MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput

Stress Engineering of a Window Porous Silicon Layer based on Pseudo Substrate Suitable for III-V Monolithic Integration

Due to Silicon (Si) material abundance and specific properties, monolithic integration of III-V semiconductors on (Si) is of paramount importance for the next-generation in Optoelectronic devises. An alternative approach to lattice mismatched single silicon crystal substrates for heteroepitaxy is proposed. In this work, we have suggested a design of a compliant virtual substrate and we have explored the modulation of stress/lattice parameter of a window layer based on porous silicon pseudo-substrates allowing a defect free epitaxial growth. We prepared a silicon window layer with low porosity and variable thicknesses whose stress is modulated by the succession of several layers with gradual porosity. As a result, we evaluated the stress and the lattice parameter in compliant substrate before and after thermal annealing. The pores reorganization process was supported in Argon atmosphere at constant temperature (900 °C). The samples were studied morphologically by Field Emission scanning Electron Microscope (FE-SEM) and structurally by High Resolution X-Ray Diffraction (HR-XRD) and Nano-Raman

Evolution de la methode de tirage EPR de Si polycristallins destines a la fabrication de photopiles

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Fabrication and characterization of magnetic porous silicon with curie temperature above room temperature

International audienceIn this study, we demonstrate a completely novel synthesis route for producing magnetic porous silicon. The magnetic properties of this material are induced by manganese atoms. The Mn-doping in Si is achieved by ion implantation. A subsequent anodization of the substrate is done to turn it into porous silicon. Several characterization techniques, such as transmission electronic microscopy, atomic force microscopy and photoluminescence are combined to probe the structural and the optical properties of this material. Furthermore, temperature and magnetic field dependent magnetization is analyzed using superconducting quantum interference device. In addition to the well-reported structural and optical properties of the porous silicon, our Mn-doped porous silicon samples exhibit a magnetic behavior with a curie temperature (T-C) higher than room temperature. These results indicate that the magnetic porous silicon can be integrated with microelectronics and photonics technologies to produce new devices, such as magnetophotonic crystals and polarized emitting diodes

The effect of nitridation on the optical properties of InAs quantum dots grown on GaAs substrate by MBE

International audienceThe effect of nitridation on the optical properties of InAs quantum dots grown on GaAs substrate by MBE, Vacuum