{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T10:27:55Z","timestamp":1724840875879},"reference-count":108,"publisher":"Association for Computing Machinery (ACM)","issue":"9","funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2013Brasil (CAPES)\u2013Finance Code 001"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"The development of the Internet of Things (IoT) paradigm and its significant spread as an affordable data source has brought many challenges when pursuing efficient data collection, distribution, and storage. Since such hierarchical logical architecture can be inefficient and costly in many cases, Data Reduction (DR) solutions have arisen to allow data preprocessing before actual transmission. To increase DR performance, researchers are using Artificial Intelligence (AI) techniques and models toward reducing sensed data volume. AI for DR on the edge is investigated in this study in the form of a Systematic Literature Review (SLR) encompassing major issues such as data heterogeneity and AI-based techniques to reduce data, architectures, and contexts of usage. An SLR is conducted to map the state of the art in this area, highlighting the most common challenges and potential research trends in addition to a proposed taxonomy.<\/jats:p>","DOI":"10.1145\/3656338","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T12:18:54Z","timestamp":1712233134000},"page":"1-39","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Intelligent Edge-powered Data Reduction: A Systematic Literature Review"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6268-1196","authenticated-orcid":false,"given":"La\u00e9rcio","family":"Pioli","sequence":"first","affiliation":[{"name":"Federal University of Santa Catarina, Florianopolis, Brazil"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3237-4168","authenticated-orcid":false,"given":"Douglas D. J.","family":"de Macedo","sequence":"additional","affiliation":[{"name":"Federal University of Santa Catarina, Florianopolis, Brazil"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3988-8476","authenticated-orcid":false,"given":"Daniel G.","family":"Costa","sequence":"additional","affiliation":[{"name":"University of Porto, Porto, Portugal"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2312-7042","authenticated-orcid":false,"given":"Mario A. R.","family":"Dantas","sequence":"additional","affiliation":[{"name":"Federal University of Juiz de Fora, Juiz de Fora, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"01017","volume-title":"ITM Web of Conferences","volume":"43","author":"Ahmad Noor","year":"2022","unstructured":"Noor Ahmad and Ali Bou Nassif. 2022. Dimensionality reduction: Challenges and solutions. In ITM Web of Conferences, Vol. 43. EDP Sciences, 01017."},{"issue":"20","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"19524","DOI":"10.1109\/JSEN.2022.3199590","article-title":"An online model to minimize energy consumption of IoT sensors in smart cities","volume":"22","author":"Al-Hawawreh Muna","year":"2022","unstructured":"Muna Al-Hawawreh, Ibrahim Elgendi, and Kumudu Munasinghe. 2022. An online model to minimize energy consumption of IoT sensors in smart cities. IEEE Sensors Journal 22, 20 (2022), 19524\u201319532.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_4_2","first-page":"370","volume-title":"2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC\u201918)","author":"Al-Marridi Abeer Z.","year":"2018","unstructured":"Abeer Z. Al-Marridi, Amr Mohamed, and Aiman Erbad. 2018. Convolutional autoencoder approach for EEG compression and reconstruction in m-health systems. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC\u201918). IEEE, 370\u2013375."},{"key":"e_1_3_2_5_2","first-page":"1","volume-title":"2020 2nd International Conference on Computer and Information Sciences (ICCIS\u201920)","author":"Alahmed Atheer","year":"2020","unstructured":"Atheer Alahmed, Amal Alrasheedi, Maha Alharbi, Norah Alrebdi, Marwan Aleasa, and Tarek Moulahi. 2020. Machine learning for real-time data reduction in cloud of things. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS\u201920). IEEE, 1\u20136."},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.future.2019.02.005","article-title":"An energy efficient IoT data compression approach for edge machine learning","volume":"96","author":"Azar Joseph","year":"2019","unstructured":"Joseph Azar, Abdallah Makhoul, Mahmoud Barhamgi, and Rapha\u00ebl Couturier. 2019. An energy efficient IoT data compression approach for edge machine learning. Future Generation Computer Systems 96 (2019), 168\u2013175.","journal-title":"Future Generation Computer Systems"},{"key":"e_1_3_2_7_2","article-title":"Efficient lossy compression for IoT using SZ and reconstruction with 1D U-net","author":"Azar Joseph","year":"2022","unstructured":"Joseph Azar, Gaby Bou Tayeh, Abdallah Makhoul, and Rapha\u00ebl Couturier. 