Data Dissemination Performance in Large-Scale Sensor Networks

As the use of wireless sensor networks increases, the need for (energy-)efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper we develop a model describing the message count in large-scale wireless sensor networks. We focus our attention on the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period. This generalization proves to be useful for optimizing the design and usage of the algorithm. For single-cell networks we show how the message count increases with the size of the network and how this depends on the Trickle parameters. Furthermore, we derive distributions of inter-broadcasting times and investigate their asymptotic behavior. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Additionally, we develop an approximation for the expected number of transmissions in multi-cell networks. All results are validated by simulations

Multiple Redundancy Constants with Trickle

International audienceWireless sensor network protocols very often use the Trickle algorithm to govern information dissemination. For example, the widely used IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) uses Trickle to emit control packets. We derive an analytical model of Trickle to take into account multiple redundancy constants and the common lack of synchronization among nodes. Moreover, we demonstrate message count unfairness when Trickle uses a unique global redundancy constant because nodes with less neighbors transmit more often. Consequently, we propose a heuristic algorithm that calculates a redundancy constant for each node as a function of its number of neighbors. Our calculated redundancy constants reduce unfairness among nodes by distributing more equally the number of transmitted messages in the network. Our analytical model is validated by emulations of constrained devices running the Contiki Operating System and its IPv6 networking stack. Furthermore, results very well corroborate the heuristic algorithm improvements

Multiple Redundancy Constants with Trickle

International audienceWireless sensor network protocols very often use the Trickle algorithm to govern information dissemination. For example, the widely used IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) uses Trickle to emit control packets. We derive an analytical model of Trickle to take into account multiple redundancy constants and the common lack of synchronization among nodes. Moreover, we demonstrate message count unfairness when Trickle uses a unique global redundancy constant because nodes with less neighbors transmit more often. Consequently, we propose a heuristic algorithm that calculates a redundancy constant for each node as a function of its number of neighbors. Our calculated redundancy constants reduce unfairness among nodes by distributing more equally the number of transmitted messages in the network. Our analytical model is validated by emulations of constrained devices running the Contiki Operating System and its IPv6 networking stack. Furthermore, results very well corroborate the heuristic algorithm improvements

Redes neuronales artificiales para simular el efecto del viento sobre el patrón de distribución del agua de un aspersor

A new approach based on Artificial Neural Networks (ANNs) is presented to simulate the effects of wind on the distribution pattern of a single sprinkler under a center pivot or block irrigation system. Field experiments were performed under various wind conditions (speed and direction). An experimental data from different distribution patterns using a Nelson R3000 Rotator® sprinkler have been split into three and used for model training, validation and testing. Parameters affecting the distribution pattern were defined. To find an optimal structure, various networks with different architectures have been trained using an Early Stopping method. The selected structure produced R2= 0.929 and RMSE = 6.69 mL for the test subset, consisting of a Multi-Layer Perceptron (MLP) neural network with a backpropagation training algorithm; two hidden layers (twenty neurons in the first hidden layer and six neurons in the second hidden layer) and a tangent-sigmoid transfer function. This optimal network was implemented in MATLAB® to develop a model termed ISSP (Intelligent Simulator of Sprinkler Pattern). ISSP uses wind speed and direction as input variables and is able to simulate the distorted distribution pattern from a R3000 Rotator® sprinkler with reasonable accuracy (R2> 0.935). Results of model evaluation confirm the accuracy and robustness of ANNs for simulation of a single sprinkler distribution pattern under real field conditions.Se presenta un nuevo modelo basado en la técnica de Redes Neuronales Artificiales (RNA) para simular el efecto del viento sobre la distribución de agua de un aspersor, en un sistema estacionario o en equipos pivote. Se han realizado una serie de ensayos experimentales con diferentes velocidades y direcciones de viento, para el emisor Rotator R3000 de Nelson. El conjunto de datos obtenidos para los diferentes patrones de distribución del agua han sido divididos en tres grupos, y utilizados en las correspondientes fases de entrenamiento, análisis y validación. Se han definido los parámetros que influyen sobre el patrón de distribución de agua. Con el fin de encontrar una estructura de red óptima, varias redes con diferente arquitectura han sido entrenadas usando un método supervisado. Con la estructura óptima se consiguió un R2= 0,929 y RMSE = 6,69 mL para el grupo de ensayos correspondientes a la red Multi-Layer Perceptron (MLP) mediante el algoritmo de aprendizaje supervisado con retroalimentación, dos niveles de capas ocultas (veinte neuronas en el primer nivel y seis neuronas en el segundo) y una función de transferencia tangente hiperbólica. Esta red optimizada fue implementada en MATLAB para desarrollar un modelo llamado ISSP (Simulador Inteligente del Modelo de Distribución). ISSP utiliza la velocidad y dirección de viento como variables de entrada y tiene la capacidad de simular el modelo distorsionado de la distribución de agua de un emisor Rotator R3000 con una buena precisión (R2> 0,935). Los resultados confirman la precisión y robustez de las técnicas de RNA para simular el patrón de distribución del agua de un aspersor en condiciones de campo

TransEnergy - a tool for energy storage optimization, peak power and energy consumption reduction in DC electric railway systems

Electrified railways are large users of electrical power at a time when grid supply conversion to renewable energy production is making supply to the grid less predictable and environmental concerns demand reduction in energy use. These developments make it desirable to control and reduce both total energy usage and peak power demand of railway systems. While AC systems have a well-developed ability to regenerate power to the grid, high transmission losses in DC systems make local storage of energy a more attractive option. A model has been created integrating a versatile and configurable database-driven generic rail network model with a power supply network representative of DC electric railways. The work is intended as a high-level design tool to explore system wide behaviors prior to detailed final design modelling of specific technologies. To validate our method, predictions of train motion and power demand have been compared with data from the Merseyrail network in the UK. Simulating a full day of traffic for the Wirral Line of Merseyrail (237 services on two routes) with the assumption of energy storage being available at each electrical sub-station revealed the dependence of storage effectiveness on the timetable and traffic density at specific locations. The model is combined with a genetic algorithm to optimise system parameters (storage size, charge/discharge power limits, timetable, train driving style/trajectory) and also enables identification of cases in which poorly specified storage technology would have little impact on peak power and energy consumption

Pore-network modeling of trickle bed reactors: Pressure drop analysis

A pore network model (PNM) has been developed to simulate gas–liquid trickle flows inside fixed beds of spherical particles. The geometry has been previously built from X-ray micro-tomography experiments, and the flow in the throats between pores is modeled as a pure viscous Poiseuille two-phase flow. The flow distribution between pores and throats is obtained by solving mass and momentum balance equations. As a first application of this simple but powerful meso-scale model, a focus is proposed on the ability of PNM to estimate pressure drop and liquid saturation in co-current gas–liquid flows. PNM results are compared to the classical 1D pressure drop models of Attou et al. (1999), Holub et al. (1992) and Larachi et al. (1991). Agreement and discrepancies are discussed, and, finally, it has been found that the actual PNM approach produces realistic pressure drops as far as inertial contributions to friction are negligible. Concerning liquid saturation, the PNM only estimates its value in the throats between pores. As a consequence, liquid saturations are overestimated, but they can be easily corrected by an ad hoc empirical model