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Data On the Go: Seamless Data Routing for Intermittently-Powered Battery-Free Sensing. <i>IEEE Transactions on Mobile Computing (TMC)</i>. Published online 2024. doi:<a href=\"https://doi.org/10.1109/TMC.2024.3429636\">10.1109/TMC.2024.3429636</a>","ieee":"G. Liu and L. Wang, “Data On the Go: Seamless Data Routing for Intermittently-Powered Battery-Free Sensing,” <i>IEEE Transactions on Mobile Computing (TMC)</i>, 2024, doi: <a href=\"https://doi.org/10.1109/TMC.2024.3429636\">10.1109/TMC.2024.3429636</a>.","apa":"Liu, G., &#38; Wang, L. (2024). Data On the Go: Seamless Data Routing for Intermittently-Powered Battery-Free Sensing. <i>IEEE Transactions on Mobile Computing (TMC)</i>. <a href=\"https://doi.org/10.1109/TMC.2024.3429636\">https://doi.org/10.1109/TMC.2024.3429636</a>","chicago":"Liu, Gaosheng, and Lin Wang. “Data On the Go: Seamless Data Routing for Intermittently-Powered Battery-Free Sensing.” <i>IEEE Transactions on Mobile Computing (TMC)</i>, 2024. <a href=\"https://doi.org/10.1109/TMC.2024.3429636\">https://doi.org/10.1109/TMC.2024.3429636</a>.","short":"G. Liu, L. Wang, IEEE Transactions on Mobile Computing (TMC) (2024)."}},{"date_created":"2023-12-22T20:06:42Z","department":[{"_id":"75"}],"type":"conference","citation":{"mla":"Blöcher, Marcel, et al. “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES.” <i>Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)</i>, IEEE, doi:<a href=\"https://doi.org/10.1109/INFOCOM52122.2024.10621125\">10.1109/INFOCOM52122.2024.10621125</a>.","bibtex":"@inproceedings{Blöcher_Nedderhut_Chuprikov_Khalili_Eugster_Wang, title={Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES}, DOI={<a href=\"https://doi.org/10.1109/INFOCOM52122.2024.10621125\">10.1109/INFOCOM52122.2024.10621125</a>}, booktitle={Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}, publisher={IEEE}, author={Blöcher, Marcel and Nedderhut, Nils and Chuprikov, Pavel and Khalili, Ramin and Eugster, Patrick and Wang, Lin} }","ama":"Blöcher M, Nedderhut N, Chuprikov P, Khalili R, Eugster P, Wang L. Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. In: <i>Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)</i>. IEEE. doi:<a href=\"https://doi.org/10.1109/INFOCOM52122.2024.10621125\">10.1109/INFOCOM52122.2024.10621125</a>","ieee":"M. Blöcher, N. Nedderhut, P. Chuprikov, R. Khalili, P. Eugster, and L. Wang, “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES,” presented at the IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada, doi: <a href=\"https://doi.org/10.1109/INFOCOM52122.2024.10621125\">10.1109/INFOCOM52122.2024.10621125</a>.","apa":"Blöcher, M., Nedderhut, N., Chuprikov, P., Khalili, R., Eugster, P., &#38; Wang, L. (n.d.). Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. <i>Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)</i>. IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada. <a href=\"https://doi.org/10.1109/INFOCOM52122.2024.10621125\">https://doi.org/10.1109/INFOCOM52122.2024.10621125</a>","short":"M. Blöcher, N. Nedderhut, P. Chuprikov, R. Khalili, P. Eugster, L. Wang, in: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE, n.d.","chicago":"Blöcher, Marcel, Nils Nedderhut, Pavel Chuprikov, Ramin Khalili, Patrick Eugster, and Lin Wang. “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES.” In <i>Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)</i>. IEEE, n.d. <a href=\"https://doi.org/10.1109/INFOCOM52122.2024.10621125\">https://doi.org/10.1109/INFOCOM52122.2024.10621125</a>."},"publication":"Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)","publisher":"IEEE","_id":"50065","language":[{"iso":"eng"}],"doi":"10.1109/INFOCOM52122.2024.10621125","user_id":"102868","conference":{"end_date":"2024-05-23","location":"Vancouver, Canada","start_date":"2024-05-20","name":"IEEE International Conference on Computer Communications (INFOCOM)"},"author":[{"first_name":"Marcel","last_name":"Blöcher","full_name":"Blöcher, Marcel"},{"full_name":"Nedderhut, Nils","last_name":"Nedderhut","first_name":"Nils"},{"first_name":"Pavel","last_name":"Chuprikov","full_name":"Chuprikov, Pavel"},{"full_name":"Khalili, Ramin","first_name":"Ramin","last_name":"Khalili"},{"last_name":"Eugster","first_name":"Patrick","full_name":"Eugster, Patrick"},{"id":"102868","last_name":"Wang","first_name":"Lin","full_name":"Wang, Lin"}],"title":"Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES","status":"public","year":"2024","date_updated":"2024-10-20T15:51:43Z","publication_status":"accepted"},{"citation":{"apa":"Pei, Q., Wang, L., Zhang, D., Yan, B., Yu, C., &#38; Liu, F. (2024). InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment. <i>Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC)</i>. ACM