2022. Efficient lossy compression for IoT using SZ and reconstruction with 1D U-net. Mobile Networks and Applications 27, 3 (2022), 984\u2013996.","journal-title":"Mobile Networks and Applications"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3419634"},{"issue":"1","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/bdcc7010044","article-title":"Disclosing edge intelligence: A systematic meta-survey","volume":"7","author":"Barbuto Vincenzo","year":"2023","unstructured":"Vincenzo Barbuto, Claudio Savaglio, Min Chen, and Giancarlo Fortino. 2023. Disclosing edge intelligence: A systematic meta-survey. Big Data and Cognitive Computing 7, 1 (2023), 44.","journal-title":"Big Data and Cognitive Computing"},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/978-3-031-10551-7_17","volume-title":"International Conference on Computing Science, Communication and Security","author":"Barot Vishal","year":"2022","unstructured":"Vishal Barot and Ritesh Patel. 2022. Achieving energy efficiency in life-logging applications of internet of things using data compression through incorporation of machine learning and edge-cloud architecture. In International Conference on Computing Science, Communication and Security. Springer, 226\u2013243."},{"key":"e_1_3_2_11_2","volume-title":"Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras","author":"Brownlee Jason","year":"2016","unstructured":"Jason Brownlee. 2016. Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras. Machine Learning Mastery."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/1134285.1134500"},{"key":"e_1_3_2_13_2","article-title":"End-to-end image classification and compression with variational autoencoders","author":"Chamain Lahiru D.","year":"2022","unstructured":"Lahiru D. Chamain, Siyu Qi, and Zhi Ding. 2022. End-to-end image classification and compression with variational autoencoders. IEEE Internet of Things Journal 9, 21 (2022), 21916\u201321931.","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"e_1_3_2_14_2","first-page":"3840","article-title":"Machine learning on graphs: A model and comprehensive taxonomy","volume":"23","author":"Chami Ines","year":"2022","unstructured":"Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher R\u00e9, and Kevin Murphy. 2022. Machine learning on graphs: A model and comprehensive taxonomy. Journal of Machine Learning Research 23, 1 (2022), 3840\u20133903.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_15_2","article-title":"A survey of recent advances in edge-computing-powered artificial intelligence of things","author":"Chang Zhuoqing","year":"2021","unstructured":"Zhuoqing Chang, Shubo Liu, Xingxing Xiong, Zhaohui Cai, and Guoqing Tu. 2021. A survey of recent advances in edge-computing-powered artificial intelligence of things. IEEE Internet of Things Journal 8, 18 (2021), 13849\u201313875.","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"e_1_3_2_16_2","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TNNLS.2021.3124370","article-title":"A feature-enriched deep convolutional neural network for JPEG image compression artifacts reduction and its applications","volume":"33","author":"Chen Honggang","year":"2021","unstructured":"Honggang Chen, Xiaohai He, Hong Yang, Linbo Qing, and Qizhi Teng. 2021. A feature-enriched deep convolutional neural network for JPEG image compression artifacts reduction and its applications. IEEE Transactions on Neural Networks and Learning Systems 33, 1 (2021), 430\u2013444.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_17_2","article-title":"Cache-assisted collaborative task offloading and resource allocation strategy: A meta reinforcement learning approach","author":"Chen Shiyou","year":"2022","unstructured":"Shiyou Chen, Lanlan Rui, Zhipeng Gao, Wenjing Li, and Xuesong Qiu. 2022. Cache-assisted collaborative task offloading and resource allocation strategy: A meta reinforcement learning approach. IEEE Internet of Things Journal 9, 20 (2022), 19823\u201319842.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_18_2","article-title":"Deep reinforcement learning for efficient IoT data compression in smart railroad management","author":"Chen Xuan","year":"2023","unstructured":"Xuan Chen, Qixuan Yu, Shuhong Dai, Pengfei Sun, Haichuan Tang, and Long Cheng. 2023. Deep reinforcement learning for efficient IoT data compression in smart railroad management. IEEE Internet of Things Journal (2023).","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1109\/LSP.2021.3090249","article-title":"Enhanced separable convolution network for lightweight jpeg compression artifacts reduction","volume":"28","author":"Chen Zhengxin","year":"2021","unstructured":"Zhengxin Chen, Xiaohai He, Chao Ren, Honggang Chen, and Tingrong Zhang. 