## 5 Conclusions and Future Work  
As was discussed in this paper, in recent years, many authors have experienced a growing interest in remote monitoring of older people, and several systems have been proposed in the literature. These systems are commonly termed as AAL systems, but the acoustic sensors were not designed with low cost and audio processing requisites. In this paper, we described the design of a low cost wireless acoustic sensor for AAL systems based on ESP32 board. In order to choose the best platform, three different low cost microcontroller boards were evaluated. It was given a detailed description of the hardware and the principle of operation of software implementation. The proposed sensor is capable of recording ambient sounds at least to 10 kHz sampling frequency and 12-bit resolution. Furthermore, the sensor board has computation capabilities to carry out audio signal processing and network communications without compromising the sample rate and the energy consumption. Hence, the proposed sensor can improve AAL solutions carrying out the audio identification for  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Experiment** & **Average Current (mA)** & **Energy Consumption (W)** \\ \hline A—Audio recording & 139 & 0.695 \\ B—Audio recording and FFT & 141 & 0.705 \\ C—Audio recording and UDP sending & 165 & 0.825 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy consumption.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Samples** & **Simultaneous Sampling** & **Average Execution Time (ms)** \\ \hline
128 & No & 3.32 \\
128 & Yes & 4.45 \\
256 & No & 7.04 \\
256 & Yes & 9.43 \\
512 & No & 14.92 \\
512 & Yes & 19.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fast Fourier Transform execution time.  
monitoring of activity and health, and the detection of distress situations at the edge of WASN. Thus, a shorter response time and better reliability is ensured enhancing quality of life and safety of older people. The acoustic sensor is very small in size, and therefore is very useful to be used in a discrete way for personal healthcare in AAL systems, and the cost of hardware platform is very competitive. The experiments on the proposed system showed that the system worked well. System validation is demonstrated by experimental results, which were statistically obtained analysing several tests of randomness and audio classification. Furthermore, evaluations of energy consumption and audio processing capabilities were carried out demonstrating the usefulness and low power, and that edge computing paradigm can be implemented at the proposed sensor.  
In our ongoing work, we are planning to design a better sound classification system based on audio fingerprint to be implemented at each acoustic sensor. Moreover, we are also planning to deploy a WASN using the proposed acoustic sensor in this paper to evaluate the whole system and network performance.  
[PERSON] designed and implemented the prototype of wireless acoustic sensor, and wrote part of manuscript. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments, analized the data, and wrote the rest of the manuscript. All authors have reviewed and approved the final manuscript.  
The authors declare no conflict of interest.