VECTOR:Velocity Based Temperature-field Monitoring with Distributed Acoustic Devices.144:9 Influence of noise and audibility.Normal noise from indoor environments occupies different frequency band with our system,so the noise will barely affect our system's performance.And our signal is working on 17~21 kHz which is insensitive for most of people and will not affect the daily activities. Blocked LOS path.The LOS path could be blocked in certain extreme cases.We can use paths other than LOS path if the other paths are stable enough,e.g.reflection from a nearby wall.The length of the non-LOS path can be estimated with one extra input of temperature and it's an one-time calibration,see details in Section 5. 3.6 Sound-based Temperature Sensing We use a basic two-device setup to illustrate the fundamentals of sound-based temperature sensing.In this scenario,we separate two devices by a fixed known distance and measure the ToF,ie.,TAB+rBa in Eq.(6),to derive the temperature.The number of full wave cycles can be determined by the coarse-grained cross-correlation and the decimal part of the cycles can be determined by the fine-grained phase measurement.The wavelength Ae can be derived by dividing the known distance by the number of wave cycles.We can then calculate the speed of sound by c =feAc and use Eq.(1)to get the temperature.Note that the distance between devices can either be measured in advance,or determined after deployment by calibrating the ToF under a known temperature. Sound-based temperature sensing is more sensitive than traditional temperature sensors such as thermistors or thermocouples.Acoustic sensing directly measures the temperature of the air,while traditional sensors measure the temperature of the probe that needs to be heated or cooled by surrounding air when the air temperature changes.To evaluate the key features of sound-based temperature sensing,we perform an experiment in a room with controlled temperature changes.Fig.3 shows the measurements of VECTOR and a traditional Bosch BME280 temperature sensor [42]within a period of two hours.We turned on the air conditioner to heat the room at t =500 seconds,turned it off and opened the window at 2760 seconds,and closed the window at 4270 seconds.Note that we carefully avoided direct air flows towards the sound path in these experiments so that these temperature fluctuations are not caused by air flows.We have three key observations on the result shown in Fig.3. For stable environments,the difference between the two temperature measurements is smaller than 0.5C.For example,before we start the air conditioner and after we close the window,the sound-based temperature measurement is stable and very close to the readings of BME280 sensor.Therefore,our system can provide accurate temperature readings that are comparable to commercial sensors. We observe that VECTOR responses to temperature changes much faster than traditional sensors.This can be seen from the differences in the two temperature curves when we turn on/off the heating.For example, when we stopped heating,the measurement of VECTOR drops by 0.5C within 5 seconds,while BME280 takes 30 seconds to detect the same temperature change.The output of sound-based sensing agrees with human perceptions,since human beings could notice temperature change caused by such events within seconds.The low latency feedback provided by VECTOR could potentially improve the performance of control algorithms in HVAC systems.We further study this phenomenon with detailed experiments in Section 6.6. When the temperature is unstable,VECTOR observes larger short-term variance in the measurements.For example,when the air conditioner is on or the window is opening,the sound-based measurements have higher fluctuation than the BME280 sensor's readings as shown in Fig.3.This phenomenon is consistent with our daily experience.When the air conditioner is on,human can perceive the fluctuation of temperatures due to the cool/warm air from the air-conditioner,which is quite different to natural stable environments However,traditional sensors only perceive smooth temperature changes as shown by Fig.3.We can leverage Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.,Vol.6,No.3,Article 144.Publication date:September 2022.VECTOR: Velocity Based Temperature-field Monitoring with Distributed Acoustic Devices • 144:9 • Influence of noise and audibility. Normal noise from indoor environments occupies different frequency band with our system, so the noise will barely affect our system’s performance. And our signal is working on 17 ∼ 21 𝑘𝐻𝑧 which is insensitive for most of people and will not affect the daily activities. • Blocked LOS path. The LOS path could be blocked in certain extreme cases. We can use paths other than LOS path if the other paths are stable enough, e.g.reflection from a nearby wall. The length of the non-LOS path can be estimated with one extra input of temperature and it’s an one-time calibration, see details in Section 5. 3.6 Sound-based Temperature Sensing We use a basic two-device setup to illustrate the fundamentals of sound-based temperature sensing. In this scenario, we separate two devices by a fixed known distance and measure the ToF, i.e., 𝜏𝐴𝐵 + 𝜏𝐵𝐴 in Eq. (6), to derive the temperature. The number of full wave cycles can be determined by the coarse-grained cross-correlation and the decimal part of the cycles can be determined by the fine-grained phase measurement. The wavelength 𝜆𝑐 can be derived by dividing the known distance by the number of wave cycles. We can then calculate the speed of sound by 𝑐 = 𝑓𝑐𝜆𝑐 and use Eq. (1) to get the temperature. Note that the distance between devices can either be measured in advance, or determined after deployment by calibrating the ToF under a known temperature. Sound-based temperature sensing is more sensitive than traditional temperature sensors such as thermistors or thermocouples. Acoustic sensing directly measures the temperature of the air, while traditional sensors measure the temperature of the probe that needs to be heated or cooled by surrounding air when the air temperature changes. To evaluate the key features of sound-based temperature sensing, we perform an experiment in a room with controlled temperature changes. Fig. 3 shows the measurements of VECTOR and a traditional Bosch BME280 temperature sensor [42] within a period of two hours. We turned on the air conditioner to heat the room at 𝑡 = 500 seconds, turned it off and opened the window at 2760 seconds, and closed the window at 4270 seconds. Note that we carefully avoided direct air flows towards the sound path in these experiments so that these temperature fluctuations are not caused by air flows. We have three key observations on the result shown in Fig. 3. • For stable environments, the difference between the two temperature measurements is smaller than 0.5 ◦C. For example, before we start the air conditioner and after we close the window, the sound-based temperature measurement is stable and very close to the readings of BME280 sensor. Therefore, our system can provide accurate temperature readings that are comparable to commercial sensors. • We observe that VECTOR responses to temperature changes much faster than traditional sensors. This can be seen from the differences in the two temperature curves when we turn on/off the heating. For example, when we stopped heating, the measurement of VECTOR drops by 0.5 ◦C within 5 seconds, while BME280 takes 30 seconds to detect the same temperature change. The output of sound-based sensing agrees with human perceptions, since human beings could notice temperature change caused by such events within seconds. The low latency feedback provided by VECTOR could potentially improve the performance of control algorithms in HVAC systems. We further study this phenomenon with detailed experiments in Section 6.6. • When the temperature is unstable, VECTOR observes larger short-term variance in the measurements. For example, when the air conditioner is on or the window is opening, the sound-based measurements have higher fluctuation than the BME280 sensor’s readings as shown in Fig. 3. This phenomenon is consistent with our daily experience. When the air conditioner is on, human can perceive the fluctuation of temperatures due to the cool/warm air from the air-conditioner, which is quite different to natural stable environments. However, traditional sensors only perceive smooth temperature changes as shown by Fig. 3. We can leverage Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 6, No. 3, Article 144. Publication date: September 2022