Publications of the Acoustic Sensor Networks project

2023 [ nach oben ]

1.
Afifi, H., Ramaswamy, A., Karl, H.: Reinforcement learning for autonomous vehicle movements in wireless multimedia applications. Pervasive and Mobile Computing. 101799 (2023).
[ BibTeX ] [ URL ] [ DOI ]
 
@article{AFIFI2023101799, abstract = {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) performance 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.}, author = {Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, journal = {Pervasive and Mobile Computing}, keywords = {KISZ fgasn kisz myown}, pages = 101799, title = {Reinforcement learning for autonomous vehicle movements in wireless multimedia applications}, year = 2023 }

2022 [ nach oben ]

1.
Afifi, H., Karl, H., Gburrek, T., Schmalenstöer, J.: Data-driven Time Synchronization in Wireless Multimedia Networks. Proc. International Wireless Communications and Mobile Computing Conference (IWCMC). , Dubrovnik, Croatia (2022).
[ BibTeX ]
 
@inproceedings{afifi2022datadriven, abstract = {In many wireless multimedia applications, like speech enhancement or speaker localization, data sources need to be tightly synchronized. Synchronization is also a well-known problem for medium access control protocols of wireless sensor networks. In this paper, we unconventionally use the synchronization from a multimedia application to synchronize the wireless transmissions. From our analytical results, we derive a threshold for the synchronization accuracy below which the application synchronization could be used to drive a fixed-schedule time-division multiple access protocol. Above that threshold, Carrier Sense Multiple Access with Collision Avoidance is preferable and results in higher throughput. For evaluation, we simulate a synchronization algorithm for acoustic sensor networks and derive stability as well as high-throughput regions.}, address = {Dubrovnik, Croatia}, author = {Afifi, Haitham and Karl, Holger and Gburrek, Tobias and Schmalenstöer, Jörg}, booktitle = {Proc. International Wireless Communications and Mobile Computing Conference (IWCMC)}, keywords = {fgasn myown}, month = {may}, title = {Data-driven Time Synchronization in Wireless Multimedia Networks}, year = 2022 }

2021 [ nach oben ]

1.
Gunther, M., Afifi, H., Brendel, A., Karl, H., Kellermann, W.: Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021).
[ BibTeX ] [ DOI ]
 
@inproceedings{Gunther_Afifi_Brendel_Karl_Kellermann_2021, author = {Gunther, Michael and Afifi, Haitham and Brendel, Andreas and Karl, Holger and Kellermann, Walter}, booktitle = {ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, keywords = {fgasn myown}, title = {Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks}, year = 2021 }
2.
Afifi, H., Guenther, M., Brendel, A., Karl, H., Kellermann, W.: Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities. 14. ITG Conference on Speech Communication (ITG 2021) (2021).
[ BibTeX ]
 
@inproceedings{Afifi_Guenther_Brendel_Karl_Kellermann_2021, author = {Afifi, Haitham and Guenther, Michael and Brendel, Andreas and Karl, Holger and Kellermann, Walter}, booktitle = {14. ITG Conference on Speech Communication (ITG 2021)}, keywords = {fgasn myown}, title = {Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities}, year = 2021 }
3.
Afifi, H., Sauer, F., Karl, H.: Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding. Prof. IEEE Into. Conf. Advanced Networks and Telecommunications Systems (ANTS). IEEE (2021).
[ BibTeX ]
 
@inproceedings{noauthororeditor, author = {Afifi, Haitham and Sauer, Fabian and Karl, Holger}, booktitle = {Prof. IEEE Into. Conf. Advanced Networks and Telecommunications Systems (ANTS)}, keywords = {fgasn myown}, month = {dec}, publisher = {IEEE}, title = {Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding}, year = 2021 }
4.
Afifi, H., Ramaswamy, A., Karl, H.: Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks. 2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium) (2021).
[ BibTeX ]
 
@inproceedings{afifi21:_reinf_learn_auton_vehic_movem, abstract = {In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs). In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength). Second, quality of service (QoS) which is used to measure the network's performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-Q-Networks (DQN). Additionally, we compare the performance of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions. We show that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs.}, author = {Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, booktitle = {2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC'21 - IoTSN Symposium)}, keywords = {fgasn imported}, title = {{Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks}}, year = 2021 }
5.
Afifi, H., Ramaswamy, A., Karl, H.: A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks. 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) (CCNC 2021) (2021).
[ BibTeX ]
 
