تصميم بنية معالجة تفرعية من أجل الاستكشاف باستخدام الإشارة الصوتية في شبكات الحساسات اللاسلكية

Abstract EN

The aim of the research is to design an energy-efficient digital signal processing structure that is a suitable option for application in wireless sensor networks (WSN), which have limited power and processing resources as well as communication, the parallel concept is achieved at several levels.

Based on the analytical approach in analyzing the problem into several detailed levels and evaluating the detailed elements with optimal selection for each stage.

First, at the node level for hardware within the framework of convertible algorithms into structures by adding a structural modification for FIR based on the concept of parallel processing and Noble identities that achieve an increase in productivity and a reduction in the number of calculations.

Then we implement the previous structure to achieve the functional structure of the filter bank for each node, secondly and at the network level in terms of design a local processing system that adopts the balanced distribution of calculation tasks required to perform spectral analysis of the audio signal by FFT for the application of exploration by sound signal in WSN.

In other words, we assign a computing role to wireless sensor node for parallel calculation implementation of the application tasks, in order to reduce computation amount required to accomplish the necessary calculation tasks which produce reduction both the time and energy.

Functional testing of the developed structure in the first phase of the results has demonstrated the complete recovery of the audio signal with reduced calculation loads over time.

It is preferred and important choice for implementing in resource-limited equipment such as the wireless sensing node in WSN.

Subsequently, we obtained numerical results that demonstrate the reduction of parallel execution time versus the serial execution time of the FFT algorithm, which improves the performance of digital signal processing applications for WSN.