Samuel Dauwe

Sampling approaches to predict urban street noise levels using fixed and temporary microphones

Requirements for static (prediction of L(den) and diurnal averaged noise pattern) and dynamic (prediction of 15 min and 60 min evolution of L(Aeq) and statistical levels L(A90,)L(A50) and L(A10)) noise level monitoring are investigated in this paper. Noise levels are measured for 72 consecutive days at 5 neighboring streets in an inner-city noise measurement network in Gent, Flanders, Belgium. We present a method to make predictions based on a fixed monitoring station, combined with short-term sampling at temporary stations. It is shown that relying on a fixed station improves the estimation of L(den) at other locations, and allows for the reduction of the number of samples needed and their duration; L(den) is estimated with an error that does not exceed 1.5 dB(A) to 3.4 dB(A) according to the location, for 90% of the 3 × 15 min samples. Also the diurnal averaged noise pattern can be estimated with a good accuracy in this way. It was shown that there is an optimal location for the fixed station which can be found by short-term measurements only. Short-term level predictions were shown to be more difficult; 7 day samples were needed to build models able to estimate the evolution of L(Aeq,60min) with a RMSE ranging between 1.4 dB(A) and 3.7 dB(A). These higher values can be explained by the very pronounced short-term variations appearing in typical streets, which are not correlated between locations. On the other hand, moderately accurate predictions can be achieved, even based on short-term sampling (a 3 × 15 minute sampling duration seems to be sufficient for many of the accuracy goals set related to static and dynamic monitoring). Finally, the method proposed also allows for the prediction of the evolution of statistical indicators.

Novel Applications Integrate Location and Context Information

New applications and services aim to adapt themselves to the users context and thus require platforms that can collect, distribute, and exchange contextual information. The Context-Aware Service Platform (CASP) can help, as exemplified here in three different use cases.

Interest based selection of user generated content for rich communication services

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval. On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the users personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience. In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the users behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories. In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the users interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.

Multiagent-Based Data Fusion in Environmental Monitoring Networks

Advances in embedded systems and mobile communication have led to the emergence of smaller, cheaper, and more intelligent sensing units. As of today, these devices have been used in many sensor network applications focused at monitoring environmental parameters in areas with relative large geographical extent. However, in many of these applications, management is often centralized and hierarchical. This approach imposes some major challenges in the context of large-scale and highly distributed sensor networks. In this paper, we present a multilayered, middleware platform for sensor networks offering transparent data aggregation, control, and management mechanisms to the application developer. Furthermore, we propose the use of multiagent systems (MASs) to create a computing environment capable of managing and optimizing tasks autonomously. In order to ensure the scalability of the distributed data fusion, we propose a three-step procedure to balance the workload among machines using mobile agent technology.

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.

Interest Based Selection of User Generated Content for Rich Multimedia Services

In view of the overwhelming popularity of user generated content, both in terms of production and consumption, new intelligent services are needed to help users finding the content they need and enhance existing services with suitably selected content. In this paper we present a set of algorithms for retrieving content, based on dynamic user profiles and learning capabilities (e.g. based on user feedback). The profile information is used in content searches as well as for assisting the user input analysis process (i.e. speech recognition). To illustrate the approach taken, a rich communication service is presented. Here, the basic service (i.e. voice/video conferencing) is enhanced by showing pictures in real time to the users based on the topic of their conversation and their specific interests.

Efficient Management of User Interests for Personalized Communication Services

Nowadays, there is a lot of interest in context- aware services, especially those services taking into account user interests. An important example is the automated filtering of the overwhelming amount of information available to the user. There is a clear need for the automated capturing of user interests without explicit user interaction. In this paper we present a set of algorithms for the management of user interests. This information is used for updating a tree structure, with added weight values, representing user interests. To illustrate the approach taken, we detail the use case of an instant messaging communication service which updates the user profiles and searches for content matching the topic of the ongoing conversation and the specific interests of the user. The retrieved content is recommended to the user, who is then able to provide feedback by selecting the content he prefers. The gain of the detailed approach is illustrated by means of simulation results, taking real-time constraints into account.