IEEE 802.11 is implemented in many wireless networks, including multi-hop networks where communications between nodes are conveyed along a chain. We present a modeling framework to evaluate the performance of flows conveyed through such a chain. Our framework is based on a hierarchical modeling composed of two levels. The lower level is dedicated to the modeling of each node, while the upper level matches the actual topology of the chain. Our approach can handle different topologies, takes into account Bit Error Rate and can be applied to multi-hop flows with rates ranging from light to heavy workloads. We assess the ability of our model to evaluate loss rate, throughput, and end-to-end delay experienced by flows on a simple scenario, where the number of nodes is limited to three. Numerical results show that our model accurately approximates the performance of flows with a relative error typically less than 10%.
International audienceMulti-hop wireless networks are often regarded as a promising means to extend the limited coverage area offered by WLANs. However, they are usually associated with poor and uncertain performance in terms of available bandwidth and packet losses, which clearly stands as a limitation to their use. In this paper, we consider the performance evaluation of a multi-hop path (also called chain), based on the IEEE 802.11 DCF. The proposed modeling framework is constructive and versatile, so that it can handle various types of multi-hop wireless paths, including scenarios with two flows in opposite directions, and topologies where nodes are exposed to the well-known hidden node problem. The models derived from our framework are conceptually simple, easy to implement and produce generally accurate results for the attained goodput of flows, as well as the datagram loss probability. Typical relative errors for these two quantities are below a few percent. Also, fundamental phenomena occurring in multi-hop wireless networks, such as performance collapse and starvation, are well captured by the models
Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.