Section: New Results

Wireless network deployment

Participants: Ahmed Boubrima, Rodrigue Domga Komguem, Leo Le Taro, Jad Oueis, Walid Bechkit, Khaled Boussetta, Hervé Rivano, Razvan Stanica, Fabrice Valois.

Deployment of Wireless Sensor Networks for Pollution Monitoring

Air pollution has become a major issue of modern megalopolis 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. In our work, we focus on an alternative or complementary approach, with a network of low cost and autonomic wireless sensors, aiming at a finer spatiotemporal granularity of sensing. Generic deployment models of the literature are not adapted to the stochastic nature of pollution sensing.

In this sense, in [2], our main contribution is to design integer linear programming models that compute sensor deployments capturing both the coverage of pollution under time-varying weather conditions and the connectivity of the infrastructure. We evaluate our deployment models on a real data set of Greater London. We analyze the performance of the proposed models and show that our joint coverage and connectivity formulation is tight and compact, with a reasonable enough execution time. We also conduct extensive simulations to derive engineering insights for effective deployments of air pollution sensors in an urban environment.

Unlike most of the existing methods, which rely on simple and generic detection models, our approach is based on the spatial analysis of pollution data, allowing to take into account the nature of the pollution phenomenon. As proof of concept, we apply our approach on real world data, namely the Paris pollution data, which was recorded in March 2014 [7]. In this paper, we consider citywide wireless sensor networks and tackle the minimum-cost node positioning issue for air pollution monitoring. We propose an efficient approach that aims to find optimal sensors and sinks locations while ensuring air pollution coverage and network connectivity.

Mobile wireless sensor networks can also be used for monitoring air pollution, where the aim is usually to generate accurate pollution maps in real time. The generation of pollution maps can be performed using either sensor measurements or physical models which simulate the phenomenon of pollution dispersion. The combination of these two information sources, known as data assimilation, makes it possible to better monitor air pollution by correcting the simulations of physical models while relying on sensor measurements. The quality of data assimilation mainly depends on the number of measurements and their locations. A careful deployment of nodes is therefore necessary in order to get better pollution maps. In an ongoing work [30], we tackle the placement problem of pollution sensors and design a mixed integer programming model allowing to maximize the assimilation quality while ensuring the connectivity of the network. We perform some simulations on a dataset of the city of Lyon in order to show the effectiveness of our model regarding the quality of pollution coverage.

For an air pollution monitoring system deployment to be relevant relative to urban air quality aspects, we are concerned with maintaining the system properties over time. Indeed, one of the major drawbacks of cheap sensors is their drift: chemical properties degrade over time and alter the measurement accuracy. We challenge this issue by designing distributed, online recalibration procedures. In [16], we present a simulation framework modelling a mobile wireless sensor network (WSN) and we assess the system's measurement confidence using trust propagation paradigms. As WSN calibrations translate to information exchange between sensors, we also study means of limiting the number of such transmissions by skipping the calibrations deemed least profitable to the system.

Wireless Sensor Networks with Linear Topology

In wireless sensor networks with linear topology, knowing the physical order in which nodes are deployed is useful not only for the target application, but also to some network services, like routing or data aggregation. Considering the limited resources of sensor nodes, the design of autonomous protocols to find this order is a challenging topic.

In [9], we propose a distributed and iterative centroid-based algorithm to address this problem. At each iteration, the algorithm selects two virtual anchors and finds the order of a subset of nodes, placed between these two anchors. The proposed algorithm requires local node connectivity knowledge and the identifier of the first sensor node of the network, which is the only manually configured parameter. This solution, scalable and lightweight from the deployment and maintenance point of view, is shown to be robust to connectivity degradation, correctly ordering more than 95% of the nodes, even under very low connectivity conditions.

Function Placement in Public Safety Networks

In response to the growing demand in the public safety community for broadband communication systems, LTE is currently being adopted as the base technology for next generation public safety networks. In parallel, notable efforts are being made by the 3GPP to enhance the LTE standard in order to offer public safety oriented services. In the recent Release 13, the Isolated E-UTRAN Operation for Public Safety (IOPS) concept was introduced. IOPS aims at maintaining a level of communication between public safety users, offering them local mission-critical services even when the backhaul connectivity to the core network is not fully functional. Isolated operation is usually needed in mission-critical situations, when the infrastructure is damaged or completely destroyed, and in out of coverage areas. In [6], we present a detailed technical overview on the IOPS specifications, and then identify several research prospects and development perspectives opened up by IOPS.

An isolated base station is a base station having no connection to a traditional core network. To provide services to users, an isolated base station is co-located with an entity providing the same functionalities as the traditional core network, referred to as Local EPC. In order to cover wider areas, several base stations are interconnected, forming a network that should be served by a single Local EPC. In [20], [24], we tackle the Local EPC placement problem in the network, to determine with which of the base stations the Local EPC must be co-located. We propose a novel centrality metric, flow centrality, which measures the capacity of a node to receive the total amount of flows in the network. We show that co-locating the Local EPC with the base station having the maximum flow centrality maximizes the total amount of traffic the Local EPC can receive from all base stations, under certain capacity and load distribution constraints. We compare the flow centrality to other state of the art centrality metrics, and emphasize its advantages.

User Association in Public Safety Oriented Mobile Networks

In many disaster scenarios, communication infrastructure fails to provide network services for both civilians and first responders. One solution is to have rapidly deployable mobile networks formed by interconnected base stations, that are easy to move, deploy, and configure. Such public safety-oriented networks are different from classical mobile networks in terms of scale, deployment, and architecture.

In this context, we revisit the user association problem [21], for two main reasons. First, the backhaul, formed by the links interconnecting the base stations, must be accounted for when deciding on the association, since it may present a bottleneck with its limited bandwidth. Second, the mission-critical nature of the traffic imposes strict guaranteed bit rate constraints, that must be respected when associating users. Therefore, we propose a network-aware optimal association that minimizes the bandwidth consumption on the backhaul, while still respecting the stringent performance requirements.