Symposium on Cloud Computing (SoCC), Redmond.","ieee":"Q. Pei, L. Wang, D. Zhang, B. Yan, C. Yu, and F. Liu, “InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment,” presented at the ACM Symposium on Cloud Computing (SoCC), Redmond, 2024.","chicago":"Pei, Qiangyu, Lin Wang, Dong Zhang, Bingheng Yan, Chen Yu, and Fangming Liu. “InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment.” In <i>Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC)</i>, 2024.","short":"Q. Pei, L. Wang, D. Zhang, B. Yan, C. Yu, F. Liu, in: Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC), 2024.","mla":"Pei, Qiangyu, et al. “InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment.” <i>Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC)</i>, 2024.","ama":"Pei Q, Wang L, Zhang D, Yan B, Yu C, Liu F. InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment. In: <i>Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC)</i>. ; 2024.","bibtex":"@inproceedings{Pei_Wang_Zhang_Yan_Yu_Liu_2024, title={InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment}, booktitle={Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC)}, author={Pei, Qiangyu and Wang, Lin and Zhang, Dong and Yan, Bingheng and Yu, Chen and Liu, Fangming}, year={2024} }"},"publication":"Proceedings of the 15th ACM Symposium on Cloud Computing (SoCC)","date_created":"2024-10-20T15:48:58Z","department":[{"_id":"34"},{"_id":"7"},{"_id":"75"}],"type":"conference","conference":{"location":"Redmond","start_date":"2024-11-20","name":"ACM Symposium on Cloud Computing (SoCC)","end_date":"2024-11-22"},"author":[{"last_name":"Pei","first_name":"Qiangyu","full_name":"Pei, Qiangyu"},{"id":"102868","last_name":"Wang","first_name":"Lin","full_name":"Wang, Lin"},{"first_name":"Dong","last_name":"Zhang","full_name":"Zhang, Dong"},{"first_name":"Bingheng","last_name":"Yan","full_name":"Yan, Bingheng"},{"full_name":"Yu, Chen","last_name":"Yu","first_name":"Chen"},{"full_name":"Liu, Fangming","first_name":"Fangming","last_name":"Liu"}],"title":"InferCool: Enhancing AI Inference Cooling through Transparent, Non-Intrusive Task Reassignment","status":"public","year":"2024","date_updated":"2024-10-20T15:49:03Z","_id":"56689","language":[{"iso":"eng"}],"user_id":"102868"},{"language":[{"iso":"eng"}],"doi":"10.1007/s11241-024-09418-4","title":"Inference serving with end-to-end latency SLOs over dynamic edge networks","year":"2024","publication_identifier":{"issn":["0922-6443","1573-1383"]},"author":[{"full_name":"Nigade, Vinod","first_name":"Vinod","last_name":"Nigade"},{"full_name":"Bauszat, Pablo","first_name":"Pablo","last_name":"Bauszat"},{"first_name":"Henri","last_name":"Bal","full_name":"Bal, Henri"},{"id":"102868","full_name":"Wang, Lin","orcid":"0000-0001-7181-6128","last_name":"Wang","first_name":"Lin"}],"date_updated":"2025-12-12T08:18:05Z","publication_status":"published","intvolume":"        60","date_created":"2025-12-12T08:16:33Z","type":"journal_article","department":[{"_id":"34"},{"_id":"7"},{"_id":"75"}],"issue":"2","publication":"Real-Time Systems","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title><jats:p>While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees for end-to-end inference latency service-level objectives (SLO). Jellyfish handles the network variability by utilizing both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to continuously map users and adapt DNNs at runtime, so that we fulfill latency SLOs while maximizing the overall inference accuracy. We further investigate <jats:italic>dynamic</jats:italic> DNNs, i.e., DNNs that encompass multiple architecture variants, and demonstrate their potential benefit through preliminary experiments. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.