If big data is the new oil, as it has often been said, then Fotech offers the right tools to dig for more of it and feed the insatiable needs of tomorrow’s smart cities. eeNews Europe caught up with Fotech’s product line director Stuart Large about what the company considers the untapped potential of distributed acoustic sensing (DAS) for smart cities.
An advanced variant of telecom’s traditional Optical Time Domain Reflectometers (OTDR), distributed acoustic sensing relies on the analysis of the coherent Rayleigh backscatter noise signature in a fibre optic cable as pulsed light is sent into the fibre. DAS is optimised to measure small changes in the coherent Rayleigh noise structure that occurs from pulse to pulse. Since the coherent Rayleigh noise structure is generated interferometrically within the fibre by the relative locations and strengths of local scattering centres intrinsic to the structure of the glass, even very small physical (acoustical or vibrational) disturbances at a point in the fibre can make detectable changes in the interferometric signal.
Of course, special algorithms and processing nodes must be put in place to make sense of such faint changes, but the beauty of DAS is that it operates with standard telecommunications grade optical fibres, either tapping into existing telecoms infrastructures or into new purpose-laid monitoring fibres, effectively turning these fibres into a linear array of discrete vibration sensors.
DAS goes way beyond what traditional OTDRs can, stresses Large. “A telecom OTDR merely tells you what splices you have along the fibre, what connections you have and the quality of these splices, it doesn’t provide sensing. That same fibre can send vibrations from cars travelling along the road, from which can be derived the flow rate, the velocity, the number of cars and possible accidents. DAS is a far more sophisticated OTDR. There are about 20 different companies stating they have DAS, but only a small bunch of them has the software to actually extract meaningful information from the collected data”.