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Evangelos Kranakis

Researcher at Carleton University

Publications -  515
Citations -  10789

Evangelos Kranakis is an academic researcher from Carleton University. The author has contributed to research in topics: Robot & Mobile robot. The author has an hindex of 46, co-authored 502 publications receiving 10330 citations. Previous affiliations of Evangelos Kranakis include Purdue University & Carleton College.

Papers
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Proceedings ArticleDOI

Tree exploration with little memory

TL;DR: It is shown that bounded memory is not sufficient to explore with stop all trees of bounded degree, and a sharper lower bound Ω(log log log log n)-bit memory is presented on required memory size.
Book ChapterDOI

Evacuating Robots from a Disk Using Face-to-Face Communication Extended Abstract

TL;DR: In this article, the authors studied the problem of minimizing the evacuation time of two robots located at the center of a unit disk, where the robots can cooperate by exchanging information whenever they meet.
Book ChapterDOI

Morelia Test: Improving the Efficiency of the Gabriel Test and Face Routing in Ad-Hoc Networks

TL;DR: A new local test for preprocessing a wireless network that produces a planar subgraph is considered, which requires low overhead and does not eliminate existing links unless it is needed to eliminate a crossing, thus reducing overhead associated with multiple hops.
Book ChapterDOI

Probabilistic Protocols for Node Discovery in Ad Hoc Multi-channel Broadcast Networks

TL;DR: This work presents a communication model that is independent of the network configuration that will be established after node discovery, and presents a pair of node discovery protocols for k ≥ 2 nodes in a multi-channel system and analyzes them using the given communication model.
Proceedings ArticleDOI

Search on a Line by Byzantine Robots

TL;DR: In this paper, the authors considered the problem of fault-tolerant parallel search on an infinite line by n robots and gave several algorithms whose running time depends on the ratio f/n, the density of faulty robots, and also prove lower bounds.