2021. Enhanced separable convolution network for lightweight jpeg compression artifacts reduction. IEEE Signal Processing Letters 28 (2021), 1280\u20131284.","journal-title":"IEEE Signal Processing Letters"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100516"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.067"},{"key":"e_1_3_2_22_2","first-page":"1","volume-title":"2022 International Conference for Advancement in Technology (ICONAT\u201922)","author":"Das Soumi","year":"2022","unstructured":"Soumi Das and Payel Guria. 2022. Adaptation of machine learning in fog computing: An analytical approach. In 2022 International Conference for Advancement in Technology (ICONAT\u201922). IEEE, 1\u201311."},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"102225","DOI":"10.1016\/j.bspc.2020.102225","article-title":"A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM","volume":"63","author":"Dasan Evangelin","year":"2021","unstructured":"Evangelin Dasan and Ithayarani Panneerselvam. 2021. A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Biomedical Signal Processing and Control 63 (2021), 102225.","journal-title":"Biomedical Signal Processing and Control"},{"issue":"8","key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","article-title":"Edge intelligence: The confluence of edge computing and artificial intelligence","volume":"7","author":"Deng Shuiguang","year":"2020","unstructured":"Shuiguang Deng, Hailiang Zhao, Weijia Fang, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya. 2020. Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal 7, 8 (2020), 7457\u20137469.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2020.01.006","article-title":"Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective","volume":"59","author":"Ding Ru-Xi","year":"2020","unstructured":"Ru-Xi Ding, Iv\u00e1n Palomares, Xueqing Wang, Guo-Rui Yang, Bingsheng Liu, Yucheng Dong, Enrique Herrera-Viedma, and Francisco Herrera. 2020. Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective. Information Fusion 59 (2020), 84\u2013102.","journal-title":"Information Fusion"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.02.105"},{"key":"e_1_3_2_27_2","first-page":"4086","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Fu Xueyang","year":"2021","unstructured":"Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, and Zheng-Jun Zha. 2021. Learning dual priors for jpeg compression artifacts removal. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 4086\u20134095."},{"key":"e_1_3_2_28_2","first-page":"271","volume-title":"2021 IEEE International Conference on Smart Internet of Things (SmartIoT\u201921)","author":"GabAllah Nada","year":"2021","unstructured":"Nada GabAllah, Ibrahim Farrag, Omar El Nawawy, Ramy Khalil, Hossam Sharara, and Tamer ElBatt. 2021. Towards enabling IoT systems with edge intelligence. In 2021 IEEE International Conference on Smart Internet of Things (SmartIoT\u201921). IEEE, 271\u2013277."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace9120750"},{"key":"e_1_3_2_30_2","first-page":"1","volume-title":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE\u201919)","author":"Ghosh Ananda M.","year":"2019","unstructured":"Ananda M. Ghosh and Katarina Grolinger. 2019. Deep learning: Edge-cloud data analytics for IoT. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE\u201919). IEEE, 1\u20137."},{"issue":"3","key":"e_1_3_2_31_2","first-page":"2191","article-title":"Edge-cloud computing for internet of things data analytics: Embedding intelligence in the edge with deep learning","volume":"17","author":"Ghosh Ananda Mohon","year":"2020","unstructured":"Ananda Mohon Ghosh and Katarina Grolinger. 2020. Edge-cloud computing for internet of things data analytics: Embedding intelligence in the edge with deep learning. IEEE Transactions on Industrial Informatics 17, 3 (2020), 2191\u20132200.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/TIT.1966.1053907","article-title":"Run-length encodings (corresp.)","volume":"12","author":"Golomb Solomon","year":"1966","unstructured":"Solomon Golomb. 1966. Run-length encodings (corresp.). IEEE Transactions on Information Theory 12, 3 (1966), 399\u2013401.","journal-title":"IEEE Transactions on Information Theory"},{"key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.future.2020.06.013","article-title":"Exploring the computational cost of machine learning at the edge for human-centric Internet of Things","volume":"112","author":"G\u00f3mez-Carmona Oihane","year":"2020","unstructured":"Oihane G\u00f3mez-Carmona, Diego Casado-Mansilla, Frank Alexander Kraemer, Diego L\u00f3pez-de Ipi\u00f1a, and Javier Garc\u00eda-Zubia. 