الفهرس 
CHAPTER -1- INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Wireless sensor networks (WSNs) are monitoring systems consisting of many small, low-cost, and low-power devices called sensor nodes. A large number of sensor nodes are deployed in an environment to monitor a physical phenomenon, execute light processes on collected data, and send either raw data or processed information to the base station. Energy consumption is the main challenge of data collection in a wireless sensor network. Several energy-efficient strategies are developed to ensure the longevity of a network. Data reduction is one of the most significant energy management strategies in sensor networks. It concentrates on reducing the volume of the data collected, processed, or communicated within the network. Proposed data reduction based energy management techniques assume radio communication is the most significant energy consumption parameter. However, there are applications in which the computational and sampling energy costs are comparable to or even higher than the communication cost. Therefore, besides the communication level, there is a need to reduce energy consumption costs on sensing and computation levels as well. The main focus of this thesis is to propose data reduction techniques that improve energy efficiency in sensing and computation. Reducing the amount of data in these levels consequently reduces data transmission costs. Data reduction in sensing level is addressed by minimizing the number of sensing operations while data reduction metrics are met. In the computation and communication levels, we used adaptive filtering techniques to process a signal to reduce the volume of information; also the filtering based on data prediction that is an important approach to reduce redundant data transmissions, conserve node energy and overcome the defects resulted from data dissemination in the sensory network. On the other hand, a filter is a device that maps its input signal to another output signal, facilitating the extraction of the desired information in the input signal and eliminate any accompanying noise and interference. An adaptive filter is a device that reduces unnecessary features from a signal. These two objectives reduce data transmission costs and raise efficient-energy. The main contributions of this thesis can be summarized as follows: We introduced a novel model was based on a Finite Impulse Response (FIR) filter integrated with the Recursive least squares (RLS) adaptive filter for improving the signals transferring function by cancelling the unwanted noise and reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy-efficient and providing high convergence of the transferred signals. A Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed Distributed Data Predictive Model (DDPM) was built upon a distributive clustering model for minimizing the amount of transmitted data aimed to decrease the energy consumption in WSN sensor nodes. The proposed DDPM reduced the rate of data transmission to ” ” " ~ " ” ” 20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network. We improved the previously proposed model by incorporating the two of adaptive-filters FIR with RLS through three adaptive two-stages performed at the level of cluster head, for independent fault-correction during the propagation platform. The proposed model included two stages, the first stage comprised self-detection the failure and self-aware for the lost scales, in which relied on responses of delay port and prior-knowledge of absent sensor-signals throughout monitoring, through adjusting the filter weights in the adaptive feedback loop for awarding convergent signals for the lost ones. The second stage is adaptive filtering the registered signals from the previous stage for gaining pure measures and free of interferences. The scheduled model attained a speed in diagnosing faults and awareness the missing readings with a rate of accuracy reached 98.8% improving the robustness of performance. Evaluation criteria revealed the progress of our proposed model in reducing the radio communication to 97.47% that kept about 93.7% of battery energy throughout the picked dataset sample. Hence, it expanded the whole network’s lifetime.