Section: New Results

Network deployment and characterization

Participants: Ahmed Boubrima, Angelo Furno, Walid Bechkit, Khaled Boussetta, Hervé Rivano, Razvan Stanica.

Deployment of Wireless Sensor Networks for Pollution Monitoring

Monitoring air quality has become a major challenge of modern cities, where the majority of population lives, because of industrial emissions and increasing urbanization, along with traffic jams and heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and by the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly and therefore seldom. Wireless sensor networks seem to be a good solution to this problem, thanks to sensors' low cost and autonomy, as well as their fine-grained deployment. A careful deployment of sensors is therefore necessary to get better performances, while ensuring a minimal financial cost.

We have tackled the issue of WSN deployment for air pollution monitoring in a series of papers this year. In [10], we tackled the optimization problem of sensor deployment and we proposed an integer programming model, which allows to find the optimal network topology while ensuring air quality monitoring with a high precision and the minimum financial cost. Most of existing deployment models of wireless sensor networks are generic and assume that sensors have a given detection range. This assumption does not fit pollutant concentrations sensing. Our model takes into account interpolation methods to place sensors in such a way that pollution concentration is estimated with a bounded error at locations where no sensor is deployed. This solution was further tested and evaluated on a data set of the Lyon city [9], giving insights on how to establish a good compromise between the deployment budget and the precision of air quality monitoring.

In practice, multiple pollution sources can be present in an area. For this reason, in [11] we propose to apply a spatial clustering algorithm to the air pollution data in order to determine pollution zones that are due to the same pollutant sources and group them together to find candidate sites for the deployment of sensors. This approach was tested on real world data, namely the Paris pollution data, which was recorded in March 2014.

A very important deployment parameter is the height at which the sensor is placed. In [12], we demonstrate the impact of this parameter, usually neglected in the literature. This pushed us to study a 3D deployment model, based on an air pollution dispersion model issued from real experiments, performed in wind tunnels emulating the pollution emitted by a steady state traffic flow in a typical street canyon.

Access Point Deployment

The problem of designing wireless local networks (WLANs) involves deciding where to install the access points (APs), and assigning frequency channels to them with the aim to cover the service area and to guarantee enough capacity to users. In [5], we propose different solutions to the problems related to the WLAN design. In the first part, we focus on the problem of designing a WLAN by treating separately the AP positioning and the channel assignment problems. For the AP positioning issue, we formulate it as a set covering problem. Since the computation complexity limits the exact solution, we propose two heuristics to offer efficient solutions. On the other hand, for the channel assignment, we define this issue as a minimum interference frequency assignment problem and propose three heuristics: two of them aim to minimize the interference at AP locations, and the third one minimizes the interference at the TPs level. In the second part, we treat jointly the two aforementioned issues based on the concept of virtual forces. In this case, we start from an initial solution provided by the separated approach and try to enhance it by adjusting the APs positions and reassigning their operating frequencies.

Mobile Traffic Analysis

The analysis of operator-side mobile traffic data is a recently emerged research field, and, apart a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last four years. In [8], we provided a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines and proposing a complete introductory guide to the research based on mobile traffic analysis.

The usage of these datasets in the design of new networking solutions, in order to achieve the so-called cognitive networking paradigm, is one of the most important applications of these analytics methods. In fact, cognitive networking techniques root in the capability of mining large amounts of mobile traffic data collected in the network, so as to understand the current resource utilization in an automated manner and realize a more dynamic management of network resources, that adapts to the significant spatiotemporal fluctuations of the mobile demand. In [6], we take a first step towards cellular cognitive networks by proposing a framework that analyzes mobile operator data, builds profiles of the typical demand, and identifies unusual situations in network-wide usages. We evaluate our framework on two real-world mobile traffic datasets, and show how it extracts from these a limited number of meaningful mobile demand profiles. In addition, the proposed framework singles out a large number of outlying behaviors in both case studies, which are mapped to social events or technical issues in the network.