Abdellah Touhafi

Sensor Systems Based on FPGAs and Their Applications: A Survey

In this manuscript, we present a survey of designs and implementations of research sensor nodes that rely on FPGAs, either based upon standalone platforms or as a combination of microcontroller and FPGA. Several current challenges in sensor networks are distinguished and linked to the features of modern FPGAs. As it turns out, low-power optimized FPGAs are able to enhance the computation of several types of algorithms in terms of speed and power consumption in comparison to microcontrollers of commercial sensor nodes. We show that architectures based on the combination of microcontrollers and FPGA can play a key role in the future of sensor networks, in fields where processing capabilities such as strong cryptography, self-testing and data compression, among others, are paramount.

Correlation analysis of noise and ultrafine particle counts in a street canyon

Ultrafine particles (UFP, diameter<100 nm) are very likely to negatively affect human health, as underlined by some epidemiological studies. Unfortunately, further investigation and monitoring are hindered by the high cost involved in measuring these UFP. Therefore we investigated the possibility to correlate UFP counts with data coming from low-cost sensors, most notably noise sensors. Analyses are based on an experiment where UFP counts, noise levels, traffic counts, nitrogen oxide (NO, NO(2) and their combination NO(x)) concentrations, and meteorological data were collected simultaneously in a street canyon with a traffic intensity of 3200 vehicles/day, over a 3-week period during summer. Previous reports that NO(x) concentrations could be used as a proxy to UFP monitoring were verified in our setup. Traffic intensity or noise level data were found to correlate with UFP to a lesser degree than NO(x) did. This can be explained by the important influence of meteorological conditions (mainly wind and humidity), influencing UFP dynamics. Although correlations remain moderate, sound levels are more correlated to UFP in the 20-30 nm range. The particles in this size range have indeed rather short atmospheric residence times, and are thus more closely short-term traffic-related. Finally, the UFP estimates were significantly improved by grouping data with similar relative humidity and wind conditions. By doing this, we were able to devise noise indicators that correlate moderately with total particle counts, reaching a Spearman correlation of R=0.62. Prediction with noise indicators is even comparable to the more-expensive-to-measure NO(x) for the smallest UFP, showing the potential of using microphones to estimate UFP counts.

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.

Using sound-taste correspondences to enhance the subjective value of tasting experiences

The soundscapes of those places where we eat and drink can influence our perception of taste. Here, we investigated whether contextual sound would enhance the subjective value of a tasting experience. The customers in a chocolate shop were invited to take part in an experiment in which they had to evaluate a chocolate’s taste while listening to an auditory stimulus. Four different conditions were presented in a between-participants design. Envisioning a more ecological approach, a pre-recorded piece of popular music and the shop’s own soundscape were used as the sonic stimuli. The results revealed that not only did the customers report having a significantly better tasting experience when the sounds were presented as part of the food’s identity, but they were also willing to pay significantly more for the experience. The method outlined here paves a new approach to dealing with the design of multisensory tasting experiences, and gastronomic situations.

Performance modeling for FPGAs: extending the roofline model with high-level synthesis tools

The potential of FPGAs as accelerators for high-performance computing applications is very large, but many factors are involved in their performance. The design for FPGAs and the selection of the proper optimizations when mapping computations to FPGAs lead to prohibitively long developing time. Alternatives are the high-level synthesis (HLS) tools, which promise a fast design space exploration due to design at high-level or analytical performance models which provide realistic performance expectations, potential impediments to performance, and optimization guidelines. In this paper we propose the combination of both, in order to construct a performance model for FPGAs which is able to visually condense all the helpful information for the designer. Our proposed model extends the roofline model, by considering the resource consumption and the parameters used in the HLS tools, to maximize the performance and the resource utilization within the area of the FPGA. The proposed model is applied to optimize the design exploration of a class of window-based image processing applications using two different HLS tools. The results show the accuracy of the model as well as its flexibility to be combined with any HLS tool.

Ultrasonic multiple-access ranging system using spread spectrum and MEMS technology for indoor localization.

Indoor localization of persons and objects poses a great engineering challenge. Previously developed localization systems demonstrate the use of wideband techniques in ultrasound ranging systems. Direct sequence and frequency hopping spread spectrum ultrasound signals have been proven to achieve a high level of accuracy. A novel ranging method using the frequency hopping spread spectrum with finite impulse response filtering will be investigated and compared against the direct sequence spread spectrum. In the first setup, distances are estimated in a single-access environment, while in the second setup, two senders and one receiver are used. During the experiments, the micro-electromechanical systems are used as ultrasonic sensors, while the senders were implemented using field programmable gate arrays. Results show that in a single-access environment, the direct sequence spread spectrum method offers slightly better accuracy and precision performance compared to the frequency hopping spread spectrum. When two senders are used, measurements point out that the frequency hopping spread spectrum is more robust to near-far effects than the direct sequence spread spectrum.

Comparative performance study of RPL in Wireless Sensor Networks

In order to enable the interoperability of the IP-based WSNs with the Internet, the Internet Engineering Task Force (IETF) chartered the IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) and Routing Over Low power and Lossy networks (RoLL) working groups. The working group has been standardizing an IPv6 routing protocol for Low-power and Lossy Networks (RPL). This paper presents a comprehensive study of the performance of RPL when compared with Collection Tree Protocol (CTP). The aim of this work is to show the superiority of RPL in network scalability. Our results describe the benefit of RPL in terms of high Packet Reception Ratio (PRR) and low energy consumption. We also highlight the advantages in the design of the protocol, which can be used in further investigation of RPL.

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.