A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.
Nowadays, sensor devices with wireless connectivity such as Wi-Fi, Bluetooth, and ZigBee are becoming important in IoT applications. Human activity monitoring based on sensed signal analysis [
As described in Figure
Sensor-based IoT device concept with sensor interface, processing unit, and wireless connectivity.
Battery recharging and replacement are very inconvenient procedures and a major obstacle in extending various IoT-based applications. Long operating lifetime of the sensor systems is therefore an important requirement in designing the system architecture and the sensor data processing algorithm.
The energy consumption in the sensor systems is caused by sampling the sensor signal, processing the sampled data, and transferring the collected data to the host machine via wireless interface. Traditional sensor devices sample the signal periodically and analyze the sensed data.
This approach has advantages requiring the simple processing unit in terms of hardware resource and the generalized algorithm in terms of software development. Syntactic activation of the entire sensor system results in more power consumption, especially in wireless connectivity.
The conventional sensing platform for human activity monitoring uses general purpose microcontrollers (MCUs), including an analog sensor interface, discrete-time analog-to-digital converter (ADC) as a data sampler, and a sensor data processing unit to analyze collected data.
However, this approach, by which the sensed data is analyzed on the microlevel of data-to-data, has operating power overhead because it is not optimized to consider the long event-to-event distance of human activity signals, which is easily observed in rare-event sensing applications [
In this paper, for efficient sensor data processing in the energy consumption, a semantic sampling method is introduced to capture the signal with the features of interest and is implemented as a preprocessor unit named to signal-to-event converter (S2E), which generates the atomic events instead of the sampled data itself. The extracted atomic events are a relatively small number of samples compared to the syntactic sample data by conventional analog-to-digital converter (ADC) as a signal-to-data converter (S2D).
The proposed S2E replaces the conventional S2D to extract atomic events from the incoming sensor signal. The event identification from human activity monitoring is performed by the event-driven sensor data processing unit (EPU) for the small set of extracted atomic events.
This paper is organized as follows. In Sections
The key motivation of the proposed method begins with the transition to macrolevel processing of the sensor signal by S2E instead of the conventional microlevel analysis for the sensed dataset. Figure
Human activity sampled data representation in event data space.
Attribute and its corresponding elapsed time representation
An example of event-space representation for incoming sensor signal
To address this limitation of the conventional digital system architecture by using the discrete time-based sensor data processing method, we propose an event-driven system architecture that modifies traditional digital system design. We present a theoretical framework to implement an event-driven sensor processor for general rare-event sensing applications by analyzing the system operations.
Our main research begins with an event-space representation of the signal, instead of the digital data space domain. The extracted features of the sensed signal are encoded into the elapsed time between events and informative value such as voltage level and edge phase crossing the trigger point of the signal. The fundamental event defined, which is defined as an atomic event with the most important information, provides a signal representation on an abstract level and reduces the computational complexity in performing basic data processing for extracted informative features of interest. The collected atomic events include partial information in the original signal that specifies whether the desired featured points of the signal are present.
The event-quantization concept extends the time-quantization method for signal representation that uses elapsed time to enhance the conventional data-sampling and processing method. Time quantization monitors only the specific conditions of the signal transition and captures the time-stamps. The event-quantization method also determines whether the specified characteristics of the signal exist.
The event-based approach, with a certain amount of accuracy error, is described by the proposed event-driven sensor data processing flow. The input signal is monitored with specified interest-of-signal characteristics to generate the specific atomic events of the signal. The set of atomic events during the specified region of the signal are traced into the tracer memory as an event vector, which contains the sequence of the atomic events and the time-distance relationship between the atomic events. The traced event vector identifies the approximate result as a final event by comparing it to the expected rules of the atomic events.
These approximation approaches enable us to reduce the computational complexity in order to manipulate a large amount of collected sensing data. As a result, power consumption will be reduced. For applications related to human interaction, an approximation approach enables developers to design the computational block using smaller hardware resources, while providing sufficient performance in limited resolution of the accuracy.
In accuracy-controlling approaches defined from the specifications, our study focused on the data-representation resolution, the timing-resolution of the sampling frequency, and the response time as a delay time [
To overcome the weakness of inefficient power consumption by the frequent CPU wake-up for the discrete time sampling, continuous-time signal processing techniques [
The continuous-time sampling method was introduced to improve the syntactic sampling and processing approach in terms of power consumption, but it requires additional hardware resources and more computational time for the time-distance calculation, which gives rise to additional power consumption. The required power and hardware resource overhead, which are needed to compensate for reduced wake-up power consumption, must be considered in order to achieve benefits in total energy efficiency due to hardware-energy trade-off.
The trade-off in terms of energy and accuracy has been studied widely [
The proposed sensor processor for the rare-event sensing applications adopts the event-driven approach of the continuous-time sampling method. Inaccurate time-data manipulation reduces computational complexity and sampling resolution by determining the presence of featured events in the specific range. The event-detection accuracy can be adjusted by making the trade-off between the processing energy consumption and the operating specification.
