Simplifying the operation of hybrid ad-hoc sensor networks with neural networks as the sole data reconstruction method

Hybrid ad hoc sensor networks, which are known for their cost-effective sensors and opportunistic yet affordable management, are becoming increasingly popular. In such networks, in addition to data reconstruction, various different methods are used to address operational problems, such as failure of sensors, resilience against attacks, loss of calibration, etc. A combination of several methods may not always be practical or optimal. However, this research demonstrates that it is not only possible but also beneficial to use a single data reconstruction method instead, thus decreasing the operational cost. Artificial neural networks, and particularly the multi-layer perceptron, are proposed as a single method. Through simulations, nine scenarios are analyzed to demonstrate the fitness for purpose of this approach. The findings demonstrate that the multi-layer perceptron can not only be used as a sole data reconstruction method, but also consistently improves the quality of data reconstruction across all the scenarios tested here.