\r\n</jats:p>"}],"page":"239-290","publisher":"Springer Science and Business Media LLC","_id":"63059","user_id":"102868","volume":60,"status":"public","citation":{"chicago":"Nigade, Vinod, Pablo Bauszat, Henri Bal, and Lin Wang. “Inference Serving with End-to-End Latency SLOs over Dynamic Edge Networks.” <i>Real-Time Systems</i> 60, no. 2 (2024): 239–90. <a href=\"https://doi.org/10.1007/s11241-024-09418-4\">https://doi.org/10.1007/s11241-024-09418-4</a>.","short":"V. Nigade, P. Bauszat, H. Bal, L. Wang, Real-Time Systems 60 (2024) 239–290.","ieee":"V. Nigade, P. Bauszat, H. Bal, and L. Wang, “Inference serving with end-to-end latency SLOs over dynamic edge networks,” <i>Real-Time Systems</i>, vol. 60, no. 2, pp. 239–290, 2024, doi: <a href=\"https://doi.org/10.1007/s11241-024-09418-4\">10.1007/s11241-024-09418-4</a>.","apa":"Nigade, V., Bauszat, P., Bal, H., &#38; Wang, L. (2024). Inference serving with end-to-end latency SLOs over dynamic edge networks. <i>Real-Time Systems</i>, <i>60</i>(2), 239–290. <a href=\"https://doi.org/10.1007/s11241-024-09418-4\">https://doi.org/10.1007/s11241-024-09418-4</a>","bibtex":"@article{Nigade_Bauszat_Bal_Wang_2024, title={Inference serving with end-to-end latency SLOs over dynamic edge networks}, volume={60}, DOI={<a href=\"https://doi.org/10.1007/s11241-024-09418-4\">10.1007/s11241-024-09418-4</a>}, number={2}, journal={Real-Time Systems}, publisher={Springer Science and Business Media LLC}, author={Nigade, Vinod and Bauszat, Pablo and Bal, Henri and Wang, Lin}, year={2024}, pages={239–290} }","ama":"Nigade V, Bauszat P, Bal H, Wang L. Inference serving with end-to-end latency SLOs over dynamic edge networks. <i>Real-Time Systems</i>. 2024;60(2):239-290. doi:<a href=\"https://doi.org/10.1007/s11241-024-09418-4\">10.1007/s11241-024-09418-4</a>","mla":"Nigade, Vinod, et al. “Inference Serving with End-to-End Latency SLOs over Dynamic Edge Networks.” <i>Real-Time Systems</i>, vol. 60, no. 2, Springer Science and Business Media LLC, 2024, pp. 239–90, doi:<a href=\"https://doi.org/10.1007/s11241-024-09418-4\">10.1007/s11241-024-09418-4</a>."}},{"date_created":"2025-03-21T07:15:20Z","type":"journal_article","department":[{"_id":"34"},{"_id":"7"},{"_id":"75"}],"publication":"Computer Networks","citation":{"ama":"Hu J, Wang L, Wu J, Pei Q, Liu F, Li B. A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios. <i>Computer Networks</i>. 2024;257. doi:<a href=\"https://doi.org/10.1016/j.comnet.2024.110952\">10.1016/j.comnet.2024.110952</a>","bibtex":"@article{Hu_Wang_Wu_Pei_Liu_Li_2024, title={A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios}, volume={257}, DOI={<a href=\"https://doi.org/10.1016/j.comnet.2024.110952\">10.1016/j.comnet.2024.110952</a>}, number={110952}, journal={Computer Networks}, publisher={Elsevier BV}, author={Hu, Jiahai and Wang, Lin and Wu, Jing and Pei, Qiangyu and Liu, Fangming and Li, Bo}, year={2024} }","mla":"Hu, Jiahai, et al. “A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios.” <i>Computer Networks</i>, vol. 257, 110952, Elsevier BV, 2024, doi:<a href=\"https://doi.org/10.1016/j.comnet.2024.110952\">10.1016/j.comnet.2024.110952</a>.","short":"J. Hu, L. Wang, J. Wu, Q. Pei, F. Liu, B. Li, Computer Networks 257 (2024).","chicago":"Hu, Jiahai, Lin Wang, Jing Wu, Qiangyu Pei, Fangming Liu, and Bo Li. “A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios.” <i>Computer Networks</i> 257 (2024). <a href=\"https://doi.org/10.1016/j.comnet.2024.110952\">https://doi.org/10.1016/j.comnet.2024.110952</a>.","apa":"Hu, J., Wang, L., Wu, J., Pei, Q., Liu, F., &#38; Li, B. (2024). A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios. <i>Computer Networks</i>, <i>257</i>, Article 110952. <a href=\"https://doi.org/10.1016/j.comnet.2024.110952\">https://doi.org/10.1016/j.comnet.2024.110952</a>","ieee":"J. Hu, L. Wang, J. Wu, Q. Pei, F. Liu, and B. Li, “A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios,” <i>Computer Networks</i>, vol. 257, Art. no. 110952, 2024, doi: <a href=\"https://doi.org/10.1016/j.comnet.2024.110952\">10.1016/j.comnet.2024.110952</a>."},"article_number":"110952","language":[{"iso":"eng"}],"_id":"59074","publisher":"Elsevier BV","doi":"10.1016/j.comnet.2024.110952","user_id":"102868","volume":257,"title":"A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios","status":"public","year":"2024","publication_identifier":{"issn":["1389-1286"]},"author":[{"full_name":"Hu, Jiahai","first_name":"Jiahai","last_name":"Hu"},{"full_name":"Wang, Lin","first_name":"Lin","last_name":"Wang"},{"last_name":"Wu","first_name":"Jing","full_name":"Wu, Jing"},{"first_name":"Qiangyu","last_name":"Pei","full_name":"Pei, Qiangyu"},{"full_name":"Liu, Fangming","first_name":"Fangming","last_name":"Liu"},{"full_name":"Li, Bo","first_name":"Bo","last_name":"Li"}],"date_updated":"2025-03-21T07:16:37Z","publication_status":"published","intvolume":"       257"},{"citation":{"mla":"Wu, Jing, et al. “Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment.” <i>IEEE Transactions on Parallel and Distributed Systems</i>, vol. 35, no. 2, Institute of Electrical and Electronics Engineers (IEEE), 2023, pp. 280–96, doi:<a href=\"https://doi.org/10.1109/tpds.2023.3340518\">10.1109/tpds.2023.3340518</a>.","ama":"Wu J, Wang L, Jin Q, Liu F. Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment. <i>IEEE Transactions on Parallel and Distributed Systems</i>. 2023;35(2):280-296. doi:<a href=\"https://doi.org/10.1109/tpds.2023.3340518\">10.1109/tpds.2023.3340518</a>","bibtex":"@article{Wu_Wang_Jin_Liu_2023, title={Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment}, volume={35}, DOI={<a href=\"https://doi.org/10.1109/tpds.2023.3340518\">10.1109/tpds.2023.3340518</a>}, number={2}, journal={IEEE Transactions on Parallel and Distributed Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Wu, Jing and Wang, Lin and Jin, Qirui and Liu, Fangming}, year={2023}, pages={280–296} }","apa":"Wu, J., Wang, L., Jin, Q., &#38; Liu, F. (2023). Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment. <i>IEEE Transactions on Parallel and Distributed Systems</i>, <i>35</i>(2), 280–296. <a href=\"https://doi.org/10.1109/tpds.2023.3340518\">https://doi.org/10.1109/tpds.2023.3340518</a>","ieee":"J. Wu, L. Wang, Q. Jin, and F. Liu, “Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment,” <i>IEEE Transactions on Parallel and Distributed Systems</i>, vol. 35, no. 2, pp. 280–296, 2023, doi: <a href=\"https://doi.org/10.1109/tpds.2023.3340518\">10.1109/tpds.2023.3340518</a>.","short":"J. Wu, L. Wang, Q. Jin, F. Liu, IEEE Transactions on Parallel and Distributed Systems 35 (2023) 280–296.","chicago":"Wu, Jing, Lin Wang, Qirui Jin, and Fangming Liu. “Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment.” <i>IEEE Transactions on Parallel and Distributed Systems</i> 35, no. 2 (2023): 280–96. <a href=\"https://doi.org/10.1109/tpds.2023.3340518\">https://doi.org/10.1109/tpds.2023.3340518</a>."},"status":"public","page":"280-296","_id":"63060","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","user_id":"102868","volume":35,"publication":"IEEE Transactions on Parallel and Distributed Systems","issue":"2","date_created":"2025-12-12T08:17:09Z","type":"journal_article","department":[{"_id":"34"},{"_id":"7"},{"_id":"75"}],"title":"Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment","year":"2023","author":[{"first_name":"Jing","last_name":"Wu","full_name":"Wu, Jing"},{"full_name":"Wang, Lin","last_name":"Wang","first_name":"Lin","orcid":"0000-0001-7181-6128","id":"102868"},{"full_name":"Jin, Qirui","first_name":"Qirui","last_name":"Jin"},{"first_name":"Fangming","last_name":"Liu","full_name":"Liu, Fangming"}],"publication_identifier":{"issn":["1045-9219","1558-2183","2161-9883"]},"date_updated":"2025-12-12T08:17:56Z","publication_status":"published","intvolume":"        35","language":[{"iso":"eng"}],"doi":"10.1109/tpds.2023.3340518"},{"author":[{"orcid":"0000-0001-8210-4011","last_name":"Schneider","first_name":"Stefan Balthasar","full_name":"Schneider, Stefan Balthasar","id":"35343"}],"status":"public","year":"2022","title":"Network and Service Coordination: Conventional and Machine Learning Approaches\"","date_updated":"2022-02-18T08:17:36Z","_id":"29672","language":[{"iso":"eng"}],"doi":"10.17619/UNIPB/1-1276 ","user_id":"15504","supervisor":[{"first_name":"Karl","last_name":"Holger","full_name":"Holger, Karl"}],"citation":{"ieee":"S. B. Schneider, <i>Network and Service Coordination: Conventional and Machine Learning Approaches\"</i>. 2022.","apa":"Schneider, S. B. (2022). <i>Network and Service Coordination: Conventional and Machine Learning Approaches\"</i>. <a href=\"https://doi.org/10.17619/UNIPB/1-1276 \">https://doi.org/10.17619/UNIPB/1-1276 </a>","short":"S.B. Schneider, Network and Service Coordination: Conventional and Machine Learning Approaches\", 2022.","chicago":"Schneider, Stefan Balthasar. <i>Network and Service Coordination: Conventional and Machine Learning Approaches\"</i>, 2022. <a href=\"https://doi.org/10.17619/UNIPB/1-1276 \">https://doi.org/10.17619/UNIPB/1-1276 </a>.","mla":"Schneider, Stefan Balthasar. <i>Network and Service Coordination: Conventional and Machine Learning Approaches\"</i>. 2022, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-1276 \">10.17619/UNIPB/1-1276 </a>.","bibtex":"@book{Schneider_2022, title={Network and Service Coordination: Conventional and Machine Learning Approaches\"}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-1276 \">10.17619/UNIPB/1-1276 </a>}, author={Schneider, Stefan Balthasar}, year={2022} }","ama":"Schneider SB. <i>Network and Service Coordination: Conventional and Machine Learning Approaches\"</i>.; 2022. