Real-Time Analog Event-Detection for Event-Based Synchronous Sampling of Sparse Sensor Signals

Abstract

The unprecedented rise of the internet-of-things (IoT) has led to an enormous rate of data generation from sensors and IoT devices, calling for an urgent need for more intelligent approaches of data acquisition from such sensors. This paper proposes an event-based sampling technique for selective acquisition of event-data from sparse sensor signals, produced by remote sensors and IoT devices. The proposed technique simultaneously combines advantageous features of uniform (synchronous) and non-uniform (asynchronous) techniques. In the proposed approach, event-detection is achieved in real-time in the analog domain, prior to analog-to-digital conversion (ADC) and digital processing, by means of an analog event-detection (AED) circuit. A proof-of-concept design for the AED circuit is implemented and analyzed through experiments and extensive SPICE simulations, demonstrating its capability of detecting the onset of an event in real-time; with speeds on the order of microseconds. Such rapid analog event-detection can enable the real-time control of the sampling process, such that no sampling would occur unless there is an event, giving rise to a richer information content in the acquired data. Moreover, the proposed technique allows all system blocks to remain in sleep-mode until an event is detected, dramatically reducing their overall power consumption. The impact of the proposed technique is further demonstrated on an important industrial application; seismic data acquisition, by testing the design - through SPICE simulation – on real seismic data obtained from seismic surveys for oil and gas exploration.