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EDBT 2026 Demo / reviewers in the wild / expert

Aya Wallwater

dblp:75/9826 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Computer networks · 2

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
2 papers
Internet of things and sensor networks · 63% Network optimization and economics · 37%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Energy-efficient computing · 100%

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

TopicWeightPapersLastEvidence papers
Network optimization and economics › resource allocation
energy allocation		 0.322013
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms · IEEE Trans. Mob. Comput. 2013
Networking low-power energy harvesting devices: Measurements and algorithms · INFOCOM 2011
Internet of things and sensor networks › wireless sensor network
energy-efficient communication		 0.222013
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms · IEEE Trans. Mob. Comput. 2013
Networking low-power energy harvesting devices: Measurements and algorithms · INFOCOM 2011
Internet of things and sensor networks › energy harvesting
energy harvesting network		 0.212013
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms · IEEE Trans. Mob. Comput. 2013
Internet of things and sensor networks › energy harvesting
energy harvesting devices		 0.112011
Networking low-power energy harvesting devices: Measurements and algorithms · INFOCOM 2011
Energy-efficient computing
energy harvesting		 0.112011
Networking low-power energy harvesting devices: Measurements and algorithms · INFOCOM 2011
Energy-efficient computing › power management
energy storage management		 0.012013
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms · IEEE Trans. Mob. Comput. 2013
Energy-efficient computing
power management		 0.012013
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms · IEEE Trans. Mob. Comput. 2013

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

measurement study · 0.6stochastic energy allocation · 0.3deterministic energy allocation · 0.3stochastic modeling · 0.2
YearPublicationVenuePosition
2013 Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms
abstract
Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as energy harvested from indoor lights. We focus on characterizing the light energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for calculating time fair energy allocation in systems with deterministic energy inputs, as well as in systems where energy inputs are stochastic.
Maria Gorlatova, Aya Wallwater, Gil Zussman
 IEEE Trans. Mob. Comput.2
2011 Networking low-power energy harvesting devices: Measurements and algorithms
abstract
Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as indoor light energy. We focus on characterizing the energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for determining time fair energy allocation in systems with predictable energy inputs, as well as in systems where energy inputs are stochastic.
Maria Gorlatova, Aya Wallwater, Gil Zussman
 INFOCOM2