---
_id: '21479'
abstract:
- lang: eng
  text: Two of the most important metrics when developing Wireless Sensor Networks
    (WSNs) applications are the Quality of Information (QoI) and Quality of Service
    (QoS). The former is used to specify the quality of the collected data by the
    sensors (e.g., measurements error or signal's intensity), while the latter defines
    the network's performance and availability (e.g., packet losses and latency).
    In this paper, we consider an example of wireless acoustic sensor networks, where
    we select a subset of microphones for two different objectives. First, we maximize
    the recording quality under QoS constraints. Second, we apply a trade-off between
    QoI and QoS. We formulate the problem as a constrained Markov Decision Problem
    (MDP) and solve it using reinforcement learning (RL). We compare the RL solution
    to a baseline model and show that in case of QoS-guarantee objective, the RL solution
    has an optimality gap up to 1\%. Meanwhile, the RL solution is better than the
    baseline with improvements up to 23\%, when using the trade-off objective.
author:
- first_name: Haitham
  full_name: Afifi, Haitham
  id: '65718'
  last_name: Afifi
- first_name: Arunselvan
  full_name: Ramaswamy, Arunselvan
  id: '66937'
  last_name: Ramaswamy
  orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Afifi H, Ramaswamy A, Karl H. A Reinforcement Learning QoI/QoS-Aware Approach
    in Acoustic Sensor Networks. In: <i>2021 IEEE 18th Annual Consumer Communications
    \&#38; Networking Conference (CCNC) (CCNC 2021)</i>. ; 2021.'
  apa: Afifi, H., Ramaswamy, A., &#38; Karl, H. (2021). A Reinforcement Learning QoI/QoS-Aware
    Approach in Acoustic Sensor Networks. In <i>2021 IEEE 18th Annual Consumer Communications
    \&#38; Networking Conference (CCNC) (CCNC 2021)</i>.
  bibtex: '@inproceedings{Afifi_Ramaswamy_Karl_2021, title={A Reinforcement Learning
    QoI/QoS-Aware Approach in Acoustic Sensor Networks}, booktitle={2021 IEEE 18th
    Annual Consumer Communications \&#38; Networking Conference (CCNC) (CCNC 2021)},
    author={Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, year={2021}
    }'
  chicago: Afifi, Haitham, Arunselvan Ramaswamy, and Holger Karl. “A Reinforcement
    Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks.” In <i>2021 IEEE
    18th Annual Consumer Communications \&#38; Networking Conference (CCNC) (CCNC
    2021)</i>, 2021.
  ieee: H. Afifi, A. Ramaswamy, and H. Karl, “A Reinforcement Learning QoI/QoS-Aware
    Approach in Acoustic Sensor Networks,” in <i>2021 IEEE 18th Annual Consumer Communications
    \&#38; Networking Conference (CCNC) (CCNC 2021)</i>, 2021.
  mla: Afifi, Haitham, et al. “A Reinforcement Learning QoI/QoS-Aware Approach in
    Acoustic Sensor Networks.” <i>2021 IEEE 18th Annual Consumer Communications \&#38;
    Networking Conference (CCNC) (CCNC 2021)</i>, 2021.
  short: 'H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications
    \&#38; Networking Conference (CCNC) (CCNC 2021), 2021.'
date_created: 2021-03-12T16:03:53Z
date_updated: 2022-01-06T06:55:00Z
keyword:
- reinforcement learning
- wireless sensor networks
- resource allocation
- acoustic sensor networks
language:
- iso: eng
project:
- _id: '27'
  name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
    über funkbasierte Sensornetzwerke
publication: 2021 IEEE 18th Annual Consumer Communications \& Networking Conference
  (CCNC) (CCNC 2021)
status: public
title: A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks
type: conference
user_id: '65718'
year: '2021'
...