---
_id: '25278'
abstract:
- lang: eng
  text: Using Service Function Chaining (SFC) in wireless networks became popular
    in many domains like networking and multimedia. It relies on allocating network
    resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm,
    so that it optimizes the performance of the SFC. When the load of incoming requests
    -- competing for the limited network resources -- increases, it becomes challenging
    to decide which requests should be admitted and which one should be rejected.
    In this work, we propose a deep Reinforcement learning (RL) solution that can
    learn the admission policy for different dependencies, such as the service lifetime
    and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve
    baseline that admits a request whenever there are available resources. We show
    that deep RL outperforms the baseline and provides higher acceptance rate with
    low rejections even when there are enough resources.
author:
- first_name: Haitham
  full_name: Afifi, Haitham
  id: '65718'
  last_name: Afifi
- first_name: Fabian Jakob
  full_name: Sauer, Fabian Jakob
  last_name: Sauer
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Afifi H, Sauer FJ, Karl H. Reinforcement Learning for Admission Control in
    Wireless Virtual Network Embedding. In: <i>2021 IEEE International Conference
    on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>. ; 2021.'
  apa: Afifi, H., Sauer, F. J., &#38; Karl, H. (2021). Reinforcement Learning for
    Admission Control in Wireless Virtual Network Embedding. <i>2021 IEEE International
    Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>.
  bibtex: '@inproceedings{Afifi_Sauer_Karl_2021, place={Hyderabad, India}, title={Reinforcement
    Learning for Admission Control in Wireless Virtual Network Embedding}, booktitle={2021
    IEEE International Conference on Advanced Networks and Telecommunications Systems
    (ANTS) (ANTS’21)}, author={Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger},
    year={2021} }'
  chicago: Afifi, Haitham, Fabian Jakob Sauer, and Holger Karl. “Reinforcement Learning
    for Admission Control in Wireless Virtual Network Embedding.” In <i>2021 IEEE
    International Conference on Advanced Networks and Telecommunications Systems (ANTS)
    (ANTS’21)</i>. Hyderabad, India, 2021.
  ieee: H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission
    Control in Wireless Virtual Network Embedding,” 2021.
  mla: Afifi, Haitham, et al. “Reinforcement Learning for Admission Control in Wireless
    Virtual Network Embedding.” <i>2021 IEEE International Conference on Advanced
    Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>, 2021.
  short: 'H. Afifi, F.J. Sauer, H. Karl, in: 2021 IEEE International Conference on
    Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21), Hyderabad,
    India, 2021.'
date_created: 2021-10-04T10:42:20Z
date_updated: 2022-01-06T06:56:58Z
ddc:
- '000'
file:
- access_level: closed
  content_type: application/pdf
  creator: hafifi
  date_created: 2021-10-04T10:43:19Z
  date_updated: 2021-10-04T10:43:19Z
  file_id: '25279'
  file_name: Preprint___Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_Wireless_Networks.pdf
  file_size: 534737
  relation: main_file
  success: 1
file_date_updated: 2021-10-04T10:43:19Z
has_accepted_license: '1'
keyword:
- reinforcement learning
- admission control
- wireless sensor networks
language:
- iso: eng
place: Hyderabad, India
project:
- _id: '27'
  name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
    über funkbasierte Sensornetzwerke
publication: 2021 IEEE International Conference on Advanced Networks and Telecommunications
  Systems (ANTS) (ANTS'21)
status: public
title: Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding
type: conference
user_id: '65718'
year: '2021'
...
---
_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'
...