2020. Exploring the computational cost of machine learning at the edge for human-centric Internet of Things. Future Generation Computer Systems 112 (2020), 670\u2013683.","journal-title":"Future Generation Computer Systems"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544836"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"49355","DOI":"10.1109\/ACCESS.2021.3069137","article-title":"Edge intelligence for data handling and predictive maintenance in IIOT","volume":"9","author":"Hafeez Taimur","year":"2021","unstructured":"Taimur Hafeez, Lina Xu, and Gavin Mcardle. 2021. Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access 9 (2021), 49355\u201349371.","journal-title":"IEEE Access"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.3389\/fnins.2018.00217","article-title":"A fast, open EEG classification framework based on feature compression and channel ranking","volume":"12","author":"Han Jiuqi","year":"2018","unstructured":"Jiuqi Han, Yuwei Zhao, Hongji Sun, Jiayun Chen, Ang Ke, Gesen Xu, Hualiang Zhang, Jin Zhou, and Changyong Wang. 2018. A fast, open EEG classification framework based on feature compression and channel ranking. Frontiers in Neuroscience 12 (2018), 217.","journal-title":"Frontiers in Neuroscience"},{"issue":"1","key":"e_1_3_2_37_2","first-page":"9","article-title":"A comprehensive survey on interoperability for IIoT: Taxonomy, standards, and future directions","volume":"55","author":"Hazra Abhishek","year":"2021","unstructured":"Abhishek Hazra, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Srirama. 2021. A comprehensive survey on interoperability for IIoT: Taxonomy, standards, and future directions. ACM Computing Surveys 55, 1, Article 9 (Nov. 2021), 35 pages.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5624909"},{"key":"e_1_3_2_39_2","first-page":"395","volume-title":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","author":"Hu Pan","year":"2020","unstructured":"Pan Hu, Junha Im, Zain Asgar, and Sachin Katti. 2020. Starfish: Resilient image compression for AIoT cameras. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 395\u2013408."},{"key":"e_1_3_2_40_2","first-page":"1","volume-title":"2020 IEEE International Conference on Communications (ICC\u201920)","author":"Huang Xiufeng","year":"2020","unstructured":"Xiufeng Huang and Sheng Zhou. 2020. Adaptive transmission for edge learning via training loss estimation. In 2020 IEEE International Conference on Communications (ICC\u201920). IEEE, 1\u20136."},{"issue":"4","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"394","DOI":"10.23919\/JCIN.2022.10005217","article-title":"Importance-aware data pre-processing and device scheduling for multi-channel edge learning","volume":"7","author":"Huang Xiufeng","year":"2022","unstructured":"Xiufeng Huang and Sheng Zhou. 2022. Importance-aware data pre-processing and device scheduling for multi-channel edge learning. Journal of Communications and Information Networks 7, 4 (2022), 394\u2013407.","journal-title":"Journal of Communications and Information Networks"},{"issue":"9","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1109\/JRPROC.1952.273898","article-title":"A method for the construction of minimum-redundancy codes","volume":"40","author":"Huffman David A.","year":"1952","unstructured":"David A. Huffman. 1952. A method for the construction of minimum-redundancy codes. Proceedings of the IRE 40, 9 (1952), 1098\u20131101.","journal-title":"Proceedings of the IRE"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2023.3278497","article-title":"Multibiosensor data sampling and transmission reduction with decision-making for remote patient monitoring in IOMT networks","author":"Idrees Ali Kadhum","year":"2023","unstructured":"Ali Kadhum Idrees, Sara Kadhum Idrees, Tara Ali-Yahiya, and Rapha\u00ebl Couturier. 2023. Multibiosensor data sampling and transmission reduction with decision-making for remote patient monitoring in IOMT networks. IEEE Sensors Journal 23, 13 (2023).","journal-title":"IEEE Sensors Journal"},{"issue":"6","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.1007\/s12652-021-03161-5","article-title":"New fog computing enabled lossless EEG data compression scheme in IoT networks","volume":"13","author":"Idrees Sara Kadhum","year":"2022","unstructured":"Sara Kadhum Idrees and Ali Kadhum Idrees. 2022. New fog computing enabled lossless EEG data compression scheme in IoT networks. Journal of Ambient Intelligence and Humanized Computing 13, 6 (2022), 3257\u20133270.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"9","key":"e_1_3_2_45_2","first-page":"238","article-title":"Survey on IoT data preprocessing","volume":"12","author":"Jane V. A.","year":"2021","unstructured":"V. A. Jane and Dr. L. Arockiamb. 