Time and Space Optimal Counting in Population Protocols
James Aspnes (YALE), Joffroy Beauquier (LRI), Janna Burman (LRI),, Devan Sohier

TL;DR
This paper investigates the fundamental limits of counting in population protocols, achieving time and space optimal solutions under different fairness assumptions for resource-limited, mobile agents.
Contribution
It introduces a time optimal protocol with minimal space under probabilistic fairness and proves the time-space trade-off bounds under weak fairness conditions.
Findings
A non-guessing protocol with O(n log n) expected time using one bit of memory.
Proven time optimality of the protocol under probabilistic fairness.
A lower bound of Ω(2^n) time for space optimal solutions under weak fairness.
Abstract
This work concerns the general issue of combined optimality in terms of time and space complexity. In this context, we study the problem of (exact) counting resource-limited and passively mobile nodes in the model of population protocols, in which the space complexity is crucial. The counted nodes are memory-limited anonymous devices (called agents) communicating asynchronously in pairs (according to a fairness condition). Moreover, we assume that these agents are prone to failures so that they cannot be correctly initialized. This study considers two classical fairness conditions, and for each we investigate the issue of time optimality of counting given the optimal space per agent. In the case of randomly interacting agents (probabilistic fairness), as usual, the convergence time is measured in terms of parallel time (or parallel interactions), which is defined as the number of…
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Taxonomy
TopicsDistributed systems and fault tolerance · Cooperative Communication and Network Coding · Access Control and Trust