---
_id: '10780'
author:
- first_name: Zakarya
  full_name: Guettatfi, Zakarya
  last_name: Guettatfi
- first_name: Philipp
  full_name: Hübner, Philipp
  last_name: Hübner
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
- first_name: Bernhard
  full_name: Rinner, Bernhard
  last_name: Rinner
citation:
  ama: 'Guettatfi Z, Hübner P, Platzner M, Rinner B. Computational self-awareness
    as design approach for visual sensor nodes. In: <i>12th International Symposium
    on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)</i>. ; 2017:1-8.
    doi:<a href="https://doi.org/10.1109/ReCoSoC.2017.8016147">10.1109/ReCoSoC.2017.8016147</a>'
  apa: Guettatfi, Z., Hübner, P., Platzner, M., &#38; Rinner, B. (2017). Computational
    self-awareness as design approach for visual sensor nodes. In <i>12th International
    Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)</i>
    (pp. 1–8). <a href="https://doi.org/10.1109/ReCoSoC.2017.8016147">https://doi.org/10.1109/ReCoSoC.2017.8016147</a>
  bibtex: '@inproceedings{Guettatfi_Hübner_Platzner_Rinner_2017, title={Computational
    self-awareness as design approach for visual sensor nodes}, DOI={<a href="https://doi.org/10.1109/ReCoSoC.2017.8016147">10.1109/ReCoSoC.2017.8016147</a>},
    booktitle={12th International Symposium on Reconfigurable Communication-centric
    Systems-on-Chip (ReCoSoC)}, author={Guettatfi, Zakarya and Hübner, Philipp and
    Platzner, Marco and Rinner, Bernhard}, year={2017}, pages={1–8} }'
  chicago: Guettatfi, Zakarya, Philipp Hübner, Marco Platzner, and Bernhard Rinner.
    “Computational Self-Awareness as Design Approach for Visual Sensor Nodes.” In
    <i>12th International Symposium on Reconfigurable Communication-Centric Systems-on-Chip
    (ReCoSoC)</i>, 1–8, 2017. <a href="https://doi.org/10.1109/ReCoSoC.2017.8016147">https://doi.org/10.1109/ReCoSoC.2017.8016147</a>.
  ieee: Z. Guettatfi, P. Hübner, M. Platzner, and B. Rinner, “Computational self-awareness
    as design approach for visual sensor nodes,” in <i>12th International Symposium
    on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)</i>, 2017, pp.
    1–8.
  mla: Guettatfi, Zakarya, et al. “Computational Self-Awareness as Design Approach
    for Visual Sensor Nodes.” <i>12th International Symposium on Reconfigurable Communication-Centric
    Systems-on-Chip (ReCoSoC)</i>, 2017, pp. 1–8, doi:<a href="https://doi.org/10.1109/ReCoSoC.2017.8016147">10.1109/ReCoSoC.2017.8016147</a>.
  short: 'Z. Guettatfi, P. Hübner, M. Platzner, B. Rinner, in: 12th International
    Symposium on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC), 2017,
    pp. 1–8.'
date_created: 2019-07-10T12:13:15Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '78'
doi: 10.1109/ReCoSoC.2017.8016147
keyword:
- embedded systems
- image sensors
- power aware computing
- wireless sensor networks
- Zynq-based VSN node prototype
- computational self-awareness
- design approach
- platform levels
- power consumption
- visual sensor networks
- visual sensor nodes
- Cameras
- Hardware
- Middleware
- Multicore processing
- Operating systems
- Runtime
- Reconfigurable platforms
- distributed embedded systems
- performance-resource trade-off
- self-awareness
- visual sensor nodes
language:
- iso: eng
page: 1-8
publication: 12th International Symposium on Reconfigurable Communication-centric
  Systems-on-Chip (ReCoSoC)
status: public
title: Computational self-awareness as design approach for visual sensor nodes
type: conference
user_id: '3118'
year: '2017'
...
---
_id: '11886'
abstract:
- lang: eng
  text: Today, we are often surrounded by devices with one or more microphones, such
    as smartphones, laptops, and wireless microphones. If they are part of an acoustic
    sensor network, their distribution in the environment can be beneficially exploited
    for various speech processing tasks. However, applications like speaker localization,
    speaker tracking, and speech enhancement by beamforming avail themselves of the
    geometrical configuration of the sensors. Therefore, acoustic microphone geometry
    calibration has recently become a very active field of research. This article
    provides an application-oriented, comprehensive survey of existing methods for
    microphone position self-calibration, which will be categorized by the measurements
    they use and the scenarios they can calibrate. Selected methods will be evaluated
    comparatively with real-world recordings.
