{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:48Z","timestamp":1750309608957,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2150010"],"award-info":[{"award-number":["CNS-2150010"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,31]]},"DOI":"10.1145\/3672608.3707804","type":"proceedings-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T18:30:17Z","timestamp":1747247417000},"page":"263-270","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["WMN-CDA: Contrastive Domain Adaptation for Wireless Mesh Network Configuration"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7116-9529","authenticated-orcid":false,"given":"Aitian","family":"Ma","sequence":"first","affiliation":[{"name":"Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2701-0159","authenticated-orcid":false,"given":"Mo","family":"Sha","sequence":"additional","affiliation":[{"name":"Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"8","volume-title":"IEEE","author":"Milic B.","year":"2014","unstructured":"B. Milic, S. Brack, and R. Naumann, \"Hierarchical configuration system for wireless mesh networks in manufacturing industry,\" in IFIP Wireless and Mobile Networking Conference (WMNC). IEEE, 2014, pp. 1\u20138."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2015.2507596"},{"key":"e_1_3_2_1_3_1","first-page":"1","article-title":"Performance evaluation of wireless mesh networks in smart cities scenarios","author":"Silva C.","year":"2018","unstructured":"C. Silva, Y. Oliveira, C. Celes, R. Braga, and C. Oliveira, \"Performance evaluation of wireless mesh networks in smart cities scenarios,\" in Proceedings of the Euro American Conference on Telematics and Information Systems, 2018, pp. 1\u20137.","journal-title":"Proceedings of the Euro American Conference on Telematics and Information Systems"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2005.1509968"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2497161"},{"key":"e_1_3_2_1_6_1","volume-title":"Adapting wireless mesh network configuration from simulation to reality via deep learning based domain adaptation,\" in USENIX Symposium on Networked Systems Design and Implementation (NSDI)","author":"Shi J.","year":"2021","unstructured":"J. Shi, M. Sha, and X. Peng, \"Adapting wireless mesh network configuration from simulation to reality via deep learning based domain adaptation,\" in USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2021."},{"key":"e_1_3_2_1_7_1","volume-title":"EWSN","author":"Cheng X.","year":"2024","unstructured":"X. Cheng, M. Sha, and D. Chen, \"Configuring industrial wireless mesh networks via multi-source domain adaptation,\" in ACM International Conference on Embedded Wireless Systems and Networks (EWSN). EWSN, 2024."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3335346"},{"key":"e_1_3_2_1_9_1","volume-title":"Analysis of representations for domain adaptation,\" Advances in neural information processing systems","author":"Ben-David S.","year":"2006","unstructured":"S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, \"Analysis of representations for domain adaptation,\" Advances in neural information processing systems, vol. 19, 2006."},{"volume-title":"A reliable real-time communication technology for industrial applications,\" Wireless Communications and Networking Conference (WCNC)","year":"2012","key":"e_1_3_2_1_10_1","unstructured":"WirelessHART, \"Wirelesshart networks: A reliable real-time communication technology for industrial applications,\" Wireless Communications and Networking Conference (WCNC), 2012."},{"volume-title":"Deep reinforcement learning for dynamic multichannel access in wireless networks,\" IEEE transactions on cognitive communications and networking","author":"Wang S.","key":"e_1_3_2_1_11_1","unstructured":"S. Wang, H. Liu, P. H. Gomes, and B. Krishnamachari, \"Deep reinforcement learning for dynamic multichannel access in wireless networks,\" IEEE transactions on cognitive communications and networking, vol. 4, no. 2, pp. 257\u2013265, 2018."},{"key":"e_1_3_2_1_12_1","first-page":"666","volume-title":"IEEE","author":"Shi J.","year":"2019","unstructured":"J. Shi and M. Sha, \"Parameter self-configuration and self-adaptation in industrial wireless sensor-actuator networks,\" in IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019, pp. 658\u2013666."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3388240"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2006.879347"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2004.06.005"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2011.08.007"},{"volume-title":"Deep-reinforcement learning multiple access for heterogeneous wireless networks,\" IEEE journal on selected areas in communications","author":"Yu Y.","key":"e_1_3_2_1_17_1","unstructured":"Y. Yu, T. Wang, and S. C. Liew, \"Deep-reinforcement learning multiple access for heterogeneous wireless networks,\" IEEE journal on selected areas in communications, vol. 37, no. 6, pp. 1277\u20131290, 2019."},{"key":"e_1_3_2_1_18_1","volume-title":"Early Access","author":"Huang L.","year":"2020","unstructured":"L. Huang, S. Bi, and Y. J. Zhang, \"Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks,\" IEEE TMC, vol. Early Access, 2020."},{"key":"e_1_3_2_1_19_1","volume-title":"Correcting sample selection bias by unlabeled data,\" Advances in Neural Information Processing Systems (NeurIPS)","author":"Huang J.","year":"2007","unstructured":"J. Huang, A. Gretton, K. M. Borgwardt, B. Sch\u00f6lkopf, and A. J. Smola, \"Correcting sample selection bias by unlabeled data,\" Advances in Neural Information Processing Systems (NeurIPS), 2007."},{"key":"e_1_3_2_1_20_1","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton A.","year":"2012","unstructured":"A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch\u00f6lkopf, and A. J. Smola, \"A kernel two-sample test,\" Journal of Machine Learning Research, vol. 13, no. Mar, pp. 723\u2013773, 2012.