Demo Data from EDBT 2026 / reviewers in the wild / expert
Jose Alvarez Lacasia
dblp:136/1040
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Computer networks · 1
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 · 87% Edge and fog computing · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks
mobile agent |
0.2 | 1 | 2013 | Interoperable mobile agents in heterogeneous wireless sensor networks · SenSys 2013 |
Internet of things and sensor networks
wireless sensor network |
0.2 | 1 | 2013 | Interoperable mobile agents in heterogeneous wireless sensor networks · SenSys 2013 |
Methods — techniques the papers use, named apart from their topics
REST architectural principles · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Mobile crowdsensing with mobile agents
Teemu Leppänen, Jose Alvarez Lacasia, Yoshito Tobe, Kaoru Sezaki, Jukka Riekki |
Auton. Agents Multi Agent Syst. | 2 |
| 2013 | Interoperable mobile agents in heterogeneous wireless sensor networksabstractWe demonstrate interoperable mobile agents for language- and platform-independent wireless sensor network programming, with low-power resource-constrained embedded devices as static nodes and Android-based smartphones as mobile nodes, over disparate networks: 6LoWPAN and Wi-Fi. Representational state transfer architectural principles are applied in agent composition, control and migration and exposing the system resources to the Web: the devices, agents, sensor data, tasks and data processing results. The adaptable agent composition includes the task code in any programming language, the agent migrates according to a resource list and the state, i.e. the intermediate task result, represents the agent as a system resource. Mobile agents are then utilized in two tasks: first to collect light sensor data cooperatively in location, saving the mobile node battery whenever possible, and secondly the magnetometer sensor data with the scanned Wi-Fi access points' signal strength is used to detect groups of mobile nodes moving in the same direction in real-time. Teemu Leppänen, Jose Alvarez Lacasia, Archana Ramalingam, Meirong Liu, Erkki Harjula, Pauli Närhi, Jani Ylioja, Jukka Riekki, Kaoru Sezaki, Yoshito Tobe, Timo Ojala |
SenSys | 2 |