Duty Cycle Measurement Techniques for Adaptive and Resilient Autonomic Systems

When systems are deployed in environments where change is the rule rather than the exception, adaptability and resilience play a crucial role in order to preserve good quality of service. This work analyses methods that can be adopted for the duty cycle measurement of sensor-originated waveforms. These methods start from the assumption that no regular sampling is possible and thus they are naturally thought for an adaptive coexistence with other heterogeneous and variable tasks. Hence, the waveform carrying the information from low-priority sensors can be sampled only at instants that are non-controlled. To tackle this problem, this paper proposes some algorithms for the duty cycle measurement of a digital pulse train signal that is sampled at random instants. The solutions are easy to implement and lightweight so that they can be scheduled in extremely loaded microcontrollers. The results show a fast convergence to the duty cycle value; in particular, a considerable gain with respect to other known solutions is obtained in terms of the average number of samples necessary to evaluate the duty cycle with a desired accuracy is obtained.