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Intuitively, identification of nodes close to the network edge is key to the successful setup, and continued operation, of many sensor network protocols and applications. In a previous study  we introduced local convex view (lcv) as a means to identify nodes close to the network edge by computing the convex hull of nodes within range. In this paper we evaluate lcv in the presence of position estimation error. Extensive simulations with networks of varying size and topology reveal the surprising observation that lev seems unaffected by estimation error. Motivated by this observation we enumerate a complete set of base node configurations seen by lcv. An analysis reveals that lcv is immune to two of these configurations. Further simulations show the frequency of false-positives and false-negatives imposed by a third, ambiguous, configuration to be low. The frequency of the ambiguous case is 10% in the worst case, for all networks tested. We conclude that the geometric properties underlying lcv are responsible for its resilience to error.