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In this paper, we have proposed a Condition-based Maintenance technique for vehicle tire pressure monitoring utilizing univariate statistical analysis. Statistical techniques are very powerful for predicting the future states based on current and previous states of the system or subsystem. Two important statistical techniques ARAR and Holt-Winters have been studied for their robustness to the predictions of such data set. This paper also performs comparative simulation studies to prove the usefulness of both the algorithms based on the data available from wireless sensor nodes. These sensors are directly mounted on tires externally and report the current air pressure to control unit. The control unit performs tire pressure prognosis using univariate statistical technique.

An average person in North America comes in regular contact with an amazing number of real-time systems on a regular basis. For example, an automobile today can have upward of 40 plus real time systems on board. A residential house has real-time systems in many household electronics. Real-time systems today deliver many customer-facing functionalities. There is a need to express requirements in a manner relevant the customers, for purposes of validation and verification. The proliferation of real-time systems on a single device also creates opportunities for reuse of functionality that rarely existed before. Use Case technique approaches requirements from a scenario perspective. Scenario provides context and external view points that are better suited to address the validation, verification and reuse challenges. In this paper, we present: A few examples of extending use cases for real-time systems to capture system functional requirements.