So this is how it all turns out? You're the hero, I'm left down I should've known you couldn't stand Up for me and be a man. I still have dreams of you at night I can't tell the dark from light I never thought I'd be The one you'd leave behind. But tonight I need you to save me I'm too close to breaking, I see the light I am standing on the edge of my life. And I wonder if you ever cared at all. I am standing on the edge of my life Standing on the edge of my life I am standing on the edge of my life. Compartilhar no Facebook Compartilhar no Twitter.
The Edge Tonight Alive. You said you'd stay, I said I'd wait All those words were spoke in vain I still recall the bitter taste I guess some things never change And then I think of yesterday And every promise that you made I never thought I'd be The one that you would break But I will fight until the day the world stops turning And they will fall to ashes, I will just keep burning But tonight I need you to save me I'm too close to breaking, I see the light I am standing on the edge of my life Standing on the edge of my life Standing on the edge So this is how it all turns out?
You're the hero, I'm left down I should've known you couldn't stand Up for me and be a man I still have dreams of you at night I can't tell the dark from light I never thought I'd be The one you'd leave behind But I will fight until the day the world stops turning And they will fall to ashes, I will just keep burning But tonight I need you to save me I'm too close to breaking, I see the light I am standing on the edge of my life We've been stuck I hold my breath And I'm tangled in your spiderwebs I choke How could I fall?
BEYOND THE EDGE
And I wonder if you ever cared at all But I will fight until the day the world stops turning And they will fall to ashes, I will just keep burning But tonight I need you to save me I'm too close to breaking, I see the light I am standing on the edge of my life I am standing on the edge of my life Standing on the edge of my life I am standing on the edge of my life. Envie pra gente. Recomendar Twitter. Playlists relacionadas. Mais acessados.
Similarly, data may be available for transmission only upon request. Energy efficient ultralow power ULP processing should also be a key aspect of any edge node implementation. The pioneer days of the industrial IoT and its precursor, machine-to-machine M2M communication, were largely defined by the role of cloud platforms as the primary application enablers.
Intelligent systems have historically relied only upon cloud level capability for their insight. The actual edge sensor devices had been relatively unsophisticated. However, this old premise is currently being shaken up as low power computing capabilities at the edge node advance at a faster rate than those at the cloud. There is a smart partition paradigm shift underway from the connected sensor model to the intelligent device model. This is providing more available architecture choices and allowing organizations deploying the industrial IoT to enhance their physical assets and processes in unique ways.
Edge computing analytics, also known as edge intelligence or interpretation, is driving this shift. Mass industrial IoT deployments rely on the availability of a diverse set of intelligent nodes that are secure, highly energy efficient, and easy to manage. Edge sensor devices may be constrained by energy, bandwidth, or raw computational power.
These constraints propagate to protocol choices that can cut IP stacks down to minimal flash memory or RAM. This can make it challenging to program and there can be some sacrifice of the IP benefit.
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Edge processing can be an analytic proposition as an approach to analyze data close to its source in addition to sending it to a remote server for cloud-level analysis. Moving the real-time analytics edge processing as early as possible in the signal chain reduces the payload burden down-stream and shortens latency.
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If the initial data processing can otherwise be done at the edge node, this will simplify the required data formatting, communications bandwidth, and eventual aggregation at the gateway to the cloud. Time sensitive feedback loops through close coupling to the sensor can provide immediate processing that provide for a more valuable informed decision. However, this requires advance intelligence about what specific information is valuable to expect from the sensed and measured data.
It may also vary from edge node to edge node due to spatial separation or application differences. Event alerts, triggers, and interrupt detection can ignore a majority of the data to transmit only what is necessary. The time value of money is the idea that a dollar today is worth more than a dollar sometime in the future. Analogously, there is a time constant for data. The time value of data means that the data you have sensed in this fractional second will not mean as much in a week, day, or even hour from now. Excellent mission critical IoT examples of this are heat surge sensing, gas leak detection, or sensing catastrophic machinery failure that requires immediate action.
Time sensitive data value decay starts at the point of interpretation.
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The longer the latency to effectively interpret your data and take action, the less valuable the decision will be. In order to solve the temporal depreciation riddle within the industrial IoT, we must gain insights further ahead in the signal chain. Processing algorithms within the edge sensor node can be used that filter, decimate, tune, and refine the sampled data down to the minimum required subset.
This requires first to define the narrow data of interest. Adjustable bandwidth, sample rate, and dynamic range help establish this baseline in the analog domain of the hardware at the onset. By using the required analog settings, the sensor will target only the needed information and provide a shorter time constant to quality interpreted data. Digital postprocessing at the edge can further focus the data of interest.
