Issue link: https://te.mouser.com/i/1349224
TE CONNECTIVITY SENSORS /// TREND PAPER THE DRIVE TO GO FROM WIRED TO WIRELESS WIRELESS VIBRATION SENSORS FOR CONDITION MONITORING The Internet of Things (IoT), connecting devices to the Internet so they can be controlled and managed remotely, has dramatically driven improvements in digital radio communications, both the radio hardware and communications protocols. With the rise of smart phones and always-connected tablets and PCs, radio hardware costs have been driven continuously downward. Mobility requirements have demanded ultra-low power radio chipsets, to extend battery life. The sheer volume of data generated from all these devices has demanded efficient, economical use of wireless bandwidth. LoRaWANTM is emerging as the most promising of the low power wide area networks (LPWANs) available. • Utilizes sub-gigahertz unlicensed radio spectrum • Ultra-low power that extends battery life • Long range between sensor and gateway (5 km or greater depending on local conditions) • Flexible deployment and able to penetrate deep in mixed environments • Allows data to be sent asynchronously (only sent when necessary), further extending battery life Many years ago, Gordon Moore famously predicted that performance in digital devices would double approximately every 18 months (known as Moore's Law). This prediction has generally held true, to the point where there is now tremendous computing power in the palm of your hand, or in your wearable device (e.g. smart watch). This has enabled Edge computing; the ability to process data at or near the end of the network (the "edge" of the network), rather than send that data in raw form all the way back to a central station to be processed there. For a wireless vibration sensor, a perhaps obvious application of Edge computing is calculating the FFT (Fast Fourier Transform) of a sampled vibration waveform at the sensor itself. In a conventional system, the raw vibration waveform would be sent to the central station (as an analog signal) and the FFT calculated there. With Edge computing, the FFT can be calculated in the sensor and the processed data sent back. Rather than sending back raw vibration signals, this reduces bandwidth overhead and usage of battery power. But this is only a simple example. Ultimately much more computing could be done at the sensor. Given the appropriate algorithms, the sensor could "learn" about the machine it is installed on and when it is running well and when it is not. The building blocks are in place for a truly smart condition-monitoring vibration sensor. DRIVER 3: THE RISE OF THE INTERNET OF THINGS (IOT) HAS IMPROVED DIGITAL RADIO PERFORMANCE DRIVER 4: EDGE COMPUTING IN IOT DEVICES FURTHER ENHANCES WIRELESS COMMUNICATIONS