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_21_1","first-page":"9","volume-title":"IEEE","author":"Ma A.","year":"2024","unstructured":"A. Ma, J. C. T. Rodriguez, and M. Sha, \"Enabling reliable environmental sensing with lora, energy harvesting, and domain adaptation,\" in 2024 33rd International Conference on Computer Communications and Networks (ICCCN). IEEE, 2024, pp. 1\u20139."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_24_1","volume-title":"Parametric augmentation for time series contrastive learning,\" in International Conference on Learning Representations (ICLR)","author":"Zheng X.","year":"2024","unstructured":"X. Zheng, T. Wang, W. Cheng, A. Ma, H. Chen, M. Sha, and D. Luo, \"Parametric augmentation for time series contrastive learning,\" in International Conference on Learning Representations (ICLR), 2024."},{"key":"e_1_3_2_1_25_1","volume-title":"32nd Int. Joint Conf. Artif. Intell.(IJCAI)","author":"Zheng X.","year":"2023","unstructured":"X. Zheng and T. Wang, \"Auto tcl: Automated time series contrastive learning with adaptive augmentations,\" in Proc. 32nd Int. Joint Conf. Artif. Intell.(IJCAI), 2023."},{"key":"e_1_3_2_1_26_1","volume-title":"IEEE","author":"Hadsell R.","year":"2006","unstructured":"R. Hadsell, S. Chopra, and Y. LeCun, \"Dimensionality reduction by learning an invariant mapping,\" in IEEE \/ CVF Computer Vision and Pattern Recognition Conference (CVPR). IEEE, 2006."},{"key":"e_1_3_2_1_27_1","volume-title":"Y. Li, and O. Vinyals, \"Representation learning with contrastive predictive coding,\" in Advances in Neural Information Processing Systems (NeurIPS)","author":"A.","year":"2018","unstructured":"A. v. d. Oord, Y. Li, and O. Vinyals, \"Representation learning with contrastive predictive coding,\" in Advances in Neural Information Processing Systems (NeurIPS), 2018."},{"key":"e_1_3_2_1_28_1","volume-title":"A simple framework for contrastive learning of visual representations,\" in International Conference on Machine Learning (ICML)","author":"Chen T.","year":"2020","unstructured":"T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, \"A simple framework for contrastive learning of visual representations,\" in International Conference on Machine Learning (ICML), 2020."},{"key":"e_1_3_2_1_29_1","volume-title":"Momentum contrast for unsupervised visual representation learning,\" in IEEE \/ CVF Computer Vision and Pattern Recognition Conference (CVPR)","author":"He K.","year":"2020","unstructured":"K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, \"Momentum contrast for unsupervised visual representation learning,\" in IEEE \/ CVF Computer Vision and Pattern Recognition Conference (CVPR), 2020."},{"key":"e_1_3_2_1_30_1","volume-title":"Gheshlaghi Azar et al., \"Bootstrap your own latent: A new approach to self-supervised learning,\" in Advances in Neural Information Processing Systems (NeurIPS)","author":"Grill J.-B.","year":"2020","unstructured":"J.-B. Grill, F. Strub, F. Altch\u00e9, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. Gheshlaghi Azar et al., \"Bootstrap your own latent: A new approach to self-supervised learning,\" in Advances in Neural Information Processing Systems (NeurIPS), 2020."},{"key":"e_1_3_2_1_31_1","volume-title":"Antiga et al., \"Pytorch: An imperative style, highperformance deep learning library,\" Advances in Neural Information Processing Systems (NeurIPS)","author":"Paszke A.","year":"2019","unstructured":"A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., \"Pytorch: An imperative style, highperformance deep learning library,\" Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019."},{"key":"e_1_3_2_1_32_1","volume-title":"A method for stochastic optimization,\" International Conference on Learning Representations (ICLR)","author":"Diederik P. K.","year":"2015","unstructured":"P. K. Diederik, \"Adam: A method for stochastic optimization,\" International Conference on Learning Representations (ICLR), 2015."},{"key":"e_1_3_2_1_33_1","first-page":"369","volume-title":"IEEE","author":"Polastre J.","year":"2005","unstructured":"J. Polastre, R. Szewczyk, and D. Culler, \"Telos: Enabling ultra-low power wireless research,\" in Proceedings of the 4th International Symposium on Information Processing in Sensor Networks. IEEE, 2005, pp. 364\u2013369."},{"key":"e_1_3_2_1_34_1","unstructured":"\"NS-3 Shadowing Model.\" [Online]. Available: https:\/\/www.nsnam.org\/docs\/release\/3.10\/doxygen\/classns3_1_1_shadowing_loss_model.html"},{"key":"e_1_3_2_1_35_1","unstructured":"\"Source Code of Cooja \" https:\/\/github.com\/contiki-os\/contiki\/wiki\/An-Introduction-to-Cooja Cooja."},{"key":"e_1_3_2_1_36_1","unstructured":"\"Source Code of TOSSIM \" https:\/\/github.com\/tinyos\/tinyos-main\/tree\/master\/tos\/lib\/tossim TOSSIM."},{"key":"e_1_3_2_1_37_1","unstructured":"\"Source Code of OMNeT \" https:\/\/github.com\/omnetpp\/omnetpp OMNeT."}],"event":{"name":"SAC '25: 40th ACM\/SIGAPP Symposium on Applied Computing","sponsor":["SIGAPP ACM Special Interest Group on Applied Computing"],"location":"Catania International Airport Catania Italy","acronym":"SAC '25"},"container-title":["Proceedings of the 40th ACM\/SIGAPP Symposium on Applied Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3672608.3707804","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3672608.3707804","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3672608.3707804","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:32Z","timestamp":1750298252000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3672608.3707804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":37,"alternative-id":["10.1145\/3672608.3707804","10.1145\/3672608"],"URL":"https:\/\/doi.org\/10.1145\/3672608.3707804","relation":{},"subject":[],"published":{"date-parts":[[2025,3,31]]},"assertion":[{"value":"2025-05-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}