The use of wireless sensor networks continue to increase in many fields (scientific, logistic, military or health, etc).The size of the sensors constitutes, however, an important limitation, mainly in term of energetic autonomy and therefore lifetime because the battery must be very small. For this reason, the improvement of energy efficiency is a critical issue for WSN protocols. Clustering in wireless sensor networks is an effective way of structuring the network. Its purpose is to identify a subset of nodes in the network and to assign a cluster head to it.Hierarchical routing or clustering routing is considered to be the most favorable approach in terms of energy efficiency. It is based on the concept (child node -parent node) where the child nodes forward their messages to their parent, who then routes them in the entire network via other parent nodes to the base station (sink).Two major approaches are derived from this type of protocol: chain-based approach and cluster-based approach.Low Energy Adaptive Clustering Hierarchy (LEACH) is considered as the first hierarchical routing protocol based on the second approach. It is also one of the most popular hierarchical routing algorithms for sensor networks. Another variant of LEACH, called Low-Energy Adaptive Clustering Hierarchy centralized (LEACH-C), is also presented. This paper presents an improvement of LEACH and LEACH-C protocol based on two modifications one on balancing energy distribution of network by means of changing range of nodes being cluster head and other by load balancing the number of nodes equally by fixing the average value N, so the lifetime of the network is increased. Simulation results show that Improved LEACH-C can improve system lifetime over its comparatives.Index Terms-Wireless sensor network (WSN), leach, LEACH-C, clustering algorithm. I. INTRODUCTIONRecent advances in micro-manufacturing and wireless communication technologies have spawned a new generation of networks called Wireless Sensor Networks (WSN) [1]. They consist of a multitude of sensors distributed randomly in areas often hostile and / or inaccessible to humans. These sensors collect various information about the physical or environmental environment and transmit them to a remote base station via wireless communications. Sensor networks find applications in monitoring (forest fire, meteorological measurements, air quality control), connected objects etc.Sensors, also known as nodes in the remainder of the paper Manuscript
<span lang="EN-US">Localization of nodes is one of the key issues of wireless sensor network (WSN) that gained a wide attention in recent years. The existing localization techniques can be generally categorized into two types: range-based and range-free. Compared with rang-based schemes, the range-free schemes are more cost-effective, because no additional ranging devices are needed. As a result, we focus our research on the range-free schemes. In this paper we study three types of range-free location algorithms to compare the localization error and energy consumption of each one. Centroid algorithm requires a normal node has at least three neighbor anchors, while DV-hop algorithm doesn’t have this requirement. The third studied algorithm is the amorphous algorithm similar to DV-Hop algorithm, and the idea is to calculate the hop distance between two nodes instead of the linear distance between them .The simulation results show that the localization accuracy of the amorphous algorithm is higher than that of other algorithms and the energy consumption does not increase too much. </span>
This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.
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