Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Luca Rufer

dblp:324/8936 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

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

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

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
energy harvesting
0.612022
Demo Abstract: DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing · IPSN 2022
Internet of things and sensor networks
wireless sensor
0.612022
Demo Abstract: DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing · IPSN 2022
Embedded and real-time systems › embedded hardware platform
dual processor platform
0.612022
Demo Abstract: DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing · IPSN 2022
Embedded and real-time systems
energy-efficient embedded systems
0.612022
Demo Abstract: DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing · IPSN 2022
Internet of things and sensor networks › energy management
power management
0.212022
Demo Abstract: DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing · IPSN 2022

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

tensorflow inference · 1.1energy harvesting · 1.1
YearPublicationVenuePosition
2022 Demo Abstract: DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing
abstract
Wireless sensors form an integral part of the Internet of Things (IoT), standing at the edge between the cyber and physical domains. Ac-quiring and transmitting environmental data is an energy-intensive workload, especially when considering networks spanning large buildings or even cities. Many works aim to integrate energy har-vesting into wireless sensors, providing them with a greater level of energy autonomy. Initially deployed for energy-rich outdoor environments, recent advances have allowed wireless sensors to efficiently utilize the reduced energy harvested in indoor lighting conditions. This demo introduces a harvesting-based Dual Proces-sor Platform, the DPP3e, designed for energy harvesting in indoor environments. It features various sensors for advanced indoor en-vironmental sensing, e.g. air quality measurements, a low-power display for immediate visual feedback, and a powerful micro controller for energy-efficient inference of Tensorflow models. Fur-thermore, it has two separate RF interfaces: a 2.4 GHz Bluetooth Low Energy (BLE) radio for short-range communication, and a sub-GHz transceiver for long-range communication. Using configurable power domains and advanced power management, it can sustain sending BLE packets every 5 seconds while consuming only 37 µ W.
Luca Rufer, Naomi Stricker, Reto Da Forno, Lothar Thiele, Andres Gomez 0001
IPSN1