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-1276 \">10.17619/UNIPB/1-1276 </a>"},"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"date_created":"2022-01-31T07:08:47Z","department":[{"_id":"75"}],"type":"dissertation"},{"citation":{"bibtex":"@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}, year={2022} }","ama":"Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. In: <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE; 2022.","mla":"Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>, IEEE, 2022.","chicago":"Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker, and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” In <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE, 2022.","short":"S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.","ieee":"S. B. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.","apa":"Schneider, S. B., Werner, S., Khalili, R., Hecker, A., &#38; Karl, H. (2022). mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest."},"file_date_updated":"2022-03-10T18:25:41Z","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"quality_controlled":"1","oa":"1","conference":{"location":"Budapest","name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","start_date":"2022-04-25","end_date":"2022-04-29"},"status":"public","has_accepted_license":"1","_id":"30236","publisher":"IEEE","ddc":["004"],"user_id":"35343","publication":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","abstract":[{"text":"Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive\r\nresults. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of\r\nwireless mobile networks.","lang":"eng"}],"date_created":"2022-03-10T18:28:14Z","file":[{"creator":"stschn","date_created":"2022-03-10T18:25:41Z","access_level":"open_access","file_size":223412,"file_name":"author_version.pdf","date_updated":"2022-03-10T18:25:41Z","relation":"main_file","content_type":"application/pdf","file_id":"30237"}],"department":[{"_id":"75"}],"type":"conference","keyword":["wireless mobile networks","network management","continuous control","cognitive networks","autonomous coordination","reinforcement learning","gym environment","simulation","open source"],"author":[{"full_name":"Schneider, Stefan Balthasar","first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011","last_name":"Schneider","id":"35343"},{"full_name":"Werner, Stefan","first_name":"Stefan","last_name":"Werner"},{"first_name":"Ramin","last_name":"Khalili","full_name":"Khalili, Ramin"},{"full_name":"Hecker, Artur","first_name":"Artur","last_name":"Hecker"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"year":"2022","title":"mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks","date_updated":"2022-03-10T18:28:19Z","language":[{"iso":"eng"}]},{"department":[{"_id":"75"}],"type":"conference","date_created":"2022-08-15T09:59:17Z","project":[{"_id":"16","name":"SFB 901 - C4: SFB 901 - Subproject C4"},{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"}],"abstract":[{"text":"The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\\Delta(n)$ is reset to one; otherwise, $\\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\\Delta(n)$ is itself $\\alpha$-mixing from which we conclude a strong law of large numbers for $\\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times.","lang":"eng"}],"citation":{"ama":"Redder A, Ramaswamy A, Karl H. Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law. In: <i>Proceedings of the 58th Allerton Conference on Communication, Control, and Computing</i>. ; 2022.","bibtex":"@inproceedings{Redder_Ramaswamy_Karl_2022, title={Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law}, booktitle={Proceedings of the 58th Allerton Conference on Communication, Control, and Computing}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }","mla":"Redder, Adrian, et al. “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law.” <i>Proceedings of the 58th Allerton Conference on Communication, Control, and Computing</i>, 2022.","chicago":"Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law.” In <i>Proceedings of the 58th Allerton Conference on Communication, Control, and Computing</i>, 2022.","short":"A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022.","apa":"Redder, A., Ramaswamy, A., &#38; Karl, H. (2022). Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law. <i>Proceedings of the 58th Allerton Conference on Communication, Control, and Computing</i>. 