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The heterogeneity of connected devices, technologies, and protocols anticipated in future-generation information and communication networks also requires the development of new approaches for robust and self-adaptive systems. Recently, methods observed from biological phenomena have gained much attention as viable alternatives or inspiration for the(More)
The Adaptive Base Station Positioning Algorithm (ABPA) is presented, which is based on a neural net approximation of the traac density in the coverage area of a cellular mobile communication system. ABPA employs simulated annealing, thereby achieving quasi-optimal base station locations depending on the topography of the investigated area. Furthermore, ABPA(More)
In this paper we propose a resilient scheme for multi-path routing using a biologically-inspired attractor selection method. The main advantage of this approach is that it is highly noise-tolerant and capable of operating in a very robust manner under changing environment conditions. We will apply an enhanced attractor selection model to multi-path routing(More)