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Stefano Antonio Putelli

dblp:437/3648 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0005-5846-2341ORCID · reported

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

Computer networks · 1 · 1 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Embedded and real-time systems
energy harvesting systems
1.012026
BIONIC: A Co-Designed Hardware and Runtime for Time-Sensitive Battery-Free IoT · MobiSys 2026
Embedded and real-time systems
intermittent computing
1.012026
BIONIC: A Co-Designed Hardware and Runtime for Time-Sensitive Battery-Free IoT · MobiSys 2026
Internet of things and sensor networks
battery-free sensing
0.312026
BIONIC: A Co-Designed Hardware and Runtime for Time-Sensitive Battery-Free IoT · MobiSys 2026

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

energy-aware runtime scheduling · 2.0capacitor switching · 2.0
YearPublicationVenuePosition
2026 BIONIC: A Co-Designed Hardware and Runtime for Time-Sensitive Battery-Free IoT
abstract
We introduce BIONIC (duraBle tImekeeper fOr iNtermIttent Computing), a novel power system architecture and software runtime that facilitates time-sensitive intermittent computing for battery-less Internet of Things. BIONIC integrates timekeeping into energy storage hardware: its power system comprises two energy storage capacitors. BIONIC switches between these two energy storage capacitors to estimate ambient power. Using these estimations, BIONIC can predict and track charging times (i.e., off-time durations) to schedule time-sensitive tasks and complete them on time. Our evaluations showed that BIONIC significantly extends the measurable off-time intervals by a factor of 15 to 1620 compared to state-of-the-art, while achieving an accuracy of up to 99.1% and effectively adapting to new energy conditions. Besides, in a typical batteryless application, BIONIC's power-aware intermittent computing runtime boosted the number of completed time-sensitive operations by 30% while reducing failed timely operations by 29%.
Eren Yildiz, Davide Cavedon, Stefano Antonio Putelli, Josiah D. Hester, Kasim Sinan Yildirim
MobiSys3