2021. Survey on IoT data preprocessing. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, 9 (2021), 238\u2013244.","journal-title":"Turkish Journal of Computer and Mathematics Education (TURCOMAT)"},{"key":"e_1_3_2_46_2","first-page":"1","volume-title":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC\u201920)","author":"Jankowski Mikolaj","year":"2020","unstructured":"Mikolaj Jankowski, Deniz G\u00fcnd\u00fcz, and Krystian Mikolajczyk. 2020. Joint device-edge inference over wireless links with pruning. In 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC\u201920). IEEE, 1\u20135."},{"key":"e_1_3_2_47_2","first-page":"4997","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Jiang Jiaxi","year":"2021","unstructured":"Jiaxi Jiang, Kai Zhang, and Radu Timofte. 2021. Towards flexible blind JPEG artifacts removal. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 4997\u20135006."},{"key":"e_1_3_2_48_2","article-title":"Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture","author":"Junior Franklin M. Ribeiro","year":"2022","unstructured":"Franklin M. Ribeiro Junior, Reinaldo A. C. Bianchi, Ronaldo C. Prati, Kari Kolehmainen, Juha-Pekka Soininen, and Carlos A. Kamienski. 2022. Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture. Biosystems Engineering 223 (2022), 142\u2013158.","journal-title":"Biosystems Engineering"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/978-3-030-57321-8_29","volume-title":"International Cross-domain Conference for Machine Learning and Knowledge Extraction","author":"Karanika Anna","year":"2020","unstructured":"Anna Karanika, Panagiotis Oikonomou, Kostas Kolomvatsos, and Christos Anagnostopoulos. 2020. An ensemble interpretable machine learning scheme for securing data quality at the edge. In International Cross-domain Conference for Machine Learning and Knowledge Extraction. Springer, 517\u2013534."},{"key":"e_1_3_2_50_2","volume-title":"Guidelines for Performing Systematic Literature Reviews in Software Engineering","author":"Keele Staffs","year":"2007","unstructured":"Staffs Keele et\u00a0al. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report. Technical report, Ver. 2.3 EBSE Technical Report. EBSE."},{"issue":"17","key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.3390\/s21175929","article-title":"An NB-IoT-based edge-of-things framework for energy-efficient image transfer","volume":"21","author":"Khan Sikandar Zulqarnain","year":"2021","unstructured":"Sikandar Zulqarnain Khan, Yannick Le Moullec, and Muhammad Mahtab Alam. 2021. An NB-IoT-based edge-of-things framework for energy-efficient image transfer. Sensors 21, 17 (2021), 5929.","journal-title":"Sensors"},{"issue":"2","key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1504\/IJESMS.2021.115533","article-title":"Intelligent healthcare data segregation using fog computing with internet of things and machine learning","volume":"12","author":"Kishor Amit","year":"2021","unstructured":"Amit Kishor, Chinmay Chakraborty, and Wilson Jeberson. 2021. Intelligent healthcare data segregation using fog computing with internet of things and machine learning. International Journal of Engineering Systems Modelling and Simulation 12, 2\u20133 (2021), 188\u2013194.","journal-title":"International Journal of Engineering Systems Modelling and Simulation"},{"issue":"2004","key":"e_1_3_2_53_2","first-page":"1","article-title":"Procedures for performing systematic reviews","volume":"33","author":"Kitchenham Barbara","year":"2004","unstructured":"Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University 33, 2004 (2004), 1\u201326.","journal-title":"Keele, UK, Keele University"},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.1109\/BigData.2017.8258215","volume-title":"2017 IEEE International Conference on Big Data (Big Data\u201917)","author":"Kurban Hasan","year":"2017","unstructured":"Hasan Kurban, Can Kockan, Mark Jenne, and Mehmet M. Dalkilic. 2017. Improving expectation maximization algorithm over stellar data. In 2017 IEEE International Conference on Big Data (Big Data\u201917). IEEE, 2559\u20132568."