Wireless Sensor Networks Case Study

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DATA COLLECTION BASED HYBRID COMPRESSIVE SENSING IN WIRELESS SENSOR NETWORKS
B.Malathi1, Ms.R.Saranya2
P.G scholor1 , Assistant professor2
P.S.R.Rengasamy College of engineering for women, Sivakasi (TN), India.
Malathi.ece44@gmailcom1,saranyar384@gmail.com2

Abstract- Hybrid compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. Hybrid CS method was proposed to reduce the number of transmissions in sensor networks. The Hybrid Compressive Sensing which uses a clustering method in wireless sensor networks.
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INTRODUCTION The research of the wireless sensor networks (WSN) has garner increasing attention owing to its technical importance in widespread applications, such as monitoring and surveillance in the military, civil industries, home automation and control the traffic field. In general, the WSN using a communication method has a multi-hopping, by which messages are transmitted through a sequence of sensor nodes (SN). A WSN consists of a number of sensor nodes and devices that collaborate with each other have a common task such as environment monitoring, object tracking, etc., and report the collected data through wireless sensor interface to a sink node. The energy efficiency of data gathering is one of the most important issues in the wireless sensor networks (WSNs). It has been tackled form various aspects in the beginning of WSNs, and includes the energy conserving sleep scheduling, topology control. Since the beginning of WSNs, which include among the others, energy conserving sleep scheduling, topology control, mobile data collectors, and data aggregations. where the first three approaches (and many others) focus on the efficiency of networking techniques and that transport of the sensory data, data aggregation directly aims to significantly reducing the amount of data to be transported, and it hence complements the other approaches and to achieve energy efficient of data collection for…show more content…
Energy-aware routing algorithm forms energy-balanced clusters and distribute energy consumption equally. This algorithm has used from fuzzy neural network for clustering and section of cluster head nodes among other nodes. Energy consumption happens in three domains: sensing, data processing (including AD/DA and digital signal processing), and communications. The sensing, signal processing parts operate at low sequential and consume less than 1mW. This is over an order of extent less than the energy consumption of the communication part. Communication/data processing exchange between sensor nodes but more local processing implemented by one sensor node in order to enlarge the lifetime of the WSN. That energy efficient routing determination is more important than simple shortest path routing. Several strategies are prevalently employed for power aware routing in WSNs that Minimizing the energy consumed for each message

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