Frequency analysis of the data at the edge sensor can make early decisions about signal content before the information leaves the node. Performing fast Fourier transforms FFTs , finite impulse response FIR filtering, and using intelligent decimation are some high order computational blocks that narrow the scope of the sampled data. In some cases, only an incremental breadcrumb of pass or fail information is needed to be transmitted out of the edge sensor node after dramatically reducing the full bandwidth of data.
In Figure 1, we can see that without a front-end analog filter or a digital postprocessing filter, a simple signal with decimation by 8 left will alias new unwanted signals center to frequency fold into the new desired signal band right. Digital postprocessing, with a digital signal processor DSP or microcontroller unit MCU , using a half-band FIR low-pass filter as a companion to decimation, will help prevent this issue by filtering the interfering aliased signals. A leading industrial IoT application is a solution for factory machine condition monitoring.
The intent of the solution is to identify and predict machine performance issues in advance of failure. At the edge sensor node, a multiaxis high dynamic range accelerometer monitors vibration displacement at various locations on industrial machines. The raw data can be filtered and decimated for frequency domain interpretation within a microcontroller unit.
An FFT compared against known performance limits can be processed for testing against pass, fail, and warning alerts downstream. Processing gain within the FFT can be achieved through FIR filtering to remove wideband noise that is otherwise outside the bandwidth of interest. The edge node processing is an important component in machine condition monitoring. The full bandwidth of sampled data can provide a significant bottleneck for the aggregation at the wireless gateway. Consider that a single machine may have many sensors and hundreds of machines may be monitored concurrently.
The filtering and intelligent decision making within the microcontroller unit offers a low bandwidth output to the wireless transceiver without the need for intensive filter processing at the cloud. Figure 2 shows a signal chain for machine condition monitoring where an accelerometer sensor measures a displacement vibration signature. With postprocessing at the edge sensor node, frequency analysis can be done within a narrow bandwidth of interest by filtering and decimating the sampled data ahead of FFT computation.
During FFT computation, similar to a real-time oscilloscope, the processing can be blind to new time domain activity until the FFT is complete. An alternate time domain path in a second thread may also be used to prevent gaps in the data analysis. If mechanical signature frequencies of interest are known precisely, the sample rate of the ADC and FFT size within the microcontroller unit can be planned such that the maximum amount of energy falls within the width of a single histogram bin.
This will prevent the signal power from leaking across multiple bins, diluting the precision of the amplitude measurement. Figure 3 provides an example of an FFT where specific predetermined zones are interpreted within the edge node MCU for more than one observed mechanical component.
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Bin energy that peaks within the required green zone represents satisfactory operation, while the yellow and red zones indicate warning and critical alarms respectively. Instead of transmitting the full sensor bandwidth, a lower data rate alarm or trigger breadcrumb can alert the system of an excursion event within the zones of interest.
There are several choices available for the computational horsepower of the edge analytics.
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Many options are available for processing algorithms, from a simplistic MCU that provide limited controls, more complex MCUs that are a sophisticated system on a chip SoC , to powerful multicore digital signal processing. The processing core size, a single- or dual-core operation, instructional RAM cache size, and fixed vs. Often there is a trade-off between the power budget available on the node and the computational requirements of the application.
For digital signal processing, two categories designate the notation format used to store and manipulate numeric representations of the sensor node data: fixed point and floating point. Fixed point refers to the manner in which numbers are represented with a fixed number of digits after and sometimes before the decimal point.
DSPs using this method process integers such as positive and negative whole numbers using a minimum of 16 bits with possible bit patterns. In comparison, floating point uses rational numbers with a minimum of possible patterns. Floating-point processing assures that a much larger dynamic range of numbers can be represented. This is important if large sets of sensor node data are to be computed where the exact range may be unknown in advance of sensing.
Additionally, since every new computation requires a mathematical calculation, rounding or truncating is an inherent result. This creates quantization errors, or digital signal noise, within the data. A quantization error is the difference between an ideal analog value and its digital representation that is the nearest rounded value. The larger the quantization gap between these values, the more pronounced the digital noise will be.
Floating-point processing yields greater precision than fixed-point processing when accuracy and precision are important to the interpreted sensor data. Firmware designers should implement a computation application with the greatest efficiency, as the speed with which operations execute is critical. Therefore, it is important to delineate the processing requirements for data interpretation to determine whether fixed or floating-point computations are required for maximum efficiency. It is possible to program a fixed-point processor to perform floating-point tasks and vice-versa.
However, this is highly inefficient and will impact processing performance and power. Fixed-point processors shine where they are optimized for higher volume general-purpose applications that do not need intensive computation algorithms.
Floating-point processors conversely can leverage specialized algorithms for ease of development and greater overall precision. While not high in performance, the number of supported GPIO pins within the processor can provide a secondary selection criteria. The core processing clock speed, number of bits per execution, amount of embedded instruction RAM available for processing, and memory interface speed will all impact the capability of the edge node processing.
Real-time clocks help time-stamp data and allow alignment of processing across multiple platforms.