{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:25Z","timestamp":1750220005048,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2022,1,7]]},"abstract":"<jats:p>Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware\/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.<\/jats:p>","DOI":"10.1145\/3511285.3511294","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T23:15:51Z","timestamp":1641942951000},"page":"28-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning"],"prefix":"10.1145","volume":"25","author":[{"given":"Francesco","family":"Restuccia","sequence":"first","affiliation":[{"name":"Wireless Internet of Things Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommaso","family":"Melodia","sequence":"additional","affiliation":[{"name":"Wireless Internet of Things Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Global Mobile Data Traffic Forecast Update","author":"Systems Cisco","year":"2016","unstructured":"Cisco Systems , \" Cisco Visual Networking Index : Global Mobile Data Traffic Forecast Update , 2016 - 2021 White Paper .\" 2017. http:\/\/tinyurl.com\/zzo6766. Cisco Systems, \"Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016- 2021 White Paper.\" 2017. http:\/\/tinyurl.com\/zzo6766."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIE.2017.2724579"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/98.788210"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/icc.2011.5962467"},{"volume-title":"Proceedings of ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec).","author":"Vo-Huu T.D.","key":"e_1_2_1_5_1","unstructured":"T.D. Vo-Huu , T.D. Vo-Huu , and G. Noubir . 2016. Interleaving jamming in Wi-Fi Networks . Proceedings of ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec). T.D. Vo-Huu, T.D. Vo-Huu, and G. Noubir. 2016. Interleaving jamming in Wi-Fi Networks. Proceedings of ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec)."},{"volume-title":"Proceedings of IEEE Annual Wireless and Microwave Technology Conference (WAMICON).","author":"Subramaniam S.","key":"e_1_2_1_6_1","unstructured":"S. Subramaniam , H. Reyes , and N. Kaabouch , 2015. Spectrum occupancy measurement: An autocorrelation based scanning technique using USRP . Proceedings of IEEE Annual Wireless and Microwave Technology Conference (WAMICON). S. Subramaniam, H. Reyes, and N. Kaabouch, 2015. Spectrum occupancy measurement: An autocorrelation based scanning technique using USRP. Proceedings of IEEE Annual Wireless and Microwave Technology Conference (WAMICON)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2364316"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2823314"},{"volume-title":"Proceedings of IEEE Conference on Computer Communications (INFOCOM).","author":"Xiong W.","key":"e_1_2_1_9_1","unstructured":"W. Xiong , P. Bogdanov , and M. Zheleva . 2019. Robust and efficient modulation recognition based on local sequential IQ features . Proceedings of IEEE Conference on Computer Communications (INFOCOM). W. Xiong, P. Bogdanov, and M. Zheleva. 2019. Robust and efficient modulation recognition based on local sequential IQ features. Proceedings of IEEE Conference on Computer Communications (INFOCOM)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2846401"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2797022"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2017.2758370"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2818794"},{"volume-title":"Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).","author":"Karra K.","key":"e_1_2_1_14_1","unstructured":"K. Karra , S. Kuzdeba , and J. Petersen . 2017. Modulation recognition using hierarchical deep neural networks . In Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). K. Karra, S. Kuzdeba, and J. Petersen. 2017. Modulation recognition using hierarchical deep neural networks. In Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)."},{"volume-title":"Proceedings of IEEE Conference on Computer Communications (INFOCOM).","author":"Restuccia F.","key":"e_1_2_1_15_1","unstructured":"F. Restuccia and T. Melodia , 2019. Big data goes small: Real-time spectrum-driven embedded wireless networking through deep learning in the RF loop . Proceedings of IEEE Conference on Computer Communications (INFOCOM). F. Restuccia and T. Melodia, 2019. Big data goes small: Real-time spectrum-driven embedded wireless networking through deep learning in the RF loop. Proceedings of IEEE Conference on Computer Communications (INFOCOM)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2846040"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000243"},{"volume-title":"Proceedings of the International Conference on Engineering Applications of Neural Networks. Springer, 213--226","author":"O'Shea T.J.","key":"e_1_2_1_18_1","unstructured":"T.J. O'Shea , J. Corgan , and T.C. Clancy . 