58th Allerton Conference on Communication, Control, and Computing.","ieee":"A. Redder, A. Ramaswamy, and H. Karl, “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law,” presented at the 58th Allerton Conference on Communication, Control, and Computing, 2022."},"publication":"Proceedings of the 58th Allerton Conference on Communication, Control, and Computing","ddc":["000"],"user_id":"477","_id":"32811","language":[{"iso":"eng"}],"has_accepted_license":"1","date_updated":"2022-11-18T09:31:19Z","conference":{"name":"58th Allerton Conference on Communication, Control, and Computing"},"author":[{"id":"52265","full_name":"Redder, Adrian","orcid":"https://orcid.org/0000-0001-7391-4688","last_name":"Redder","first_name":"Adrian"},{"last_name":"Ramaswamy","first_name":"Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan","id":"66937"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"status":"public","year":"2022","title":"Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law"},{"has_accepted_license":"1","status":"public","user_id":"477","ddc":["006"],"_id":"30793","publisher":"SCITEPRESS - Science and Technology Publications","project":[{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"},{"_id":"24","name":"NICCI-CN: Netzgewahre Regelung & regelungsgewahre Netze"},{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"}],"citation":{"chicago":"Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” In <i>Proceedings of the 14th International Conference on Agents and Artificial Intelligence</i>. SCITEPRESS - Science and Technology Publications, 2022. <a href=\"https://doi.org/10.5220/0010845400003116\">https://doi.org/10.5220/0010845400003116</a>.","short":"A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 14th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2022.","ieee":"A. Redder, A. Ramaswamy, and H. Karl, “Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication,” 2022, doi: <a href=\"https://doi.org/10.5220/0010845400003116\">10.5220/0010845400003116</a>.","apa":"Redder, A., Ramaswamy, A., &#38; Karl, H. (2022). Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. <i>Proceedings of the 14th International Conference on Agents and Artificial Intelligence</i>. <a href=\"https://doi.org/10.5220/0010845400003116\">https://doi.org/10.5220/0010845400003116</a>","bibtex":"@inproceedings{Redder_Ramaswamy_Karl_2022, title={Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}, DOI={<a href=\"https://doi.org/10.5220/0010845400003116\">10.5220/0010845400003116</a>}, booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence}, publisher={SCITEPRESS - Science and Technology Publications}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }","ama":"Redder A, Ramaswamy A, Karl H. Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. In: <i>Proceedings of the 14th International Conference on Agents and Artificial Intelligence</i>. SCITEPRESS - Science and Technology Publications; 2022. doi:<a href=\"https://doi.org/10.5220/0010845400003116\">10.5220/0010845400003116</a>","mla":"Redder, Adrian, et al. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” <i>Proceedings of the 14th International Conference on Agents and Artificial Intelligence</i>, SCITEPRESS - Science and Technology Publications, 2022, doi:<a href=\"https://doi.org/10.5220/0010845400003116\">10.5220/0010845400003116</a>."},"file_date_updated":"2022-08-31T07:10:13Z","publication_status":"published","date_updated":"2022-11-18T09:32:14Z","author":[{"full_name":"Redder, Adrian","orcid":"https://orcid.org/0000-0001-7391-4688","last_name":"Redder","first_name":"Adrian","id":"52265"},{"id":"66937","full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","last_name":"Ramaswamy","first_name":"Arunselvan"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"title":"Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication","year":"2022","doi":"10.5220/0010845400003116","language":[{"iso":"eng"}],"publication":"Proceedings of the 14th International Conference on Agents and Artificial Intelligence","department":[{"_id":"75"}],"type":"conference","date_created":"2022-04-06T07:18:36Z","file":[{"access_level":"closed","file_size":298926,"file_name":"ICCART2022.pdf","date_updated":"2022-08-31T07:10:13Z","relation":"main_file","content_type":"application/pdf","success":1,"file_id":"33237","creator":"aredder","date_created":"2022-08-31T07:10:13Z"}]},{"department":[{"_id":"75"}],"type":"preprint","date_created":"2022-04-06T06:53:38Z","external_id":{"arxiv":["2201.11343"]},"project":[{"_id":"16","name":"SFB 