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There is strong trend, in current research on communication and sensor networks, to study selforganizing, self-healing systems. This poses great challenges to the research on decentralized systems, but at the same offers great potentials for future developments, especially in view of the current trend towards miniaturized systems. Even if the development of self-organizing systems is probably at the beginning, biological systems offers many examples of self-organization and selfhealing. This is as testified, for example, by swarming behaviors, brain activity, and so on. It is then of great interest to derive mathematical models of biological systems and see how they can suggest novel design tools for engineers. Signal Processing can play a big role in this cross-fertilization, as it can help to find out manageable mathematical problems, study their behavior and test the performance in the presence of disturbances. The challenge is to establish a cross-fertilization of ideas from biological to artificial systems, as well as to help understanding biological systems as such. This dissertation considers the problem of dynamic radio access based on sensing in cognitive radio networks. In particular, we follow a rather alternative path with respect to more conventional approaches and, inspired by biological models, we formulate the search for radio resources, i.e. time and frequency slots, as the search for food by a flock of birds swarming in a cooperative manner, but without any centralized control. The interference distribution in the time-frequency plane takes the role of the food spatial distribution: The birds (radio nodes) fly (allocate their resources) over the regions (time-frequency domain) where there is more food (less interference). During the flight, the birds move (choose their time-frequency slots) in a coordinated way, even in the absence of any central control, in order to avoid collisions (conflicts over common radio resources), yet maintaining the swarm cohesion (i.e., avoiding unnecessary spread in the occupancy of the time-frequency plane). This procedure is applied to the dynamic resource allocation in the frequency domain and in the time-frequency domain, where the primary users in a cognitive radio system are modeled as statistically independent homogeneous continuous-time Markov processes. A rigorous mathematical analysis of the proposed algorithm is also derived. First, we study the stability and the cohesiveness of the swarm in case of local interactions among the nodes, providing closed form expressions for the upper and lower bounds of the swarm size as a function of the network connectivity. Then, using stochastic approximation arguments, we derive the convergence properties of the swarming algorithm in the presence of random disturbances introduced by realistic channels, i.e., link failures, quantization, noise and estimation errors. Spectrum sensing is a critical prerequisite in envisioned applications of wireless cognitive radio networks which promise to resolve the perceived bandwidth scarcity versus under-utilization dilemma. Creating an interference map of the operational region plays an instrumental role in enabling spatial frequency reuse and allowing for dynamic spectrum allocation in a hierarchical access model comprising primary and secondary users. For such purpose, a distributed technique for cooperative spectrum estimation in cognitive radio systems is proposed based on a basis expansion model of the power spectral density map in frequency. The proposed method, based on diffusion adaptation algorithms, estimates and learns the interference profile through local cooperation and without the need for a central processor. Convergence and mean square analysis of the diffusion filter applied to the distributed cooperative sensing problem is also derived. Finally, it is proposed a dynamic resource allocation technique combining a distributed diffusion algorithm, for implementing cooperative sensing, with a swarming technique, for allocating resources in a parsimonious way (i.e., avoiding unnecessary spread in the frequency domain), yet avoiding collisions. In particular, the procedure is applied to the dynamic resource allocation problem in the frequency domain. Numerical results show the improvement that results in the resource allocation performance due to the cooperative estimation of the spectrum. Furthermore, it is shown how the proposed technique endows the resulting bio-inspired network with powerful learning and adaptation capabilities.