Wireless sensors networks are currently being used in different engineering fields such as civil, mechanical and
aerospace engineering for damage detection. Each network contains approximately hundreds to thousands of MEMS
sensors that communicate to its base station. These sensors are placed in different environments and locations that create
changes in their output due to obstacles or interference between them and their base station. A research study was
conducted on wireless MEMS sensor nodes to evaluate the noise level and the effect of environmental interferences as
well as their maximum distance communication. In this paper, the effect of interference environments and obstacles
such as magnetic field created by electricity and cell phone communications, concrete and metal enclosures, and
outside/inside environments were evaluated. In addition, a neural network computer simulation was developed to learn
and teach the users what it takes to classify signals such as time, amount of samples and overtraining in order to obtain
the correct output instead of an unknown. By gathering all this information it helps to save money and time in any
application wireless MEMS sensors are used and idealized models and pictures of communication paths have been
created for easier evaluation of the MEMS sensor networks.
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