Many commercial platforms for fast prototyping have gained support for lpwan technologies. However, these solutions do not meet the low-cost and low-power requirements for a large-scale distribution of battery-powered sensor nodes. This paper presents the design, realization and validation of an open-source lpwan versatile platform. Energy and cost are considered key constraints for this hardware design. A power-efficient LoRa radio interface is implemented by hosting MAC functionality on the application microcontroller, eliminating the need for a modem. In the system architecture, power and cost savings are obtained by omitting and controlling lossy power circuitry. The resulting platform allows entry-level prototyping, while featuring an ultra-low sleep power of $25.2$ $\mathsf{\mu }\mathrm{W}$ . This makes lpwan sensor applications accessible in domains that would otherwise require custom hardware development. The proposed design is validated by an illustrative but functional example of sensor nodes deployed in the field.

In this article we present the design of a low-power MEMS microphone array for wireless sensors. The array is used as part of a device that performs acoustic Angle-Of-Arrival (AOA) measurements. The power consumption of this design is lower than that of comparable designs. Each of the 4 analog channels (each with a microphone and amplifier) can be turned on or off (standby) separately. The power consumption is 1.8 mW per channel, and about 0.13 μW in standby. For a single AOA detection cycle (array activation, audio sampling, AOA computation), the energy consumption is 6.02 mJ. When consecutive AOA detections are performed, the energy cost per detection converges to 3.20 mJ. The AOA accuracy corresponds with the expectations. With a mean error of 4°, this is lower than that of comparable designs.

Wireless acuusuc sensor networks (wasns) are a promising technology for performing acoustic surveillance because of their flexibility and low cost. However, their commercialization is nowadays limited due to their high energy consumption, which is mainly a result of the high data rates required to stream audio data between sensor nodes. In order to improve energy efficiency in WASNs, we explore the benefits of introducing capabilities to reconfigure and collaborate. More specifically we consider an application for localizing and recording sound events in a warehouse environment. For studying this, we introduce an energy consumption model for a WASN node, which includes the cost of sensing, processing and wireless communications. Presented results show that the centralized features allow important energy savings, while maintaining a sufficient Quality of Service. For the considered setup, our simulations show that a centralized approach saves 22% of energy. Furthermore, by using a smart activation of nodes, the energy consumed by all the nodes can be balanced, resulting in a longer network lifetime.

Advances in energy efficient electronic components create new opportunities for wireless acoustic sensor networks. Such sensors can be deployed to localize unwanted and unexpected sound events in surveillance applications, home assisted living, etc. This research focused on a wireless acoustic sensor network with low-profile low-power linear MEMS microphone arrays, enabling the retrieval of angular information of sound events. The angular information was wirelessly transmitted to a central server, which estimated the location of the sound event. Common angle-of-arrival localization approaches use triangulation, however this article presents a way of using angular probability density functions combined with a matching algorithm to localize sound events. First, two computationally efficient delay-based angle-of-arrival calculation methods were investigated. The matching algorithm is described and compared to a common triangulation approach. The two localization algorithms were experimentally evaluated in a $4.25$ m by $9.20$ m room, localizing white noise and vocal sounds. The results demonstrate the superior accuracy of the proposed matching algorithm over a common triangulation approach. When localizing a white noise source, an accuracy improvement of up to 114% was achieved.

Indoor positioning systems are mostly supported by Radio Frequency (RF) technologies, due to the omnipresence of wireless communication infrastructure and handsets. Most of these localization systems rely on a Line Of Sight (LOS) connection to calculate a position based on signal strength, delay or direction. However, indoor environments are characterized by Non Line Of Sight (NLOS) connections and multipath effects such as reflections, scattering, diffraction and refraction. Multipath components are generally considered as undesirable and therefore most techniques focus on mitigation. This research applies an Angle of Arrival (AoA) multipath assistance approach, using ray tracing in a known environment to exploit signal reflections. Because the complete propagation channel is taken into account, a mobile node can already be localized by a single fixed antenna array. In order to assess the performance of the system, LOS and NLOS experiments are performed in three indoor environments in the 2.4 GHz and 5 GHz frequency bands. All measurements are processed by the multipath assisted AoA method, as well as a standard triangulation approach. These tests indicate the superior accuracy of the multipath assisted method, especially in NLOS conditions. Also, the tests demonstrate that the systems performs significantly better at 2.4 GHz than at 5 GHz. Finally, the performance of the presented system is compared to results that can be found in related work.