author:
- first_name: Axel
  full_name: Plinge, Axel
  last_name: Plinge
- first_name: Florian
  full_name: Jacob, Florian
  last_name: Jacob
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Gernot A.
  full_name: Fink, Gernot A.
  last_name: Fink
citation:
  ama: 'Plinge A, Jacob F, Haeb-Umbach R, Fink GA. Acoustic Microphone Geometry Calibration:
    An overview and experimental evaluation of state-of-the-art algorithms. <i>IEEE
    Signal Processing Magazine</i>. 2016;33(4):14-29. doi:<a href="https://doi.org/10.1109/MSP.2016.2555198">10.1109/MSP.2016.2555198</a>'
  apa: 'Plinge, A., Jacob, F., Haeb-Umbach, R., &#38; Fink, G. A. (2016). Acoustic
    Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art
    algorithms. <i>IEEE Signal Processing Magazine</i>, <i>33</i>(4), 14–29. <a href="https://doi.org/10.1109/MSP.2016.2555198">https://doi.org/10.1109/MSP.2016.2555198</a>'
  bibtex: '@article{Plinge_Jacob_Haeb-Umbach_Fink_2016, title={Acoustic Microphone
    Geometry Calibration: An overview and experimental evaluation of state-of-the-art
    algorithms}, volume={33}, DOI={<a href="https://doi.org/10.1109/MSP.2016.2555198">10.1109/MSP.2016.2555198</a>},
    number={4}, journal={IEEE Signal Processing Magazine}, author={Plinge, Axel and
    Jacob, Florian and Haeb-Umbach, Reinhold and Fink, Gernot A.}, year={2016}, pages={14–29}
    }'
  chicago: 'Plinge, Axel, Florian Jacob, Reinhold Haeb-Umbach, and Gernot A. Fink.
    “Acoustic Microphone Geometry Calibration: An Overview and Experimental Evaluation
    of State-of-the-Art Algorithms.” <i>IEEE Signal Processing Magazine</i> 33, no.
    4 (2016): 14–29. <a href="https://doi.org/10.1109/MSP.2016.2555198">https://doi.org/10.1109/MSP.2016.2555198</a>.'
  ieee: 'A. Plinge, F. Jacob, R. Haeb-Umbach, and G. A. Fink, “Acoustic Microphone
    Geometry Calibration: An overview and experimental evaluation of state-of-the-art
    algorithms,” <i>IEEE Signal Processing Magazine</i>, vol. 33, no. 4, pp. 14–29,
    2016.'
  mla: 'Plinge, Axel, et al. “Acoustic Microphone Geometry Calibration: An Overview
    and Experimental Evaluation of State-of-the-Art Algorithms.” <i>IEEE Signal Processing
    Magazine</i>, vol. 33, no. 4, 2016, pp. 14–29, doi:<a href="https://doi.org/10.1109/MSP.2016.2555198">10.1109/MSP.2016.2555198</a>.'
  short: A. Plinge, F. Jacob, R. Haeb-Umbach, G.A. Fink, IEEE Signal Processing Magazine
    33 (2016) 14–29.
date_created: 2019-07-12T05:30:09Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/MSP.2016.2555198
intvolume: '        33'
issue: '4'
keyword:
- Acoustic sensors
- Microphones
- Portable computers
- Smart phones
- Wireless communication
- Wireless sensor networks
language:
- iso: eng
page: 14-29
publication: IEEE Signal Processing Magazine
publication_identifier:
  issn:
  - 1053-5888
status: public
title: 'Acoustic Microphone Geometry Calibration: An overview and experimental evaluation
  of state-of-the-art algorithms'
type: journal_article
user_id: '44006'
volume: 33
year: '2016'
...
