---
_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: 2021 IEEE International Conference
on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21). ; 2021.'
apa: Afifi, H., Sauer, F. J., & Karl, H. (2021). Reinforcement Learning for
Admission Control in Wireless Virtual Network Embedding. 2021 IEEE International
Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21).
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 2021 IEEE
International Conference on Advanced Networks and Telecommunications Systems (ANTS)
(ANTS’21). 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.” 2021 IEEE International Conference on Advanced
Networks and Telecommunications Systems (ANTS) (ANTS’21), 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: 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021). ; 2021.'
apa: Afifi, H., Ramaswamy, A., & Karl, H. (2021). A Reinforcement Learning QoI/QoS-Aware
Approach in Acoustic Sensor Networks. In 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021).
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 \& 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 2021 IEEE
18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC
2021), 2021.
ieee: H. Afifi, A. Ramaswamy, and H. Karl, “A Reinforcement Learning QoI/QoS-Aware
Approach in Acoustic Sensor Networks,” in 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021), 2021.
mla: Afifi, Haitham, et al. “A Reinforcement Learning QoI/QoS-Aware Approach in
Acoustic Sensor Networks.” 2021 IEEE 18th Annual Consumer Communications \&
Networking Conference (CCNC) (CCNC 2021), 2021.
short: 'H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications
\& 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: 12th International Symposium
on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC). ; 2017:1-8.
doi:10.1109/ReCoSoC.2017.8016147'
apa: Guettatfi, Z., Hübner, P., Platzner, M., & Rinner, B. (2017). Computational
self-awareness as design approach for visual sensor nodes. In 12th International
Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)
(pp. 1–8). https://doi.org/10.1109/ReCoSoC.2017.8016147
bibtex: '@inproceedings{Guettatfi_Hübner_Platzner_Rinner_2017, title={Computational
self-awareness as design approach for visual sensor nodes}, DOI={10.1109/ReCoSoC.2017.8016147},
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
12th International Symposium on Reconfigurable Communication-Centric Systems-on-Chip
(ReCoSoC), 1–8, 2017. https://doi.org/10.1109/ReCoSoC.2017.8016147.
ieee: Z. Guettatfi, P. Hübner, M. Platzner, and B. Rinner, “Computational self-awareness
as design approach for visual sensor nodes,” in 12th International Symposium
on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), 2017, pp.
1–8.
mla: Guettatfi, Zakarya, et al. “Computational Self-Awareness as Design Approach
for Visual Sensor Nodes.” 12th International Symposium on Reconfigurable Communication-Centric
Systems-on-Chip (ReCoSoC), 2017, pp. 1–8, doi:10.1109/ReCoSoC.2017.8016147.
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. IEEE
Signal Processing Magazine. 2016;33(4):14-29. doi:10.1109/MSP.2016.2555198'
apa: 'Plinge, A., Jacob, F., Haeb-Umbach, R., & Fink, G. A. (2016). Acoustic
Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art
algorithms. IEEE Signal Processing Magazine, 33(4), 14–29. https://doi.org/10.1109/MSP.2016.2555198'
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={10.1109/MSP.2016.2555198},
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.” IEEE Signal Processing Magazine 33, no.
4 (2016): 14–29. https://doi.org/10.1109/MSP.2016.2555198.'
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,” IEEE Signal Processing Magazine, 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.” IEEE Signal Processing
Magazine, vol. 33, no. 4, 2016, pp. 14–29, doi:10.1109/MSP.2016.2555198.'
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. Networking, IEEE/ACM Transactions on. 2013;21(5):1461-1471.
doi:10.1109/TNET.2012.2227792
apa: Cohen, R., Nudelman, I., & Polevoy, G. (2013). On the Admission of Dependent
Flows in Powerful Sensor Networks. Networking, IEEE/ACM Transactions On,
21(5), 1461–1471. https://doi.org/10.1109/TNET.2012.2227792
bibtex: '@article{Cohen_Nudelman_Polevoy_2013, title={On the Admission of Dependent
Flows in Powerful Sensor Networks}, volume={21}, DOI={10.1109/TNET.2012.2227792},
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.” Networking, IEEE/ACM Transactions On
21, no. 5 (2013): 1461–71. https://doi.org/10.1109/TNET.2012.2227792.'
ieee: R. Cohen, I. Nudelman, and G. Polevoy, “On the Admission of Dependent Flows
in Powerful Sensor Networks,” Networking, IEEE/ACM Transactions on, vol.
21, no. 5, pp. 1461–1471, 2013.
mla: Cohen, R., et al. “On the Admission of Dependent Flows in Powerful Sensor Networks.”
Networking, IEEE/ACM Transactions On, vol. 21, no. 5, 2013, pp. 1461–71,
doi:10.1109/TNET.2012.2227792.
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'
...