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In this article, we have showed some examples illustrating how natural swarming mechanisms can be a source of inspiration for devising innovative resource allocation algorithms in ad hoc cognitive networks having self-organization capabilities. Even though the illustrated mechanisms are rather simple, they are able to tackle some basic issues like decentralized resource allocation with spatial reuse capability. We have illustrated how natural swarms can suggest different levels of adaptation and learning, including cooperative sensing. At the same time, we have shown how the swarming models can benefit from signal processing tools to become more robust and suitable for the application at hand. As an example, we have shown how to make the swarming mechanism robust against random packet drop, quantization, and estimation errors. The simplicity of the swarming model has been instrumental to allow for mathematically tractability and to grasp the fundamental properties of the proposed techniques. This work is only an initial step, together with many parallel approaches in the increasing literature on bioinspired methods, in the direction of an ever-deeper synergism between biological mathematical modeling and signal processing. This is expected to be particularly useful for applications requiring some sort of self-organization. Further developments can be expected from a deeper interaction between the learning phase and the swarming mechanism in a dynamic environment.