{"language":[{"iso":"eng"}],"type":"conference","date_updated":"2022-01-06T06:55:00Z","publication":"2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC'21 - IoTSN Symposium)","author":[{"first_name":"Haitham","full_name":"Afifi, Haitham","id":"65718","last_name":"Afifi"},{"orcid":"https://orcid.org/ 0000-0001-7547-8111","first_name":"Arunselvan","full_name":"Ramaswamy, Arunselvan","last_name":"Ramaswamy","id":"66937"},{"first_name":"Holger","id":"126","last_name":"Karl","full_name":"Karl, Holger"}],"abstract":[{"lang":"eng","text":"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."}],"status":"public","project":[{"_id":"27","name":"Akustische Sensornetzwerke - Teilprojekt \"Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke"}],"year":"2021","place":"Montreal, Canada","user_id":"65718","citation":{"apa":"Afifi, H., Ramaswamy, A., &#38; Karl, H. (2021). Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks. In <i>2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium)</i>. Montreal, Canada.","ama":"Afifi H, Ramaswamy A, Karl H. Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks. In: <i>2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium)</i>. Montreal, Canada; 2021.","mla":"Afifi, Haitham, et al. “Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks.” <i>2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium)</i>, 2021.","ieee":"H. Afifi, A. Ramaswamy, and H. Karl, “Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks,” in <i>2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium)</i>, 2021.","chicago":"Afifi, Haitham, Arunselvan Ramaswamy, and Holger Karl. “Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks.” In <i>2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium)</i>. Montreal, Canada, 2021.","short":"H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium), Montreal, Canada, 2021.","bibtex":"@inproceedings{Afifi_Ramaswamy_Karl_2021, place={Montreal, Canada}, title={Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks}, booktitle={2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium)}, author={Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, year={2021} }"},"date_created":"2021-03-12T16:02:04Z","title":"Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks","_id":"21478"}