Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Reinforcement learning for autonomous vehicle movements in wireless multimedia applications
Hasso Platter Institute, Germany.
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).ORCID-id: 0000-0001-7547-8111
Hasso Platter Institute, Germany.
2023 (Engelska)Ingår i: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 92, artikel-id 101799Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We develop a Deep Reinforcement Learning (DeepRL)-based, multi-agent algorithm to efficiently control autonomous vehicles that are typically used within the context of Wireless Sensor Networks (WSNs), in order to boost application performance. As an application example, we consider wireless acoustic sensor networks where a group of speakers move inside a room. In a traditional setup, microphones cannot move autonomously and are, e.g., located at fixed positions. We claim that autonomously moving microphones improve the application performance. To control these movements, we compare simple greedy heuristics against a DeepRL solution and show that the latter achieves best application performance. As the range of audio applications is broad and each has its own (subjective) per-formance metric, we replace those application metrics by two immediately observable ones: First, quality of information (QoI), which is used to measure the quality of sensed data (e.g., audio signal strength). Second, quality of service (QoS), which is used to measure the network's performance when forwarding data (e.g., delay). In this context, we propose two multi-agent solutions (where one agent controls one microphone) and show that they perform similarly to a single-agent solution (where one agent controls all microphones and has a global knowledge). Moreover, we show via simulations and theoretical analysis how other parameters such as the number of microphones and their speed impacts performance.

Ort, förlag, år, upplaga, sidor
Elsevier, 2023. Vol. 92, artikel-id 101799
Nyckelord [en]
Wireless sensor networks, Reinforcement learning, Quality of service, Quality of information, Unmanned vehicles
Nationell ämneskategori
Signalbehandling Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-96027DOI: 10.1016/j.pmcj.2023.101799ISI: 001010863600001OAI: oai:DiVA.org:kau-96027DiVA, id: diva2:1780590
Forskningsfinansiär
Deutsche Forschungsgemeinschaft (DFG), 282835863Tillgänglig från: 2023-07-06 Skapad: 2023-07-06 Senast uppdaterad: 2023-07-06Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Person

Ramaswamy, Arunselvan

Sök vidare i DiVA

Av författaren/redaktören
Ramaswamy, Arunselvan
Av organisationen
Institutionen för matematik och datavetenskap (from 2013)
I samma tidskrift
Pervasive and Mobile Computing
SignalbehandlingDatavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 92 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf