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The traditional design of communication networks has rarely been able to focus on the optimization of global network properties. Ultra-wideband (UWB) radio is emerging as an attractive physical layer for wireless communication networks offering new opportunities for the principled design and optimization of network properties. We develop a framework for the principled design of UWB wireless networks based on a flexible cost function that can be tailored and scaled to a wide range of networks and applications, ranging from sensor networks to voice and data wire- less networks. The function comprises cost terms associated with transmission, connection setup, interference, and quality-of-ser- vice. Multihop routing strategies are associated with admissible paths of minimal cost that are computable in linear time. The cost function together with the overall level of requests determine the dynamics of the connections and the equilibrium topology of the network. We report simulation results in the case of simple ring and square lattice networks. Index Terms—Ad hoc networks, cost minimization, routing, sensor networks, small-world networks, UWB (ultra-wideband) radio, wireless networks

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The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms.