By Jacek Cichoń, Jakub Lemiesz, Marcin Zawada (auth.), Xiang-Yang Li, Symeon Papavassiliou, Stefan Ruehrup (eds.)
This publication constitutes the refereed complaints of the eleventh overseas convention on Ad-hoc, cellular, and instant Networks, ADHOC-NOW 2012 held in Belgrade, Serbia, July 11th of September, 2012. The 36 revised complete papers awarded have been conscientiously reviewed and chosen from seventy six submissions. The authorised papers hide a large spectrum of conventional networking subject matters starting from routing to the appliance layer, to localization in a variety of networking environments reminiscent of instant sensor and ad-hoc networks, and provides insights in a number of software areas.
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Extra resources for Ad-hoc, Mobile, and Wireless Networks: 11th International Conference, ADHOC-NOW 2012, Belgrade, Serbia, July 9-11, 2012. Proceedings
Experiments are conducted to analyze the performance of GeoDE and the inﬂuence of identiﬁed error sources. GeoDE is examined in two scenarios (a) computing the distance between any two neighbors in the network and (b) computing the distance between all nodes and one anchor node. The behavior of GeoDE is tested in diﬀerent network scenarios and for varying signal radius of the devices. The results indicate that the estimation using GeoDE is more accurate than estimates derived by a hop based algorithm.
Nutov’s algorithm  solves a generalized Directed Weighted Degree Constrained Network Design (DWDCN) problem, where the objective is to compute a minimum cost directed subgraph that satisﬁes speciﬁed connectivity requirements and weighted degree constraints. The DWDCN problem is actually more general than we need, so, as in , we consider only two special variants of this problem. One variant computes a k-edge-connected (outconnected or inconnected) subgraph, which is used to solve the multiple topology RMNL problems.
32 S. Merkel, S. Mostaghim, and H. Schmeck A= r2 · (α − sin(α)) 2 (6) α h = r · (1 − cos( )) 2 (7) Substitution A and h by rearranging Equations (5) and (4), it becomes apparent that Δ and θ only depend on α, which has a ﬁxed value range, but are independent from r. The relation of Δ and θ can be approximated using regression. Figure 3 shows data points (Grey line) and the approximated third-degree polynomial function f : Δ → θ (dotted line) derived through polynomial regression. Apparently, f is an almost perfect approximation of the relationship between Δ and θ.