The Energy Complexity of Las Vegas Leader Election

• Main Authors: Chang, Yi-Jun, Jiang, Shunhua
• Format: Journal Article
• Language: English
• Published: 17.05.2022
• Subjects: Computer Science - Data Structures and Algorithms
• DOI: 10.48550/arxiv.2205.08642

We consider the time and energy complexities of randomized leader election in a multiple-access channel, where the number of devices $n\geq 2$ is unknown. It is well-known that for polynomial-time randomized leader election algorithms with success probability $1-1/poly(n)$, the optimal energy complexity is $\Theta(\log\log^*n)$ if receivers can detect collisions, and $\Theta(\log^*n)$ otherwise. Without collision detection, all existing randomized leader election algorithms using $o(\log\log n)$ energy are Monte Carlo in that they may fail with some small probability, and they may consume unbounded energy and never halt when they fail. Though the optimal energy complexity of leader election appears to be settled, it is still an open question to attain the optimal $O(\log^*n)$ energy complexity by an efficient Las Vegas algorithm that never fails. In this paper we address this fundamental question. $\textbf{Separation between Monte Carlo and Las Vegas:}$ Without collision detection, we prove that any Las Vegas leader election algorithm with finite expected time complexity must use $\Omega(\log\log n)$ energy, establishing a large separation between Monte Carlo and Las Vegas algorithms. $\textbf{Exponential improvement with sender collision detection:}$ In the setting where senders can detect collisions, we design a new leader election algorithm that finishes in $O(\log^{1+\epsilon}n)$ time and uses $O(\epsilon^{-1}\log\log\log n)$ energy in expectation, showing that sender collision detection helps improve the energy complexity exponentially. $\textbf{Optimal deterministic leader election algorithm:}$ As a side result, via derandomization, we show a new deterministic algorithm that takes $O(n\log(N/n))$ time and $O(\log(N/n))$ energy to elect a leader from $n$ devices, where each device has a unique identifier in $[N]$. This algorithm is time-optimal and energy-optimal.