This article describes how fault tolerance is an essential issue for many WSN (Wireless Sensor Network) applications such as wildlife monitoring, battlefield surveillance and health monitoring. It represents a great challenge for researchers regarding to the characteristics of sensor nodes which are prone to failures due essentially to their limited resources. Faults may occur, not only when sensor nodes exhaust their energy, but also when the congestion phenomenon emerges, because of a high traffic in the network and limited storage capacity of the sensor nodes. In order to support fault tolerance in WSNs, the authors propose a new scheme which incorporates a link quality estimation algorithm and a congestion detection mechanism to enable nodes that present high quality links to be chosen for routing in a non-congested area in case of faults. Evaluations through simulations under NS2 show that our proposed protocol tolerates faults with a minimum cost relatively to HEEP protocol and improves network’s performances comparatively to other fault tolerant protocols such as EF-LEACH.

The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.

This paper shows a new Double Gate Junctionless (DGJL) MOSFET design based on both highly doped drain/source extensions and gate material engineering. The device electrical behavior at the nanoscale level is investigated using an accurate numerical computation based on TCAD simulation provided by ATLAS 2D simulator, where quantum confinement effects and the modified drift-diffusion transport model that takes into account SCEs are included. Circuit performance parameters are also developed incorporating the impact of dual-material gate engineering and highly doped extension regions. The effect of the proposed design amendments on the performance of the common source single stage amplifier circuit based on DGJL MOSFET is also performed, where the proposed design exhibits an outstanding capability for offering improved amplifier properties. In order to analyze the characteristics and circuit performance of the device, we have adopted mixed mode simulation under Silvaco environment for the implementation of the inverter circuit. Based on the numerical, outcomes satisfactory results are recorded in comparison with the conventional nanoscale junctionless design.