Figure
The event-quantization accuracy depending on the resolution of the elapsed time-stamp is described as
With these application-specific constraints in (
The proposed sensor processor is designed with these application-specific constraints by reducing the accuracy of the time-stamp measurement clock, decreasing the bit width of the timer block to capture the time-stamps, and decreasing the operational complexity of the time-to-time distance measurement blocks, which are specially implemented as a dedicated accelerator for event recognition in the implemented hardware.
The conventional MCU performs data sampling in the ADC unit, data tracing in buffer memory, and digital data processing to identify the original event generated by event sources, such as a swipe gesture. The syntactic sampling is performed without the consideration of the incoming signal property. Then, the lazy evaluation using the features of interest is performed to generate the final event
Event sampling based on signal-to-event (S2E) and event-driven data processing.
Data sampling and lazy evaluation for syntactic data processing
Event sampling by early evaluation and event processing
Wake-up frequency for data sampling and event-driven sampling.
The proposed EPU can perform the event relationship analysis with a reduced computation overhead for the smaller set of atomic events. The signal abstraction by extracting atomic events as signal shape in S2E leads to accuracy error in identifying the final event. The overall procedure of the event-driven processing in the modified MCU is described in Figure
The event-driven signal sampling in the proposed architecture captures the signal shapes of interest using the feature scanning window, which determines the presence of the expected features of the signal. The feature scanning window in Figure
Atomic event generator (AEG) based on feature scanning window and signal segmentation.
Configuration of signal scanning window for atomic event extraction
Atomic event generator definition
Examples of set of signal segment
Representing various atomic events according to featured points
The S2E includes the atomic event generator (AEG) unit to generate a set of atomic events by using the user-defined set of signal segments
Figure
Given continuous signal
The meet condition
As shown in Figures
From (
The event-quantized signal representation is dependent on the event slice resolution of the configured set of signal segments, which is described in Figure
Given the configured feature scanning window to extract the atomic events from
One signal shape can be divided into several slices by user-defined signal segmentation. If the time window for signal segmentation is the same as the fixed width
The application-specific constraints in configuring the set of signal segments must be considered for the accuracy-energy trade-off to provide reasonable accuracy of event identification with limited energy consumption. Figure
Iterative procedure to determine sampling method and signal segments for the sensor signal.
Syntactic procedure to determine conventional data sampling frequency
Iterative procedure to determine appropriate event segmentation set
Event sample by capturing the specific features of interest and elapsed time.
Figure
The AEG scans the continuous signal
Comparison of conventional digital signal processing versus event-driven signal processing.
Index-based feature table including attributes and elapsed time range.
The signal representation by a set of atomic events with a certain amount of error is denoted in the following equation:
The modified architecture of the proposed MCU includes S2E to extract atomic event
The sensor signal in rare-event applications is described with an example in Figure
The path from S2E to the event tracer is designed with the event bus, on which the atomic event transactions are loaded. The predefined event types are configured in EPU configuration by the user knowing the signal characteristics for which attributes are represented. The EPU handles the index to the events in the event table, which is stored in the EPU configuration. Figure
Figure
Implemented circuit and experimental results.
S2E circuit data path
Modified microcontroller bus architecture
Measurement environment
Energy consumption according to event quantization error
The hardware implementation based on the proposed concept requires the additional 7500 NAND gates and 1 KB SRAM tracer in 0.18 um CMOS process. The implemented designs are integrated in an 8051-based microcontroller. Figure
For power consumption measurement, the raw dump of the electrical signal generated by hand gesture is gathered into the host computer, as shown in Figure
Figure
The elapsed time resolution for the time quantization reduces directly the power consumption, which is constantly required to monitor the incoming signal shape. Trade-off between the time quantization error and the power consumption reduction is performed to determine the error bound allowing the appropriate signal detection. The event segmentation size also affects the power consumption reduction slightly, which is showed with an example of 168 events and 104 events in Figure
The macrolevel signal processing concept is based on the early evaluation of incoming sensor signal data by the S2E. The signal-specific signal segmentation with the features of interest enables the atomic event extraction from the continuous sensor data signal. The early evaluation of the signal features enables the entire system in sleep mode, with the exception of the S2E, to consume relatively little current. The extracted small number of atomic events is analyzed by the EPU, which will traverse the reduced state space. The proposed method requires the additional hardware by modifying the conventional MCU bus architecture and the user must perform the iterative configuration on the S2E and EPU carefully after analyzing the signal characteristics for rare-event activity-sensing applications until the reasonable power reduction is accomplished. The event-space representation and signal abstraction of atomic events extracted by S2E could reduce the data processing cost in terms of the energy consumption by considering specific characteristics of signals observed in rare-event sensing applications. The experimental result shows that the proposed method is an effective way to provide the power reduction.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A6A3A04059410), the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the C-ITRC (Convergence Information Technology Research Center) support program (NIPA-2014-H0401-14-1004) supervised by the NIPA (National IT Industry Promotion Agency), and the 2013 Yeungnam University Research Grant.