This paper presents a solution for building awareness of the electromagnetic situation in cognitive mobile ad hoc networks (MANET) using the cooperative spectrum sensing method. Signal detection is performed using energy detectors with noise level estimation. Based on the evidence theory, the fusion center decides on the particular channel occupancy, which can process incomplete and unambiguous input data. Next, a reinforced machine learning algorithm estimates the usefulness of particular channels for the MANET transmission and creates backup channels list that could be used in case of interferences. Initial simulations were performed using the MATLAB environment, and next an OMNET-based MAENA high fidelity simulator was used. Performed simulations showed a significant increase in sensing efficiency compared to sensing performed using simple data fusion rules.
Wireless sensor networks are an increasingly popular tool for monitoring various environmental parameters. They can also be used for monitoring the electromagnetic spectrum. Wireless sensors, due to their small size, typically have simplified radio receivers with reduced sensitivity and use small antennas. As a result, their effective performance area is similarly limited. This is especially important in urban areas where there are various kinds of adverse propagation phenomena related to area coverage. The aim of this paper is to present the phenomena in the wireless sensor networks and propose criteria and methods to optimize their deployment to ensure maximizing the probability of detection of emissions, minimization of unmonitored areas, and to provide the necessary hardware redundancy in the priority areas. Influence of detection parameters, number of sensors and range constraints between sensors on received outcomes are also presented.
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