This position paper advocates for digital sobriety in the design and usage of wireless acoustic sensors. As of today, these devices all rely on batteries, which are either recharged by a human operator or via solar panels. Yet, batteries contain chemical pollutants and have a shorter lifespan than electronic components: as such, they hinder the autonomy and sustainability of the Internet of Sounds at large. Against this problem, our radical answer is to avoid the use of batteries altogether; and instead, to harvest ambient energy in real time and store it in a supercapacitor allowing a few minutes of operation. We show the inherent limitations of battery-dependent technologies for acoustic sensing. Then, we describe how a lowcost Micro-Controller Unit (MCU) could serve for audio acquisition and feature extraction on the edge. In particular, we stress the advantage of storing intermediate computations in ferroelectric random-access memory (FeRAM), which is nonvolatile, fast, endurant and consumes little. As a proof of concept, we present a simple-minded detector of sine tones in background noise, which relies on a fixed-point implementation of the fast Fourier transform (FFT). We outline future directions towards bioacoustic event detection and urban acoustic monitoring without batteries nor wires. CCS CONCEPTS• Hardware → Sound-based input / output; Digital signal processing; Power estimation and optimization; Impact on the environment; • Computer systems organization → Real-time systems; • Networks → Sensor networks.
The rat race between user-generated data and data-processing systems is currently won by data. The increased use of machine learning leads to further increase in processing requirements, while data volume keeps growing. To win the race, machine learning needs to be applied to the data as it goes through the network. In-network classification of data can reduce the load on servers, reduce response time and increase scalability.In this paper, we introduce IIsy, implementing machine learning classification models in a hybrid fashion using offthe-shelf network devices. IIsy targets three main challenges of in-network classification: (i) mapping classification models to network devices (ii) extracting the required features and (iii) addressing resource and functionality constraints. IIsy supports a range of traditional and ensemble machine learning models, scaling independently of the number of stages in a switch pipeline. Moreover, we demonstrate the use of IIsy for hybrid classification, where a small model is implemented on a switch and a large model at the backend, achieving near optimal classification results , while significantly reducing latency and load on the servers.
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