Federico Domínguez

SoundCompass: A Distributed MEMS Microphone Array-Based Sensor for Sound Source Localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass’s hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

Wireless sensor networks for environmental research: A survey on limitations and challenges

Wireless Sensor Networks (WSNs) have permeated the entire fabric of academic disciplines that greatly rely on control and decision-making processes such as medical health care, agriculture, ecology and industrial automation. In ecology, an infrastructure consisting of networks of interconnected sensors can provide in-situ high-precision monitoring of natural phenomena at a large-scale. Moreover, WSNs provide a number of advantages to ecoinformatics such as the possibility of constructing quality datasets that coupled to machine learning-based prediction systems, can be used for ecological and process modelling. In this manuscript, we present a survey on recent WSNs based on several fields related to environmental research e.g. ecoinformatics, precision agriculture and wildlife observation. Finally, we have focused on the identification of limitations and open problems therein.

Coexistence with WiFi for a Home Automation ZigBee product

Home Automation (HA) products based on IEEE 802.15.4 and ZigBee, both low power wireless communication standards, are starting to appear on the market. Most of these products use the 2.4GHz Industrial Scientific Medical (ISM) band, sharing the wireless spectrum with several other ubiquitous technologies such as Bluetooth, cordless phones, microwaves ovens and WiFi. The potential for cross technology interference exists and it is a looming threat to the success of new HA products based on the IEEE 802.15.4 standard. We developed a ZigBee Home Automation line of products for the Belgium market and therefore decided to evaluate the robustness of our products in the face of cross technology interference in the crowded 2.4 GHz band. We exposed our product, in a controlled laboratory setting, to increasing levels of WiFi interference and measured its performance. This paper summarizes our findings and presents recommendations and a methodology to measure and avoid WiFi interference while deploying and installing ZigBee based products in a home setting. While ZigBee products can successfully withstand interference from microwave ovens and Bluetooth devices, they are still vulnerable to high load WiFi traffic. ZigBee and WiFi can peacefully coexist in the home environment as long as certain basic precautions are taken.

Towards an Environmental Measurement Cloud: Delivering Pollution Awareness to the Public

Geosensor networks and sensor webs are two technologies widely used for determining our exposure to pollution levels and ensuring that this information is publicly available. However, most of these networks are independent from each other and often designed for specific domains, hindering the integration of sensor data from different sources. We contributed to the integration of several environmental sensor networks in the context of the IDEA project. The objective of this project was to measure noise and air quality pollution levels in urban areas in Belgium using low-cost sensors. This paper presents the IDEA Environmental Measurement Cloud as a proof-of-concept Data-as-a-Service (DaaS) cloud platform that integrates environmental sensor networks with a sensor web. Our DaaS platform implements a federated two-layer architecture to loosely couple together sensor networks deployed over a wide geographical area with web services. It offers several data access, discovery, and visualization services to the public while serving as a scientific tool for noise pollution research. After one year of operation, it hosts approximately 6.5 TB of environmental data and offers to the public near real-time noise pollution measurements from over 40 locations in Belgium.

Multimodal Selfies: Designing a Multimodal Recording Device for Students in Traditional Classrooms

The traditional recording of student interaction in classrooms has raised privacy concerns in both students and academics. However, the same students are happy to share their daily lives through social media. Perception of data ownership is the key factor in this paradox. This article proposes the design of a personal Multimodal Recording Device (MRD) that could capture the actions of its owner during lectures. The MRD would be able to capture close-range video, audio, writing, and other environmental signals. Differently from traditional centralized recording systems, students would have control over their own recorded data. They could decide to share their information in exchange of access to the recordings of the instructor, notes form their classmates, and analysis of, for example, their attention performance. By sharing their data, students participate in the co-creation of enhanced and synchronized course notes that will benefit all the participating students. This work presents details about how such a device could be build from available components. This work also discusses and evaluates the design of such device, including its foreseeable costs, scalability, flexibility, intrusiveness and recording quality.

Migration from a legacy wireless technology to ZigBee for a Home Automation market ready system

In the evolving Home Automation (HA) market, several wireless technologies compete for a place in your home. Technology standards such as Bluetooth, ZigBee, 6LowPan, WirelessHART and others offer hardware solutions to HA products manufacturers. The HA industry faces now important choices and unknowns when migrating their legacy HA systems to these new technologies. We migrated - for a Belgium manufacturer - a wireless window shutter control system to a ZigBee HA system. This paper presents the challenges encountered and experiences obtained during product development, manufacturing, and deployment. Migrating an existing HA product to any technology adds several design constraints to the development cycle of a product. Migrating to ZigBee adds more constraints arising from the incompatibility of the ZigBee specification with the HA manufacturer production, deployment, and marketing practices. Together, these constraints are non-trivial and act as a barrier for the widespread adoption of ZigBee as the standard for Home Automation.

The RAP system: automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors

Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the systems feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.

Active Self-Testing Noise Measurement Sensors for Large-Scale Environmental Sensor Networks

Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive. Several environmental network projects have started using off-the-shelf low-cost microphone sensors to reduce their costs, but these sensors have higher failure rates and produce lower quality data. To offset this disadvantage, we developed a low-cost noise sensor that actively checks its condition and indirectly the integrity of the data it produces. The main design concept is to embed a 13 mm speaker in the noise sensor casing and, by regularly scheduling a frequency sweep, estimate the evolution of the microphones frequency response over time. This paper presents our noise sensors hardware and software design together with the results of a test deployment in a large-scale environmental network in Belgium. Our middle-range-value sensor (around €50) effectively detected all experienced malfunctions, in laboratory tests and outdoor deployments, with a few false positives. Future improvements could further lower the cost of our sensor below €10.

Smart objects in education: An early survey to assess opportunities and challenges

Smart Objects are computationally enhanced versions of everyday objects such as shoes, coffee makers, pens, and more. They have the capacity to process information, sense and act on their environment and interconnect with each other and the Internet, creating an Internet of Things (IoT). IoT and Smart Objects technologies have been adopted in industries such as transportation and healthcare, however few implementations exist in the educational domain. This article presents a survey of Smart Objects applications in learning environments and explores the opportunities that exist in this domain and the main challenges that hinder the adoption of IoT in education.

Service composition with quality of service management in environmental sensor networks

Environmental sensor networks consist of geographically distributed resource-constrained computers that use sensors to measure environmental parameters. They typically collect and distribute environmental data in near real-time using different types of web services. These services are consumed by users and applications that expect appropriate quality of service QoS levels. However, acceptable and stable QoS levels are difficult to maintain in environmental sensor networks due to limited computing resources and hostile deployment conditions. We developed a new approach to efficiently manage QoS levels in these networks using two separate tasks: online QoS estimation and QoS-aware service composition. This paper presents our first prototype of a web service composition system that leverages Bayesian classification techniques to perform online QoS estimation by constantly monitoring key environmental and network performance parameters. This system has been tested in a live noise pollution environmental sensor network deployed in the Flanders region in Belgium.