Walid Bechkit

A Highly Scalable Key Pre-Distribution Scheme for Wireless Sensor Networks

Given the sensitivity of the potential WSN applications and because of resource limitations, key management emerges as a challenging issue for WSNs. One of the main concerns when designing a key management scheme is the network scalability. Indeed, the protocol should support a large number of nodes to enable a large scale deployment of the network. In this paper, we propose a new scalable key management scheme for WSNs which provides a good secure connectivity coverage. For this purpose, we make use of the unital design theory. We show that the basic mapping from unitals to key pre-distribution allows us to achieve high network scalability. Nonetheless, this naive mapping does not guarantee a high key sharing probability. Therefore, we propose an enhanced unital-based key pre-distribution scheme providing high network scalability and good key sharing probability approximately lower bounded by 1-e-1 ≈ 0.632. We conduct approximate analysis and simulations and compare our solution to those of existing methods for different criteria such as storage overhead, network scalability, network connectivity, average secure path length and network resiliency. Our results show that the proposed approach enhances the network scalability while providing high secure connectivity coverage and overall improved performance. Moreover, for an equal network size, our solution reduces significantly the storage overhead compared to those of existing solutions.

Optimal WSN Deployment Models for Air Pollution Monitoring

Air pollution has become a major issue in the modern megalopolis because of industrial emissions and increasing urbanization along with traffic jams and the heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly, and, therefore, seldom. In this paper, 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 in the literature are not adapted to the stochastic nature of pollution sensing. 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.

A New Scalable Key Pre-Distribution Scheme for WSN

Given the sensitivity of the potential WSN applications, security emerges as a challenging issue in these networks. Because of the resource limitations, symmetric key establishment is one favorite paradigm for securing WSN. One of the main concerns when designing a key management scheme for WSN is the network scalability. Indeed, the protocol should support a large number of nodes to enable a large scale deployment of the network. In this paper, we propose a new highly scalable key establishment scheme for WSN. For that purpose, we make use, for the first time, of the unital design theory. We show that the basic mapping from unitals to pairwise key establishment allows to achieve an extremely high network scalability while degrading the key sharing probability. Then, we propose a new unital-based key pre-distribution approach which provides high network scalability and good key sharing probability. We conduct analytical analysis to compare our solutions to existing ones, the obtained results show that our approach enhances the network scalability while providing good overall performances. Also, we show that our solutions reduce significantly the storage overhead at equal network size compared to existing solutions.

Optimal Deployment of Wireless Sensor Networks for Air Pollution Monitoring

Recently, air pollution monitoring emerges as a main service of smart cities because of the increasing industrialization and the massive urbanization. Wireless sensor networks (WSN) are a suitable technology for this purpose thanks to their substantial benefits including low cost and autonomy. Minimizing the deployment cost is one of the major challenges in WSN design, therefore sensors positions have to be carefully determined. In this paper, we propose two integer linear programming formulations based on real pollutants dispersion modeling to deal with the minimum cost WSN deployment for air pollution monitoring. We illustrate the concept by applying our models on real world data, namely the Nottingham City street lights. We compare the two models in terms of execution time and show that the second flow based formulation is much better. We finally conduct extensive simulations to study the impact of some parameters and derive some guidelines for efficient WSN deployment for air pollution monitoring.

Joint Connectivity-Coverage Temperature-Aware Algorithms for Wireless Sensor Networks

Temperature variations have a significant effect on low power wireless sensor networks as wireless communication links drastically deteriorate when temperature increases. A reliable deployment should take temperature into account to avoid network connectivity problems resulting from poor wireless links when temperature increases. A good deployment needs also to adapt its operation and save resources when temperature decreases and wireless links improve. Taking into account the probabilistic nature of the wireless communication channel, we develop a mathematical model that provides the most energy efficient deployment in function of temperature without compromising the correct operation of the network by preserving both connectivity and coverage. We use our model to design three temperature-aware algorithms that seek to save energy (i) by putting some nodes in hibernate mode as in the Stop-Operate (SO) algorithm, or (ii) by using transmission power control as in Power-Control (PC), or (iii) by doing both techniques as in Stop-Operate Power-Control (SOPC). All proposed algorithms are fully distributed and solely rely on temperature readings without any information exchange between neighbors, which makes them low overhead and robust. Our results identify the optimal operation of each algorithm and show that a significant amount of energy can be saved by taking temperature into account.

Optimal Deployment of Dense WSN for Error Bounded Air Pollution Mapping

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 this ongoing work, we focus on an alternative or complementary approach, using a network of low cost and autonomic wireless sensors, allowing for a finer spatiotemporal granularity of air quality sensing. We tackle the optimization problem of sensor deployment and propose 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.

Error-Bounded Air Quality Mapping Using Wireless Sensor Networks

Monitoring air quality has become a major challenge of modern cities where the majority of population lives. In this paper, we focus on using wireless sensor networks for air pollution mapping. We tackle the optimization problem of sensor deployment and propose two placement models allowing to minimize the deployment cost and ensure an error-bounded air pollution mapping. Our models take into account the sensing drift of sensor nodes and the impact of weather conditions. Unlike most of existing deployment models, which assume that sensors have a given detection range, we base on 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. We evaluate our model on a dataset of the Lyon City and give insights on how to establish a good compromise between the deployment budget and the precision of air quality monitoring. We also compare our model to generic approaches and show that our formulation is at least 3 times better than random and uniform deployment.

Temperature-Aware Density Optimization for Low Power Wireless Sensor Networks

High temperatures negatively affect the quality of radio communication links both at transmission and reception sides. In this paper, we investigate the effect of temperature on connectivity and show that more energy can be saved by allowing some nodes to go to deep sleep mode when temperature decreases and links improve. We propose a simple and fully distributed temperature-aware algorithm that dynamically adapts the network effective density to allow further energy savings while maintaining network connectivity.

Poster: Toward a Better Monitoring of Air Pollution using Mobile Wireless Sensor Networks

Mobile wireless sensor networks (MWSN) are widely used for monitoring physical phenomena such as 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 this ongoing work, 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 Lyon city, France in order to show the effectiveness of our model regarding the quality of pollution coverage.

Cost-Precision Tradeoffs in 3D Air Pollution Mapping using WSN

Air pollution has become a major issue of modern megalopolis, where the majority of world population lives. Measuring air pollution levels is an important step in designing and assessing air quality related public policies. Unfortunately, existing solutions are inadequate to get insights on the real exposition of citizens. In particular, high quality sensors deployed today are too large and too costly to envision a three dimensional deployment at the scale of a street. In this paper, we investigate the deployment of wireless sensor networks (WSN) used for building a three-dimensional mapping of pollution concentrations. We consider in our simulations a 3D model of air pollution dispersion based on real experiments performed in wind tunnels emulating the pollution emitted by a steady state traffic flow in a typical street canyon. Our contribution is to analyze the performances of different 3D WSN topologies in terms of the trade-off between the economical cost of the infrastructure and the quality of the reconstructed air pollution mapping.