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Abdelhamid Mellouk 2 AuthorId : Author. Amar Aissani 4 AuthorId : Author.

WSNs deployment framework based on the theory of belief functions

Hide details. Indeed, the number and locations of sensors determine the topology of the WSN, which will further influence its performance. Usually, the sensor locations are precomputed based on a 'perfect' sensor coverage model. However, in reality, there is an inherent uncertainty and imprecision associated with sensor readings.


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This study uses Poisson process and improved Fuzzy logic model to simulate and optimize deployment sensing coverage. Also, the research uses hard and soft computing techniques, disk communication model, and radio frequency RF propagation model to optimize sensing and connectivity coverage, and energy efficiency challenges. The performance of the framework is compared with actual small-scale stochastic deployment and existing simulator such as GloMoSim to demonstrate its optimal performance. Results show a significant difference and commonality.

The results are also compared with theoretical, experimental and simulation deployment scenarios.

Results show a feasible approach that can be used to automatically determine areas of high signal obstruction—which is essential to estimate obstruction parameters in simulations—and mapping of accurate WSN path loss models to enhance the overall optimization process during pre-deployment of large-scale WSN. A computational framework for quality of information analysis for detection-oriented sensor networks Sina Zahedi , M.

Srivastava , Chatschik Bisdikian.

Optimization Framework for Deployment of Wireless Sensor Networks

Russell , Guna Seetharaman. Enhancing the data quality in wireless sensor networks — A review B.


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