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Nicola Santoro

Researcher at Carleton University

Publications -  372
Citations -  11399

Nicola Santoro is an academic researcher from Carleton University. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 54, co-authored 357 publications receiving 10693 citations. Previous affiliations of Nicola Santoro include University of Waterloo & University of Bari.

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Journal ArticleDOI

Connected graph searching

TL;DR: It is proved that the connected search game is monotone for trees, i.e. restricting search strategies to only those where the clean territories increase monotonically does not require more searchers.
Proceedings ArticleDOI

Focused-coverage by mobile sensor networks

TL;DR: This study proposes two solutions, Greedy Advance (GA) and Greedy-Rotation-Greedy (GRG), which are to this knowledge the first sensor self-deployment algorithms that operate in a purely localized manner and yet provide coverage guarantee.
Journal ArticleDOI

Distributed computing by mobile robots: uniform circle formation

TL;DR: In this article, it was shown that the uniform circle formation problem is solvable for any initial configuration in which the robots are in distinct locations, without any additional assumption (if two robots were in the same location, the problem is easily seen to be unsolvable).
Journal ArticleDOI

On the impact of sense of direction on message complexity

TL;DR: It is shown that, in arbitrary graphs, any sense of direction has a dramatic effect on the communication complexity of several important distributed problems: Broadcast, Depth First Traversal, Election, and Spanning Tree Construction.
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Searching for Black Holes in Subways

TL;DR: This work studies the problem of a team of asynchronous computational entities determining the location of black holes in a highly dynamic graph, whose edges are defined by the asynchronous movements of mobile entities (the subway carriers), and presents a solution protocol that solves the fault mapping problem in subway networks with the minimum number of agents possible.