Energy efficient software defined networking algorithm for wireless sensor networks

The real-time properties and operational constraints of Wireless Sensor Networks (WSNs) have emerged the need for designing energy efficient routing protocols. Recently, software defined network based WSN (SDN-WSN) emerging technology has offered a significant development by untying control logic plane from the low power sensor nodes. This centralized programmable control still suffers from several configuration challenges in distributed sensors environment. Meta-heuristic based SDN approaches had been proposed for the efficient path selection in WSN but they still suffer from both, exploration and exploitation problems. Therefore, this paper addresses these shortcomings by proposing a meta-heuristic based dolphin echolocation algorithm (DEA) for optimizing route selection in WSNs. Objective function of the DEA algorithm is to consider the residual energy of the nodes for selecting energy efficient routes. The proposed algorithm performance is compared with several meta-heuristic algorithms in terms of energy-consumption, and network throughput parameters

Wireless Sensor Networks Deployment for Air Pollution Monitoring

International audienceRecently, air pollution monitoring emerges as a major issue of the development of smart cities and the well-being of citizens. Air pollution is traditionally monitored using some measuring stations that are accurate but expensive, big and inflexible. This leads to bad global estimations of pollution concentrations. The emergence of air quality sensors, which are less expensive, allows to consider a new pollution monitoring paradigm based on the wireless interconnection between these sensors. This allows to ensure a district-wide air pollution monitoring. In this paper, we tackle the minimum-cost node positioning issue for the detection of air pollution thresholds. We propose two models for wireless sensors deployment while taking into account the air pollution modelling and the probabilistic sensing of nodes. We evaluate our deployment models on a real data set of Greater London and conduct extensive simulations to study the impact of some parameters, among which sensors' height. Results show that the deployment cost depends on the dispersion of pollutants in the area of interest and can be minimized by placing sensors at a height close to the one of pollution sources

WSN Scheduling for Energy-Efficient Correction of Environmental Modelling

International audienceWireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical parameter such as temperature, humidity, air pollution, etc. Most existing WSN-based environmental monitoring systems use data interpolation based on sensor measurements in order to construct the spatiotemporal field of physical parameters. However, these fields can be also approximated using physical models which simulate the dynamics of physical phenomena. In this paper, we focus on the use of wireless sensor networks for the aim of correcting the physical model errors rather than interpolating sensor measurements. We tackle the activity scheduling problem and design an optimization model and a heuristic algorithm in order to select the sensor nodes that should be turned off to extend the lifetime of the network. Our approach is based on data assimilation which allows us to use both measurements and the physical model outputs in the estimation of the spatiotemporal field. We evaluate our approach in the context of air pollution monitoring while using a dataset from the Lyon city, France and considering the characteristics of a monitoring system developed in our lab. We analyze the impact of the nodes' characteristics on the network lifetime and derive guidelines on the optimal scheduling of air pollution sensors

Error-Bounded Air Quality Mapping Using Wireless Sensor Networks

International audienceMonitoring 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 data set 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

Leveraging the Potential of WSN for an Efficient Correction of Air Pollution Fine-Grained Simulations

International audienc

Optimal WSN Deployment Models for Air Pollution Monitoring

International audienceAir 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 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 of 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

On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms

International audienc