MONICA in Hamburg: Towards Large-Scale IoT Deployments in a Smart City

Modern cities and metropolitan areas all over the world face new management challenges in the 21st century primarily due to increasing demands on living standards by the urban population. These challenges range from climate change, pollution, transportation, and citizen engagement, to urban planning, and security threats. The primary goal of a Smart City is to counteract these problems and mitigate their effects by means of modern ICT to improve urban administration and infrastructure. Key ideas are to utilise network communication to inter-connect public authorities; but also to deploy and integrate numerous sensors and actuators throughout the city infrastructure - which is also widely known as the Internet of Things (IoT). Thus, IoT technologies will be an integral part and key enabler to achieve many objectives of the Smart City vision. The contributions of this paper are as follows. We first examine a number of IoT platforms, technologies and network standards that can help to foster a Smart City environment. Second, we introduce the EU project MONICA which aims for demonstration of large-scale IoT deployments at public, inner-city events and give an overview on its IoT platform architecture. And third, we provide a case-study report on SmartCity activities by the City of Hamburg and provide insights on recent (on-going) field tests of a vertically integrated, end-to-end IoT sensor application.Comment: 6 page

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

NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping

In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

On the Application of the Raspberry Pi as an Advanced Acoustic Sensor Network for Noise Monitoring

The concept of Smart Cities and the monitoring of environmental parameters is an area of research that has attracted scientific attention during the last decade. These environmental parameters are well-known as important factors in their affection towards people. Massive monitoring of this kind of parameters in cities is an expensive and complex task. Recent technologies of low-cost computing and low-power devices have opened researchers to a wide and more accessible research field, developing monitoring devices for deploying Wireless Sensor Networks. Gathering information from them, improved urban plans could be carried out and the information could help citizens. In this work, the prototyping of a low-cost acoustic sensor based on the Raspberry Pi platform for its use in the analysis of the sound field is described. The device is also connected to the cloud to share results in real time. The computation resources of the Raspberry Pi allow treating high quality audio for calculating acoustic parameters. A pilot test was carried out with the installation of two acoustic devices in the refurbishment works of a neighbourhood. In this deployment, the evaluation of these devices through long-term measurements was carried out, obtaining several acoustic parameters in real time for its broadcasting and study. This test has shown the Raspberry Pi as a powerful and affordable computing core of a low-cost device, but also the pilot test has served as a query tool for the inhabitants of the neighbourhood to be more aware about the noise in their own place of residence.Ingeniería, Industria y Construcció