الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Wireless sensor networks (WSNs) can be used for a wide variety of applications deal with monitoring (health environments, seismic, etc.), control (object detection and tracking)and surveillance (battle eld surveillance, perimeter and topology dis covery). Regardless the application in which the sensor network is serving, the data generated in the network eventually must be delivered to the base station (BS However, the limited network bandwidth, node/link failure along with the unreliable communication medium pose great challenges on the sensor network communication
paradigms. To this aim, we propose an adaptive and ecient technique based on compressive sensing for improving the performance of routing in wireless sensor network by compress the sensor’s reading while relying them to the base station. The
proposed technique achieves both minimum energy consumption and increase net work lifetime. Experimental results demonstrate that the proposed ECST technique outperforms PEGASIS and PEGASIS with Hu man coding in terms of the network life time and energy consumption On the other side, to reconstruct the original data from the CS compressed data
that received by the base station and also in order to improve the reconstruction performance, a novel CS reconstruction approach called Adaptive Iterative Forward Backward Greedy Algorithm (AFB) , which falling into the category of TST algo-
rithms is proposed. By solving the least-square problem instead of using the absolute value of inner product, AFB increase the probability to select the correct columns from and therefore, it can give much better approximation performance than all the other tested OMP type algorithms. In addition, AFB uses a simple backtracking step to detect the previous chosen columns reliability and then remove the unreliable
columns at each time. In the provided experiments, AFB,with some modest settings performs better reconstruction for real data set, uniform and Gaussian sparse sig nals. It also shows robust performance under the presence of noise. AFB achieve overwhelming success over OMP, ROMP, SP and FBP methods for the sparse binary signals. To conclude, the demonstrated reconstruction performance of AFB indicates that it is a promising approach, that is capable of reducing the reconstruction errors
signi cantly.