Two representative and biologically-inspired spectrum access strategies are proposed and evaluated relative to a baseline strategy that provides anti-social random access to the underlying spectrum. In this work, a bio-socially inspired approach is proposed for SU interactions in support of better throughput for the whole community of SUs. In DSA, Primary Users (PUs) allow Secondary Users (SUs) to access the spectrum as long as they do not interfere with PU transmissions beyond a pre-agreed acceptable level. Within the realm of spectrum access, the problem of "spectrum crunch" is present since some of the spectrum bands are overcrowded and others are underutilized.ĭSA aims to alleviate the problem of spectrum crunch. Our results also suggest that novel optimisation algorithms can benefit from stronger biological mimicry.ĭynamic Spectrum Access (DSA) has been introduced to fulfill the expanded need for spectrum by different wireless networks and applications. Contrary to previous studies, our study shows that mass-recruiting ant species such as the Argentine ant can forage effectively in a dynamic environment. The presence of exploration pheromone increased the efficiency of the resulting network and increased the ants' ability to adapt to changing conditions. We show that the ants are capable of solving the Towers of Hanoi, and are able to adapt when sections of the maze are blocked off and new sections installed.
We mapped all possible solutions to the Towers of Hanoi on a single graph and converted this into a maze for the ants to solve. We also tested whether the ants can adapt to dynamic changes in the problem. We used the Towers of Hanoi puzzle to test whether Argentine ants can solve a potentially difficult optimisation problem. Moreover, ant algorithms, neural networks and similar methods are usually applied to static problems, whereas most biological systems have evolved to perform under dynamically changing conditions. Yet most 'nature-inspired' algorithms take only superficial inspiration from biology, and little is known about how real biological systems solve difficult problems. Natural systems are a source of inspiration for computer algorithms designed to solve optimisation problems.