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Dominick Banasik

dblp:407/4542 · DBLP profile ↗
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3ranked-venue papers
3as first author
3since 2021 · last 2026
0009-0002-1414-7268ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Distributed computing theory · 100%
Computer networks
1 paper
Wireless networking · 50% Internet of things and sensor networks · 50%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed computing theory › distributed complexity
energy complexity
1.922026
Brief Announcement: On Energy Complexity and Multi-Instance Computation in the Congested Clique · PODC 2026
Brief Announcement: Energy-Efficient Maximal Independent Sets in Radio Networks · PODC 2025
Distributed computing theory
distributed algorithms
1.012026
Brief Announcement: On Energy Complexity and Multi-Instance Computation in the Congested Clique · PODC 2026
Distributed computing theory
distributed graph algorithms
0.912025
Brief Announcement: Energy-Efficient Maximal Independent Sets in Radio Networks · PODC 2025
Distributed computing theory › distributed graph algorithms
maximal independent set
0.912025
Brief Announcement: Energy-Efficient Maximal Independent Sets in Radio Networks · PODC 2025
Distributed computing theory › distributed graph algorithms
congested clique
0.312026
Brief Announcement: On Energy Complexity and Multi-Instance Computation in the Congested Clique · PODC 2026
Wireless networking
radio networks
0.312025
Brief Announcement: Energy-Efficient Maximal Independent Sets in Radio Networks · PODC 2025
Internet of things and sensor networks
wireless sensor network
0.312025
Brief Announcement: Energy-Efficient Maximal Independent Sets in Radio Networks · PODC 2025

Methods — techniques the papers use, named apart from their topics

distributed algorithm · 1.7
YearPublicationVenuePosition
2026 Brief Announcement: On Energy Complexity and Multi-Instance Computation in the Congested Clique
Dominick Banasik, Varsha Dani
PODC1
2025 Brief Announcement: Energy-Efficient Maximal Independent Sets in Radio Networks
abstract
Maximal Independent Set (MIS) is one of the most fundamental problems in distributed computing, and it has been studied intensively for over four decades. This paper focuses on the MIS problem in the radio networks model, a standard model widely used to model wireless networks, particularly ad hoc wireless and sensor networks. Energy is a premium resource in these networks, which are typically battery-powered. Hence, designing distributed algorithms that use as little energy as possible is crucial. We use the well-established energy model where a node can be sleeping or awake in a round, and only the awake rounds (when it can send or listen) determine the energy complexity of the algorithm.
Dominick Banasik, Varsha Dani, Fabien Dufoulon, Thomas P. Hayes, Gopal Pandurangan
PODC1
2025 Energy-Efficient Maximal Independent Sets in Radio Networks
abstract
The maximal independent set (MIS) is one of the most fundamental problems in distributed computing, and it has been studied intensively for over four decades. This paper focuses on the MIS problem in the Radio Network model, a standard model widely used to model wireless networks, particularly ad hoc wireless and sensor networks. Energy is a premium resource in these networks, which are typically battery-powered. Hence, designing distributed algorithms that use as little energy as possible is crucial. We use the well-established energy model where a node can be sleeping or awake in a round, and only the awake rounds (when it can send or listen) determine the energy complexity of the algorithm, which we want to minimize. We present new, more energy-efficient MIS algorithms in radio networks with arbitrary and unknown graph topology. We present algorithms for two popular variants of the radio model -- with collision detection (CD) and without collision detection (no-CD). Specifically, we obtain the following results: 1. CD model: We present a randomized distributed MIS algorithm with energy complexity $O(\log n)$, round complexity $O(\log^2 n)$, and failure probability $1 / poly(n)$, where $n$ is the network size. We show that our energy complexity is optimal by showing a matching $Ω(\log n)$ lower bound. 2. no-CD model: In the more challenging no-CD model, we present a randomized distributed MIS algorithm with energy complexity $O(\log^2n \log \log n)$, round complexity $O(\log^3 n \log Δ)$, and failure probability $1 / poly(n)$. The energy complexity of our algorithm is significantly lower than the round (and energy) complexity of $O(\log^3 n)$ of the best known distributed MIS algorithm of Davies [PODC 2023] for arbitrary graph topology.
Dominick Banasik, Varsha Dani, Fabien Dufoulon, Thomas P. Hayes, Gopal Pandurangan
DISC1