},{"issue":"1","key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.32604\/cmc.2022.020027","article-title":"Data traffic reduction with compressed sensing in an AIoT system","volume":"70","author":"Kwon Hye-Min","year":"2022","unstructured":"Hye-Min Kwon, Seng-Phil Hong, Mingoo Kang, and Jeongwook Seo. 2022. Data traffic reduction with compressed sensing in an AIoT system. Computers, Materials, & Continua 70, 1 (2022), 1769\u20131780.","journal-title":"Computers, Materials, & Continua"},{"issue":"1","key":"e_1_3_2_56_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.dcan.2018.10.008","article-title":"Enabling intelligence in fog computing to achieve energy and latency reduction","volume":"5","author":"La Quang Duy","year":"2019","unstructured":"Quang Duy La, Mao V. Ngo, Thinh Quang Dinh, Tony Q. S. Quek, and Hyundong Shin. 2019. Enabling intelligence in fog computing to achieve energy and latency reduction. Digital Communications and Networks 5, 1 (2019), 3\u20139.","journal-title":"Digital Communications and Networks"},{"issue":"22","key":"e_1_3_2_57_2","first-page":"16440","article-title":"Dimension reduction algorithm based on adaptive maximum linear neighborhood selection in edge computing","volume":"8","author":"Li Guangshun","year":"2020","unstructured":"Guangshun Li, Jiabin Cao, Junhua Wu, Xinrong Ren, and Haili Yu. 2020. Dimension reduction algorithm based on adaptive maximum linear neighborhood selection in edge computing. IEEE Internet of Things Journal 8, 22 (2020), 16440\u201316451.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_58_2","article-title":"BARRN: A blind image compression artifact reduction network for industrial IoT systems","author":"Li Jiafeng","year":"2023","unstructured":"Jiafeng Li, Xiaoyu Liu, Yuqi Gao, Li Zhuo, and Jing Zhang. 2023. BARRN: A blind image compression artifact reduction network for industrial IoT systems. IEEE Transactions on Industrial Informatics 19, 9 (2023).","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_3_2_59_2","article-title":"A review and a taxonomy of edge machine learning: Requirements, paradigms, and techniques","author":"Li Wenbin","year":"2023","unstructured":"Wenbin Li, Hakim Hacid, Ebtesam Almazrouei, and Merouane Debbah. 2023. A review and a taxonomy of edge machine learning: Requirements, paradigms, and techniques. arXiv preprint arXiv:2302.08571 (2023).","journal-title":"arXiv preprint arXiv:2302.08571"},{"issue":"5","key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1109\/JIOT.2019.2963635","article-title":"Toward edge-based deep learning in industrial Internet of Things","volume":"7","author":"Liang Fan","year":"2020","unstructured":"Fan Liang, Wei Yu, Xing Liu, David Griffith, and Nada Golmie. 2020. Toward edge-based deep learning in industrial Internet of Things. IEEE Internet of Things Journal 7, 5 (2020), 4329\u20134341.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107121"},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1145\/3528416.3530230","volume-title":"Proceedings of the 19th ACM International Conference on Computing Frontiers","author":"Machidon Alina","year":"2022","unstructured":"Alina Machidon and Veljko Pejovi\u0107. 2022. Enabling resource-efficient edge intelligence with compressive sensing-based deep learning. In Proceedings of the 19th ACM International Conference on Computing Frontiers. 141\u2013149."},{"key":"e_1_3_2_63_2","article-title":"In search of the most efficient and memory-saving visualization of high dimensional data","author":"Minch Bartosz","year":"2023","unstructured":"Bartosz Minch. 2023. In search of the most efficient and memory-saving visualization of high dimensional data. arXiv preprint arXiv:2303.05455 (2023).","journal-title":"arXiv preprint arXiv:2303.05455"},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/CANDAR.2017.13","volume-title":"2017 5th International Symposium on Computing and Networking (CANDAR\u201917)","author":"Mitani Takamasa","year":"2017","unstructured":"Takamasa Mitani, Hisakazu Fukuoka, Yuria Hiraga, Takashi Nakada, and Yasuhiko Nakashima. 2017. Compression and aggregation for optimizing information transmission in distributed CNN. In 2017 5th International Symposium on Computing and Networking (CANDAR\u201917). IEEE, 112\u2013118."},{"issue":"4","key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.3390\/s23041791","article-title":"Deep-reinforcement-learning-based IoT sensor data cleaning framework for enhanced data analytics","volume":"23","author":"Mohammed Alaelddin F. Y.","year":"2023","unstructured":"Alaelddin F. Y. Mohammed, Salman Md Sultan, Joohyung Lee, and Sunhwan Lim. 2023. Deep-reinforcement-learning-based IoT sensor data cleaning framework for enhanced data analytics. Sensors 23, 4 (2023), 1791.","journal-title":"Sensors"},{"issue":"9","key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"e4812","DOI":"10.1002\/dac.4812","article-title":"A fog computing data reduce level to enhance the cloud of things performance","volume":"34","author":"Moulahi Tarek","year":"2021","unstructured":"Tarek Moulahi, Salim El Khediri, Rehan Ullah Khan, and Salah Zidi. 2021. A fog computing data reduce level to enhance the cloud of things performance. International Journal of Communication Systems 34, 9 (2021), e4812.","journal-title":"International Journal of Communication Systems"},{"issue":"1","key":"e_1_3_2_67_2","article-title":"Optimized energy efficient strategy for data reduction between edge devices in cloud-IoT.","volume":"72","author":"Mukherjee Dibyendu","year":"2022","unstructured":"Dibyendu Mukherjee, Shivnath Ghosh, Souvik Pal, D. Akila, N. Z. Jhanjhi, Mehedi Masud, and Mohammed A. AlZain. 