## 4 Results and Discussion
### Dataset
#### 4.2.2 Audio Fingerprint Matching  
To carry out the audio fingerprint matching, an audio fingerprint was computed both for each recorded audio and original environmental sounds. All recorded audios are 10 s long, therefore, each recorded audio fingerprint is 66 subfingerprints long. Thus, the vectors used in Equation (1) are 2112 (66 \(\times\) 32) bits length. Afterwards, Algorithm 1 was used to evaluate the shortest Hamming distance between all original environmental sounds and each recorded audio. The shortest distance identifies and matches the recorded audio with the original sound.  
Table 3 shows the results returned by Algorithm 1 when it was evaluated for each recorded audio. For each one the shortest Hamming Distance is bold marked. As can be seen, most of recorded audios match with its correspondent original sound, yielding an 85.4% accuracy, and there is not dependency with the kind of sound, indoor or outdoor. Furthermore, a 91.6% accuracy is reached when the three shortest Hamming distances are used, that is, a recorded audio is correctly classified and found in a set of three sounds with a 91.6% probability. On the other hand, all S10 recordings were classified as S3 original audio. It can be because doppler effect is perceived in both audios. In any case, the aim of this experiment is not to implement a robust classification system, but to demonstrate the validity of the proposed acoustic sensor. Taking in account the results, it can be asserted that the recorded audios have a high grade of similarity with its original sound, and hence, the proposed acoustic sensor can be validated.  
Wi-Fi connection. Audio recording was carried out in an infinite loop using a 10 kHz sampling frequency and 12-bit resolution. The ArduinoFFT library [33] was used to implement the FFT, and it was computed each 12.8 ms, that is, every FFT was run after 128 new samples were recorded. UDP datagram sending was carried out each 25.6 ms, therefore, each UDP datagram is sent when 256 new samples are gathered. The prototype sensor was powered by 5 V, and the current consumption was measurement for each experiment. Table 4 shows the results. As can be seen, the A and B experiments have similar energy consumption because all operations are carried out in an infinite loop and additional resources are not used, only the processor. However, in the C experiment, Wi-Fi module is periodiocally transmitting a datagram, and therefore, the energy consumption is higher. In any case, the maximun energy consumption is about 0.8 W, and the proposed sensor could be powered by battery for a long time.  
On the other hand, audio processing capabilities were evaluated implementing the FFT with different number of samples and carrying out an audio recording using the same core. FFT was chosen because is an expensive computational algorithm in audio processing. Each experiment was performed 1000 times and the average execution time was computed. Table 5 shows the average execution time for each experiment. As can be seen, the execution time increases with the number of samples. In addition, using the same number of samples, the FFT execution time is slightly higher when the simultaneous sampling is carried out, that is, the FFT was computed while other samples were gathered. Nevertheless, about 20 ms is only spent to compute a 512 sample FFT. Hence, audio processing capabilities could be performed without compromising the sample rate and the energy consumption, and edge computing paradigm can be implemented in the proposed sensor.