{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T13:00:20Z","timestamp":1769086820948,"version":"3.49.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:00:00Z","timestamp":1671494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Western Norway University of Applied Sciences, Norway"},{"DOI":"10.13039\/501100005632","name":"National Centre for Research and Development","doi-asserted-by":"crossref","award":["NOR\/POLNOR\/CoBotAGV\/0027\/2019-00"],"award-info":[{"award-number":["NOR\/POLNOR\/CoBotAGV\/0027\/2019-00"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>\n            In recent years, intensive research has been conducted to enable people to live more comfortably. Developments in the\n            <jats:bold>Internet of Things (IoT)<\/jats:bold>\n            , big data, and artificial intelligence have taken this type of research to a new level and led to the emergence of the\n            <jats:bold>Internet of Behaviors (IoB)<\/jats:bold>\n            , which analyzes behavioral patterns. However, current IoB technologies are not capable of handling heterogeneous data. While it is quite common to have different formats of sensor data for the same behavioral observation, the use of these different data formats can significantly help to obtain a more accurate classification of the observation. Another limitation is that existing IoB deep learning models rely on inefficient hyperparameter tuning strategies. In this paper, we present an\n            <jats:bold>Advanced Deep Learning framework for IoB (ADLIoB)<\/jats:bold>\n            applied to connected vehicles. Several deep learning architectures are employed in this framework: CNN, Graph CNN (GCNN), and LSTM are used to train sensor data of different formats. In addition, a branch-and-bound technique is used to intelligently select hyperparameters. To validate ADLIoB, experiments were conducted on four databases for connected vehicles. The results clearly show that ADLIoB is superior to the baseline solutions in terms of both accuracy and runtime.\n          <\/jats:p>","DOI":"10.1145\/3526192","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T06:22:27Z","timestamp":1648189347000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Towards an Advanced Deep Learning for the Internet of Behaviors: Application to Connected Vehicles"],"prefix":"10.1145","volume":"19","author":[{"given":"Tinhinane","family":"Mezair","sequence":"first","affiliation":[{"name":"Ecole Nationale Polytechnique, El Harrach, Algeria"}]},{"given":"Youcef","family":"Djenouri","sequence":"additional","affiliation":[{"name":"SINTEF Digital, Oslo, Norway"}]},{"given":"Asma","family":"Belhadi","sequence":"additional","affiliation":[{"name":"Kristiania University College, Oslo, Norway"}]},{"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Brandon University, Brandon, Manitoba, Canada and China Medical University, North District, Taichuing, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-9709","authenticated-orcid":false,"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Western Norway University of Applied Sciences Bergen, Bergen, Norway"}]}],"member":"320","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3066210"},{"issue":"1","key":"e_1_3_2_3_2","article-title":"Distributed probability density based multi-objective routing for Opp-IoT networks enabled by machine learning","author":"Kumar S. P. Ajith","year":"2021","unstructured":"S. P. Ajith Kumar, Siddhant Banyal, Kartik Krishna Bharadwaj, Hardeo Kumar Thakur, and Deepak Kumar Sharma. 2021. Distributed probability density based multi-objective routing for Opp-IoT networks enabled by machine learning. Journal of Intelligent & Fuzzy Systems1\u201313 (2021).","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2019.02.001"},{"issue":"81","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","DOI":"10.1201\/9781003133391-5","article-title":"Security vulnerabilities, challenges, and schemes in IoT-enabled technologies","author":"Amartya Siddhant Banyal","year":"2021","unstructured":"Siddhant Banyal Amartya and Deepak Kumar Sharma. 2021. Security vulnerabilities, challenges, and schemes in IoT-enabled technologies. Blockchain Technology for Data Privacy Management81\u2013108 (2021).","journal-title":"Blockchain Technology for Data Privacy Management"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2981837"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Asma Belhadi Youcef Djenouri Djamel Djenouri and Jerry Chun-Wei Lin. 2020. A recurrent neural network for urban long-term traffic flow forecasting.","DOI":"10.1007\/s10489-020-01716-1"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.08.003"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2021.106892"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3441626"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3114825"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446937"},{"key":"e_1_3_2_13_2","article-title":"Defending against link flooding attacks in Internet of Things: A Bayesian game approach","author":"Chen Xu","year":"2021","unstructured":"Xu Chen, Wei Feng, Yantian Luo, Meng Shen, Ning Ge, and Xianbin Wang. 2021. Defending against link flooding attacks in Internet of Things: A Bayesian game approach. IEEE Internet of Things Journal (2021).","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3311950"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.08.043"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.12.003"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832656"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/1138127.1138128"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2760281"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0248221"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/1689239.1689247"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.01.030"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2986501"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2020.102152"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3032896"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2935463"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.44"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3434776"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100227"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102945"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.03.072"},{"key":"e_1_3_2_32_2","article-title":"Intrusion detection for secure social Internet of Things based on collaborative edge computing: A generative adversarial network-based approach","author":"Nie Laisen","year":"2021","unstructured":"Laisen Nie, Yixuan Wu, Xiaojie Wang, Lei Guo, Guoyin Wang, Xinbo Gao, and Shengtao Li. 2021. 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