2022. Optimized energy efficient strategy for data reduction between edge devices in cloud-IoT. Computers, Materials, & Continua 72, 1 (2022), 125\u2013140.","journal-title":"Computers, Materials, & Continua"},{"key":"e_1_3_2_68_2","first-page":"87","volume-title":"2020 IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT\u201920)","author":"Ndubuaku Maryleen U.","year":"2020","unstructured":"Maryleen U. Ndubuaku, Muhammad K. Ali, Ashiq Anjum, Antonio Liotta, and Stephan Reiff-Marganiec. 2020. Edge-enhanced analytics via latent space dimensionality reduction. In 2020 IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT\u201920). IEEE, 87\u201395."},{"key":"e_1_3_2_69_2","doi-asserted-by":"crossref","DOI":"10.4135\/9781412985000","volume-title":"Meta-ethnography: Synthesizing Qualitative Studies","author":"Noblit George W.","year":"1988","unstructured":"George W. Noblit, R. Dwight Hare, and R. Dwight Hare. 1988. Meta-ethnography: Synthesizing Qualitative Studies. Vol. 11. Sage."},{"key":"e_1_3_2_70_2","first-page":"1","article-title":"Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing","volume":"10","author":"Nwogbaga Nweso Emmanuel","year":"2021","unstructured":"Nweso Emmanuel Nwogbaga, Rohaya Latip, Lilly Suriani Affendey, and Amir Rizaan Abdul Rahiman. 2021. Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing. Journal of Cloud Computing 10 (2021), 1\u201312.","journal-title":"Journal of Cloud Computing"},{"key":"e_1_3_2_71_2","first-page":"1","volume-title":"2021 IEEE International Conference on Pervasive Computing and Communications (PerCom\u201921)","author":"Oikawa Hiroki","year":"2021","unstructured":"Hiroki Oikawa and Masaaki Kondo. 2021. Density-based data selection and management for edge computing. In 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom\u201921). IEEE, 1\u201311."},{"issue":"6","key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3456630","article-title":"On the use of intelligent models towards meeting the challenges of the edge mesh","volume":"54","author":"Oikonomou Panagiotis","year":"2021","unstructured":"Panagiotis Oikonomou, Anna Karanika, Christos Anagnostopoulos, and Kostas Kolomvatsos. 2021. On the use of intelligent models towards meeting the challenges of the edge mesh. ACM Computing Surveys (CSUR) 54, 6 (2021), 1\u201342.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_73_2","article-title":"Systematic Reviews in the Social Sciences: A Practical Guide.","author":"Petticrew M.","year":"2006","unstructured":"M. Petticrew and H. Roberts. 2006. Systematic Reviews in the Social Sciences: A Practical Guide. Malden, MA, USA: Blackwell Publishing CrossRef Google Scholar.","journal-title":"Malden, MA, USA: Blackwell Publishing CrossRef Google Scholar"},{"key":"e_1_3_2_74_2","first-page":"287","volume-title":"International Conference on P2P, Parallel, Grid, Cloud and Internet Computing","author":"Pioli La\u00e9rcio","year":"2019","unstructured":"La\u00e9rcio Pioli, Victor Str\u00f6ele de Andrade Menezes, and Mario Antonio Ribeiro Dantas. 2019. Research characterization on i\/o improvements of storage environments. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. Springer, 287\u2013298."},{"issue":"2","key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"358","DOI":"10.3390\/s24020358","article-title":"Towards an AI-driven data reduction framework for smart city applications","volume":"24","author":"Pioli Laercio","year":"2024","unstructured":"Laercio Pioli, Douglas D. J. de Macedo, Daniel G. Costa, and Mario A. R. Dantas. 2024. Towards an AI-driven data reduction framework for smart city applications. Sensors 24, 2 (2024), 358.","journal-title":"Sensors"},{"key":"e_1_3_2_76_2","article-title":"An overview of data reduction solutions at the edge of IoT systems: A systematic mapping of the literature","author":"Pioli La\u00e9rcio","year":"2022","unstructured":"La\u00e9rcio Pioli, Carina F. Dorneles, Douglas D. J. de Macedo, and Mario A. R. Dantas. 2022. An overview of data reduction solutions at the edge of IoT systems: A systematic mapping of the literature. Computing 104, 8 (2022), 1867\u20131889.","journal-title":"Computing"},{"issue":"19","key":"e_1_3_2_77_2","doi-asserted-by":"crossref","first-page":"14545","DOI":"10.1007\/s00500-020-04804-z","article-title":"Huffman quantization approach for optimized EEG signal compression with transformation technique","volume":"24","author":"Rajasekar P.","year":"2020","unstructured":"P. Rajasekar and M. Pushpalatha. 