@inproceedings{afifi21:_reinf_learn_qoi_qos_aware, abstract = {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 = {Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, booktitle = {2021 IEEE 18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2021)}, keywords = {fgasn imported}, title = {{A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks}}, year = 2021 }

2020 [ nach oben ]

1.
Afifi, H., Karl, H.: Reinforcement Learning for Virtual Network Embedding in Wireless Sensor Networks. 2020 Thirteenth International Workshop on Selected Topics in Mobile and Wireless Computing (STWiMob’2020) (2020).
[ BibTeX ]
 
@inproceedings{afifi20:_reinf_learn_virtual_networ_embed, abstract = {Upcoming sensing applications (acoustic or video) will have high processing requirements not satisfiable by a single node or need input from multiple sources (e.g., speaker localization). Offloading these applications to cloud or mobile edge is an option, but when running in a wireless senor network (WSN), it might entail needlessly high data rate and latency. An alternative is to spread processing inside the WSN, which is particularly attractive if the application comprises individual components. This scenario is typical for applications like acoustic signal processing. Mapping components to nodes can be formulated as wireless version of the NP-hard Virtual Network Embedding (VNE) problem, for which various heuristics exist. We propose a Reinforcement Learning (RL) framework, which relies on Q-Learning and uses either Greedy Epsilon or Epsilon Decay for exploration. We compare both exploration methods to the result of an optimization approach and show empirically that the RL framework achieves good results in terms of network delay within few number of steps.}, author = {Afifi, Haitham and Karl, Holger}, booktitle = {2020 Thirteenth International Workshop on Selected Topics in Mobile and Wireless Computing (STWiMob'2020)}, keywords = {fgasn imported}, title = {{Reinforcement Learning for Virtual Network Embedding in Wireless Sensor Networks}}, year = 2020 }

2019 [ nach oben ]

1.
Afifi, H., Karl, H.: An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding. 12th IFIP Wireless and Mobile Networking Conference, WMNC 2019, Paris, France, September 11-13, 2019. pp. 95–102 (2019).
[ BibTeX ] [ URL ] [ DOI ]
 
@inproceedings{DBLP:conf/mwcn/AfifiK19, author = {Afifi, Haitham and Karl, Holger}, booktitle = {12th {IFIP} Wireless and Mobile Networking Conference, {WMNC} 2019, Paris, France, September 11-13, 2019}, keywords = {fgasn imported}, pages = {95–102}, title = {An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding}, year = 2019 }
2.
Afifi, H., Karl, H.: Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding. 16th IEEE Annual Consumer Communications & Networking Conference, CCNC 2019, Las Vegas, NV, USA, January 11-14, 2019. pp. 1–4 (2019).
[ BibTeX ] [ URL ] [ DOI ]
 
@inproceedings{DBLP:conf/ccnc/AfifiK19, author = {Afifi, Haitham and Karl, Holger}, booktitle = {16th {IEEE} Annual Consumer Communications {\&} Networking Conference, {CCNC} 2019, Las Vegas, NV, USA, January 11-14, 2019}, keywords = {fgasn imported}, pages = {1–4}, title = {Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding}, year = 2019 }
3.
Afifi, H., Horbach, K., Karl, H.: A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019, Barcelona, Spain, October 21-23, 2019. pp. 1–6 (2019).
[ BibTeX ] [ URL ] [ DOI ]
 
@inproceedings{DBLP:conf/wimob/AfifiHK19, author = {Afifi, Haitham and Horbach, Konrad and Karl, Holger}, booktitle = {2019 International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019, Barcelona, Spain, October 21-23, 2019}, keywords = {fgasn imported}, pages = {1–6}, title = {A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding}, year = 2019 }

2018 [ nach oben ]

1.
Afifi, H., Auroux, S., Karl, H.: MARVELO: Wireless virtual network embedding for overlay graphs with loops. 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018, Barcelona, Spain, April 15-18, 2018. pp. 1–6 (2018).
[ BibTeX ] [ URL ] [ DOI ]
 
@inproceedings{DBLP:conf/wcnc/AfifiAK18, author = {Afifi, Haitham and Auroux, S{{é}}bastien and Karl, Holger}, booktitle = {2018 {IEEE} Wireless Communications and Networking Conference, {WCNC} 2018, Barcelona, Spain, April 15-18, 2018}, keywords = {fgasn imported}, pages = {1–6}, title = {{MARVELO:} Wireless virtual network embedding for overlay graphs with loops}, year = 2018 }