901 - C4: SFB 901 - Subproject C4"},{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"}],"abstract":[{"lang":"eng","text":"Iterative distributed optimization algorithms involve multiple agents that\r\ncommunicate with each other, over time, in order to minimize/maximize a global\r\nobjective. In the presence of unreliable communication networks, the\r\nAge-of-Information (AoI), which measures the freshness of data received, may be\r\nlarge and hence hinder algorithmic convergence. In this paper, we study the\r\nconvergence of general distributed gradient-based optimization algorithms in\r\nthe presence of communication that neither happens periodically nor at\r\nstochastically independent points in time. We show that convergence is\r\nguaranteed provided the random variables associated with the AoI processes are\r\nstochastically dominated by a random variable with finite first moment. This\r\nimproves on previous requirements of boundedness of more than the first moment.\r\nWe then introduce stochastically strongly connected (SSC) networks, a new\r\nstochastic form of strong connectedness for time-varying networks. We show: If\r\nfor any $p \\ge0$ the processes that describe the success of communication\r\nbetween agents in a SSC network are $\\alpha$-mixing with $n^{p-1}\\alpha(n)$\r\nsummable, then the associated AoI processes are stochastically dominated by a\r\nrandom variable with finite $p$-th moment. In combination with our first\r\ncontribution, this implies that distributed stochastic gradient descend\r\nconverges in the presence of AoI, if $\\alpha(n)$ is summable."}],"citation":{"bibtex":"@article{Redder_Ramaswamy_Karl_2022, title={Distributed gradient-based optimization in the presence of dependent  aperiodic communication}, journal={arXiv:2201.11343}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }","ama":"Redder A, Ramaswamy A, Karl H. Distributed gradient-based optimization in the presence of dependent  aperiodic communication. <i>arXiv:220111343</i>. Published online 2022.","mla":"Redder, Adrian, et al. “Distributed Gradient-Based Optimization in the Presence of Dependent  Aperiodic Communication.” <i>ArXiv:2201.11343</i>, 2022.","chicago":"Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Distributed Gradient-Based Optimization in the Presence of Dependent  Aperiodic Communication.” <i>ArXiv:2201.11343</i>, 2022.","short":"A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.11343 (2022).","ieee":"A. Redder, A. Ramaswamy, and H. Karl, “Distributed gradient-based optimization in the presence of dependent  aperiodic communication,” <i>arXiv:2201.11343</i>. 2022.","apa":"Redder, A., Ramaswamy, A., &#38; Karl, H. (2022). Distributed gradient-based optimization in the presence of dependent  aperiodic communication. In <i>arXiv:2201.11343</i>."},"publication":"arXiv:2201.11343","user_id":"477","_id":"30790","language":[{"iso":"eng"}],"date_updated":"2022-11-18T09:33:01Z","author":[{"id":"52265","last_name":"Redder","first_name":"Adrian","orcid":"https://orcid.org/0000-0001-7391-4688","full_name":"Redder, Adrian"},{"id":"66937","first_name":"Arunselvan","last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan"},{"last_name":"Karl","first_name":"Holger","full_name":"Karl, Holger","id":"126"}],"status":"public","title":"Distributed gradient-based optimization in the presence of dependent  aperiodic communication","year":"2022"},{"department":[{"_id":"75"}],"type":"preprint","date_created":"2022-04-06T06:53:52Z","external_id":{"arxiv":["2201.00570"]},"project":[{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"},{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"}],"abstract":[{"text":"We present sufficient conditions that ensure convergence of the multi-agent\r\nDeep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of\r\nthe most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling\r\ncontinuous action spaces: the actor-critic paradigm. In the setting considered\r\nherein, each agent observes a part of the global state space in order to take\r\nlocal actions, for which it receives local rewards. For every agent, DDPG\r\ntrains a local actor (policy) and a local critic (Q-function). The analysis\r\nshows that multi-agent DDPG using neural networks to approximate the local\r\npolicies and critics converge to limits with the following properties: The\r\ncritic limits minimize the average squared Bellman loss; the actor limits\r\nparameterize a policy that maximizes the local critic's approximation of\r\n$Q_i^*$, where $i$ is the agent index. The averaging is with respect to a\r\nprobability distribution over the global state-action space. It captures the\r\nasymptotics of all local training processes. Finally, we extend the analysis to\r\na fully decentralized setting where agents communicate over a wireless network\r\nprone to delays and losses; a typical scenario in, e.g., robotic applications.","lang":"eng"}],"citation":{"chicago":"Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.” <i>ArXiv:2201.00570</i>, 2022.","short":"A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.00570 (2022).","ieee":"A. Redder, A. Ramaswamy, and H. Karl, “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms,” <i>arXiv:2201.00570</i>. 2022.","apa":"Redder, A., Ramaswamy, A., &#38; Karl, H. (2022). Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. In <i>arXiv:2201.00570</i>.","bibtex":"@article{Redder_Ramaswamy_Karl_2022, title={Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms}, journal={arXiv:2201.00570}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }","ama":"Redder A, Ramaswamy A, Karl H. Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. <i>arXiv:220100570</i>. Published online 2022.","mla":"Redder, Adrian, et al. “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.” <i>ArXiv:2201.00570</i>, 2022."},"publication":"arXiv:2201.00570","user_id":"477","_id":"30791","language":[{"iso":"eng"}],"date_updated":"2022-11-18T09:33:42Z","author":[{"orcid":"https://orcid.org/0000-0001-7391-4688","first_name":"Adrian","last_name":"Redder","full_name":"Redder, Adrian","id":"52265"},{"id":"66937","full_name":"Ramaswamy, Arunselvan","last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","first_name":"Arunselvan"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"year":"2022","title":"Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms","status":"public"},{"citation":{"chicago":"Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Practical Network Conditions for the Convergence of Distributed Optimization.” <i>IFAC-PapersOnLine</i> 55, no. 13 (2022): 133–138.","short":"A. Redder, A. Ramaswamy, H. Karl, IFAC-PapersOnLine 55 (2022) 133–138.","apa":"Redder, A., Ramaswamy, A., &#38; Karl, H. (2022). Practical Network Conditions for the Convergence of Distributed Optimization. <i>IFAC-PapersOnLine</i>, <i>55</i>(13), 133–138.","ieee":"A. Redder, A. Ramaswamy, and H. Karl, “Practical Network Conditions for the Convergence of Distributed Optimization,” <i>IFAC-PapersOnLine</i>, vol. 55, no. 13, pp. 133–138, 2022.","ama":"Redder A, Ramaswamy A, Karl H. Practical Network Conditions for the Convergence of Distributed Optimization. <i>IFAC-PapersOnLine</i>. 2022;55(13):133–138.","bibtex":"@article{Redder_Ramaswamy_Karl_2022, title={Practical Network Conditions for the Convergence of Distributed Optimization}, volume={55}, number={13}, journal={IFAC-PapersOnLine}, publisher={Elsevier}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022}, pages={133–138} }","mla":"Redder, Adrian, et al. “Practical Network Conditions for the Convergence of Distributed Optimization.” <i>IFAC-PapersOnLine</i>, vol. 55, no. 13, Elsevier, 2022, pp. 133–138."},"file_date_updated":"2022-08-31T07:06:30Z","project":[{"_id":"16","name":"SFB 901 - C4: SFB 901 - Subproject C4"},{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"}],"conference":{"name":"IFAC Conference on Networked Systems"},"status":"public","has_accepted_license":"1","publisher":"Elsevier","_id":"32854","page":"133–138","volume":55,"ddc":["006"],"user_id":"477","publication":"IFAC-PapersOnLine","issue":"13","date_created":"2022-08-16T09:12:55Z","file":[{"creator":"aredder","date_created":"2022-08-31T07:06:30Z","access_level":"closed","file_size":298395,"file_name":"NecSys2022____Practical_Conditions_for_Conv.pdf","date_updated":"2022-08-31T07:06:30Z","relation":"main_file","content_type":"application/pdf","success":1,"file_id":"33236"}],"department":[{"_id":"75"}],"type":"journal_article","author":[{"first_name":"Adrian","last_name":"Redder","orcid":"https://orcid.org/0000-0001-7391-4688","full_name":"Redder, Adrian","id":"52265"},{"full_name":"Ramaswamy, Arunselvan","first_name":"Arunselvan","last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","id":"66937"},{"full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger","id":"126"}],"title":"Practical Network Conditions for the Convergence of Distributed Optimization","year":"2022","intvolume":"        55","date_updated":"2022-11-18T10:05:14Z","language":[{"iso":"eng"}]}]