A reconfigurable FPGA-based architecture for modular nodes in wireless sensor networks

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Portilla, T. Riesgo, and Á. de Castro, "A reconfigurable FPGA-based architecture for modular nodes in wireless sensor networks", 3rd Southern Conference on Programmable Logic, SPL 2007, Mar del Plata (Argentina), pp. 203 - 206A reconfigurable platform for sensor networks is presented. This platform has features that allow easy reuse of the node in several applications avoiding redesigning the system from scratch. The node includes an FPGA which is the core of the reconfiguration capabilities of the node. Several hardware interfaces for sensor standard protocols like I2C or PWM have been developed and implemented in the FPGA. Remote reconfiguration is an important feature and sensor networks can take advantage of it in order to improve the global performance

Engine performance characteristics and evaluation of variation in the length of intake plenum

In the engine with multipoint fuel injection system using electronically controlled fuel injectors has an intake manifold in which only the air flows and, the fuel is injected into the intake valve. Since the intake manifolds transport mainly air, the supercharging effects of the variable length intake plenum will be different from carbureted engine. Engine tests have been carried out with the aim of constituting a base study to design a new variable length intake manifold plenum. The objective in this research is to study the engine performance characteristics and to evaluate the effects of the variation in the length of intake plenum. The engine test bed used for experimental work consists of a control panel, a hydraulic dynamometer and measurement instruments to measure the parameters of engine performance characteristics. The control panel is being used to perform administrative and management operating system. Besides that, the hydraulic dynamometer was used to measure the power of an engine by using a cell filled with liquid to increase its load. Thus, measurement instrument is provided in this test to measure the as brake torque, brake power, thermal efficiency and specific fuel consumption. The results showed that the variation in the plenum length causes an improvement on the engine performance characteristics especially on the fuel consumption at high load and low engine speeds which are put forward the system using for urban roads. From this experiment, it will show the behavior of engine performance

Distributed computing in space-based wireless sensor networks

This thesis investigates the application of distributed computing in general and wireless sensor networks in particular to space applications. Particularly, the thesis addresses issues related to the design of "space-based wireless sensor networks" that consist of ultra-small satellite nodes flying together in close formations. The design space of space-based wireless sensor networks is explored. Consequently, a methodology for designing space-based wireless sensor networks is proposed that is based on a modular architecture. The hardware modules take the form of 3-D Multi-Chip Modules (MCM). The design of hardware modules is demonstrated by designing a representative on-board computer module. The onboard computer module contains an FPGA which includes a system-on-chip architecture that is based on soft components and provides a degree of flexibility at the later stages of the design of the mission.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each