--- _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' ...