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Jose Alvarez Lacasia

dblp:136/1040 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
mobile agent
0.212013
Interoperable mobile agents in heterogeneous wireless sensor networks · SenSys 2013
Internet of things and sensor networks
wireless sensor network
0.212013
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
YearPublicationVenuePosition
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 networks
abstract
We 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
SenSys2