlin Heidelberg 2007.