2016. Convolutional radio modulation recognition networks . Proceedings of the International Conference on Engineering Applications of Neural Networks. Springer, 213--226 . T.J. O'Shea, J. Corgan, and T.C. Clancy. 2016. Convolutional radio modulation recognition networks. Proceedings of the International Conference on Engineering Applications of Neural Networks. Springer, 213--226."},{"volume-title":"Proceedings of IEEE Conference on Computer Communications (INFOCOM).","author":"Restuccia F.","key":"e_1_2_1_19_1","unstructured":"F. Restuccia and T. Melodia , 2020. DeepWiERL: Bringing deep reinforcement learning to the internet of self-adaptive things . Proceedings of IEEE Conference on Computer Communications (INFOCOM). F. Restuccia and T. Melodia, 2020. DeepWiERL: Bringing deep reinforcement learning to the internet of self-adaptive things. Proceedings of IEEE Conference on Computer Communications (INFOCOM)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2010.2041805"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2011.2159000"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2012.021712.100638"},{"volume-title":"Proceedings of International Conference on Signal Processing, Communication and Networking (ICSCN).","author":"Ghodeswar S.","key":"e_1_2_1_23_1","unstructured":"S. Ghodeswar and P.G. Poonacha . 2015. An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals . Proceedings of International Conference on Signal Processing, Communication and Networking (ICSCN). S. Ghodeswar and P.G. Poonacha. 2015. An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals. Proceedings of International Conference on Signal Processing, Communication and Networking (ICSCN)."},{"key":"e_1_2_1_24_1","unstructured":"Analog Devices Incorporated. 2018. AD9361 RF agile transceiver data sheet http:\/\/www. analog.com\/media\/en\/technical-documentation\/ data-sheets\/AD9361.pdf.  Analog Devices Incorporated. 2018. AD9361 RF agile transceiver data sheet http:\/\/www. analog.com\/media\/en\/technical-documentation\/ data-sheets\/AD9361.pdf."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2766581"},{"key":"e_1_2_1_26_1","volume-title":"Overview","author":"Xilinx Inc.","year":"2018","unstructured":"Xilinx Inc. , \"Zynq-7000 So C Data Sheet : Overview .\" 2018 . https:\/\/www.xilinx.com\/ support\/documentation\/data_sheets\/ds190- Zynq- 7000-Overview.pdf. Xilinx Inc., \"Zynq-7000 SoC Data Sheet: Overview.\" 2018. https:\/\/www.xilinx.com\/ support\/documentation\/data_sheets\/ds190- Zynq-7000-Overview.pdf."},{"key":"e_1_2_1_27_1","unstructured":"Xilinx Inc. \"Zynq-7000 SoC Data Sheet \n   https:\/\/www.xilinx.com\/support\/ documentation\/boards_and_ kits\/zc706\/ug954- zc706- eval- board- xc7z045- ap- soc.pdf.  -- \"ZC706 Evaluation Board for the Zynq- 7000 XC7Z045 All Programmable SoC User Guide \" 2018. https:\/\/www.xilinx.com\/support\/ documentation\/boards_and_ kits\/zc706\/ug954- zc706- eval- board- xc7z045- ap- soc.pdf."},{"volume-title":"Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 271--280","author":"Restuccia F.","key":"e_1_2_1_28_1","unstructured":"F. Restuccia and T. Melodia . 2020. PolymoRF: Polymorphic wireless receivers through physical- layer deep learning . Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 271--280 . F. Restuccia and T. Melodia. 2020. PolymoRF: Polymorphic wireless receivers through physical- layer deep learning. Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 271--280."}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511285.3511294","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511285.3511294","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:03Z","timestamp":1750182663000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511285.3511294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,1,7]]}},"alternative-id":["10.1145\/3511285.3511294"],"URL":"https:\/\/doi.org\/10.1145\/3511285.3511294","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"type":"print","value":"2375-0529"},{"type":"electronic","value":"2375-0537"}],"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"2022-01-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}