For the Marco Polo problem (binary radial probes), the paper gives algorithms using 2.53 to 6 times log-base-2 of n probes, a lower bound of 2.4 times log n, and an O(log k)-competitive strategy for finding all k targets.
Freeze-Tag in $L_1$ has Wake-up Time Five
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abstract
The Freeze-Tag Problem, introduced in Arkin et al. (SODA'02) consists of waking up a swarm of $n$ robots, starting from a single active robot. In the basic geometric version, every robot is given coordinates in the plane. As soon as a robot is awakened, it can move towards inactive robots to wake them up. The goal is to minimize the wake-up time of the last robot, the makespan. Despite significant progress on the computational complexity of this problem and on approximation algorithms, the characterization of exact bounds on the makespan remains one of the main open questions. In this paper, we settle this question for the $\ell_1$-norm, showing that a makespan of at most $5r$ can always be achieved, where $r$ is the maximum distance between the initial active robot and any sleeping robot. Moreover, a schedule achieving a makespan of at most $5r$ can be computed in optimal time $O(n)$. Both bounds, the time and the makespan are optimal. This implies a new upper bound of $5\sqrt{2}r \approx 7.07r$ on the makespan in the $\ell_2$-norm, improving the best known bound so far $(5+2\sqrt{2}+\sqrt{5})r \approx 10.06r$.
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The Marco Polo Problem: A Combinatorial Approach to Geometric Localization
For the Marco Polo problem (binary radial probes), the paper gives algorithms using 2.53 to 6 times log-base-2 of n probes, a lower bound of 2.4 times log n, and an O(log k)-competitive strategy for finding all k targets.