---
_id: '17663'
abstract:
- lang: eng
  text: 'In this paper, we define and study a new problem, referred to as the Dependent
    Unsplittable Flow Problem (D-UFP). We present and discuss this problem in the
    context of large-scale powerful (radar/camera) sensor networks, but we believe
    it has important applications on the admission of large flows in other networks
    as well. In order to optimize the selection of flows transmitted to the gateway,
    D-UFP takes into account possible dependencies between flows. We show that D-UFP
    is more difficult than NP-hard problems for which no good approximation is known.
    Then, we address two special cases of this problem: the case where all the sensors
    have a shared channel and the case where the sensors form a mesh and route to
    the gateway over a spanning tree.'
author:
- first_name: R.
  full_name: Cohen, R.
  last_name: Cohen
- first_name: I.
  full_name: Nudelman, I.
  last_name: Nudelman
- first_name: Gleb
  full_name: Polevoy, Gleb
  id: '83983'
  last_name: Polevoy
citation:
  ama: Cohen R, Nudelman I, Polevoy G. On the Admission of Dependent Flows in Powerful
    Sensor Networks. <i>Networking, IEEE/ACM Transactions on</i>. 2013;21(5):1461-1471.
    doi:<a href="https://doi.org/10.1109/TNET.2012.2227792">10.1109/TNET.2012.2227792</a>
  apa: Cohen, R., Nudelman, I., &#38; Polevoy, G. (2013). On the Admission of Dependent
    Flows in Powerful Sensor Networks. <i>Networking, IEEE/ACM Transactions On</i>,
    <i>21</i>(5), 1461–1471. <a href="https://doi.org/10.1109/TNET.2012.2227792">https://doi.org/10.1109/TNET.2012.2227792</a>
  bibtex: '@article{Cohen_Nudelman_Polevoy_2013, title={On the Admission of Dependent
    Flows in Powerful Sensor Networks}, volume={21}, DOI={<a href="https://doi.org/10.1109/TNET.2012.2227792">10.1109/TNET.2012.2227792</a>},
    number={5}, journal={Networking, IEEE/ACM Transactions on}, author={Cohen, R.
    and Nudelman, I. and Polevoy, Gleb}, year={2013}, pages={1461–1471} }'
  chicago: 'Cohen, R., I. Nudelman, and Gleb Polevoy. “On the Admission of Dependent
    Flows in Powerful Sensor Networks.” <i>Networking, IEEE/ACM Transactions On</i>
    21, no. 5 (2013): 1461–71. <a href="https://doi.org/10.1109/TNET.2012.2227792">https://doi.org/10.1109/TNET.2012.2227792</a>.'
  ieee: R. Cohen, I. Nudelman, and G. Polevoy, “On the Admission of Dependent Flows
    in Powerful Sensor Networks,” <i>Networking, IEEE/ACM Transactions on</i>, vol.
    21, no. 5, pp. 1461–1471, 2013.
  mla: Cohen, R., et al. “On the Admission of Dependent Flows in Powerful Sensor Networks.”
    <i>Networking, IEEE/ACM Transactions On</i>, vol. 21, no. 5, 2013, pp. 1461–71,
    doi:<a href="https://doi.org/10.1109/TNET.2012.2227792">10.1109/TNET.2012.2227792</a>.
  short: R. Cohen, I. Nudelman, G. Polevoy, Networking, IEEE/ACM Transactions On 21
    (2013) 1461–1471.
date_created: 2020-08-06T15:22:05Z
date_updated: 2022-01-06T06:53:16Z
department:
- _id: '63'
- _id: '541'
doi: 10.1109/TNET.2012.2227792
extern: '1'
intvolume: '        21'
issue: '5'
keyword:
- Approximation algorithms
- Approximation methods
- Bandwidth
- Logic gates
- Radar
- Vectors
- Wireless sensor networks
- Dependent flow scheduling
- sensor networks
language:
- iso: eng
page: 1461-1471
publication: Networking, IEEE/ACM Transactions on
publication_identifier:
  issn:
  - 1063-6692
status: public
title: On the Admission of Dependent Flows in Powerful Sensor Networks
type: journal_article
user_id: '83983'
volume: 21
year: '2013'
...