In this paper we compare the capabilities of Wireless LAN and Ultra-Wideband to enable advanced localization systems. The two technologies are compared regarding their suitability towards time-delay-estimation by theoretical bounds as well as by means of practical time-of-flight range estimation in LOS and NLOS conditions in an industrial environment.
This paper presents positioning results determined by a multi-layer, packet based OMNet++ simulator for communication and positioning in an autonomous wireless sensor network. The simulator includes an IR-UWB physical layer model considering the impact of multi-user interference, a highly flexible MAC layer which performs physical layer adaptations to optimize the total link performance, and a ranging and positioning module. We will give an estimation of the positioning accuracy of the system by considering ideal, LOS and NLOS channel conditions.
We derive asymptotic expressions for the probability mass function (PMF) of the number of hops (a.k.a hop-count distribution) required to reach a designated distance D in random, connected and uniformly distributed one-and twodimensional ad hoc networks. The elegance of the result is owned to the application of Renewal Theory, a generalization of Poisson processes. The method is general, requiring only that the distribution of the hop-length under the desired policy be known, such that application to two-dimensional networks is straightforward, amounting to the problem of computing the projection of the average hop-length onto a straight. Consequently, the formulae are remarkably simple and accurate compared to current knowledge, allowing for the effect of hopping policies (e.g. random, closest-and furthest-neighbor) to be easily taken into account; and yielding counter-intuitive insight on this important parameter of ad hoc networks. For instance, the analysis reveals that the hop-count distribution is nothing but a scaled version of the node-number 1 distribution, with the scale parameter inversely proportional the average hop-length times the network density. In the linear case, where the node-count distribution is Poisson, this result both elucidates the equivalence and quantifies the impact of node-density and hopping policy on the likely number of hops to reach any destination.
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