2020. Huffman quantization approach for optimized EEG signal compression with transformation technique. Soft Computing 24, 19 (2020), 14545\u201314559.","journal-title":"Soft Computing"},{"issue":"2","key":"e_1_3_2_78_2","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1147\/rd.232.0149","article-title":"Arithmetic coding","volume":"23","author":"Rissanen Jorma","year":"1979","unstructured":"Jorma Rissanen and Glen G. Langdon. 1979. Arithmetic coding. IBM Journal of Research and Development 23, 2 (1979), 149\u2013162.","journal-title":"IBM Journal of Research and Development"},{"key":"e_1_3_2_79_2","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.procs.2022.12.267","article-title":"An intelligent data capturing framework to improve condition monitoring and anomaly detection for industrial machines","volume":"217","author":"Robyns Steven","year":"2023","unstructured":"Steven Robyns, Stijn Helsen, Sam Weckx, Sachin Kumar Bhoi, Mohamed El Baghdadi, Omar Hegazy, and Jasper De Smet. 2023. An intelligent data capturing framework to improve condition monitoring and anomaly detection for industrial machines. Procedia Computer Science 217 (2023), 709\u2013719.","journal-title":"Procedia Computer Science"},{"key":"e_1_3_2_80_2","first-page":"1","volume-title":"2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS\u201921)","author":"Rumy S. M. Shahidur Harun","year":"2021","unstructured":"S. M. Shahidur Harun Rumy, Md Ishan Arefin Hossain, Forji Jahan, and Tanjina Tanvin. 2021. An IoT based system with edge intelligence for rice leaf disease detection using machine learning. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS\u201921). IEEE, 1\u20136."},{"key":"e_1_3_2_81_2","doi-asserted-by":"crossref","first-page":"102882","DOI":"10.1016\/j.jnca.2020.102882","article-title":"Fog data management: A vision, challenges, and future directions","volume":"174","author":"Sadri Ali Akbar","year":"2021","unstructured":"Ali Akbar Sadri, Amir Masoud Rahmani, Morteza Saberikamarposhti, and Mehdi Hosseinzadeh. 2021. Fog data management: A vision, challenges, and future directions. Journal of Network and Computer Applications 174 (2021), 102882.","journal-title":"Journal of Network and Computer Applications"},{"key":"e_1_3_2_82_2","doi-asserted-by":"crossref","first-page":"100629","DOI":"10.1016\/j.iot.2022.100629","article-title":"Data reduction in fog computing and internet of things: A systematic literature survey","author":"Sadri Ali Akbar","year":"2022","unstructured":"Ali Akbar Sadri, Amir Masoud Rahmani, Morteza Saberikamarposhti, and Mehdi Hosseinzadeh. 2022. Data reduction in fog computing and internet of things: A systematic literature survey. Internet of Things 20 (2022), 100629.","journal-title":"Internet of Things"},{"key":"e_1_3_2_83_2","doi-asserted-by":"crossref","first-page":"16353","DOI":"10.1109\/ACCESS.2023.3244099","article-title":"Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G","volume":"11","author":"Salh Adeb","year":"2023","unstructured":"Adeb Salh, Razali Ngah, Lukman Audah, Kwang Soon Kim, Qazwan Abdullah, Yahya M. Al-Moliki, Khaled A. Aljaloud, and Hairul Nizam Talib. 2023. Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G. IEEE Access 11 (2023), 16353\u201316367.","journal-title":"IEEE Access"},{"issue":"3","key":"e_1_3_2_84_2","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00607-020-00864-z","article-title":"K-predictions based data reduction approach in WSN for smart agriculture","volume":"103","author":"Salim Christian","year":"2021","unstructured":"Christian Salim and Nathalie Mitton. 2021. K-predictions based data reduction approach in WSN for smart agriculture. Computing 103, 3 (2021), 509\u2013532.","journal-title":"Computing"},{"key":"e_1_3_2_85_2","first-page":"41","volume-title":"2019 IEEE 5th World Forum on Internet of Things (WF-IoT\u201919)","author":"Sarabia-J\u00e1come David","year":"2019","unstructured":"David Sarabia-J\u00e1come, Ignacio Lacalle, Carlos E. Palau, and Manuel Esteve. 2019. Efficient deployment of predictive analytics in edge gateways: Fall detection scenario. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT\u201919). IEEE, 41\u201346."},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.cmpb.2019.03.009","article-title":"Algorithmic clustering based on string compression to extract P300 structure in EEG signals","volume":"176","author":"Sarasa Guillermo","year":"2019","unstructured":"Guillermo Sarasa, Ana Granados, and Francisco B. Rodriguez. 2019. Algorithmic clustering based on string compression to extract P300 structure in EEG signals. Computer Methods and Programs in Biomedicine 176 (2019), 225\u2013235.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_3_2_87_2","first-page":"1","volume-title":"2019 IEEE Global Communications Conference (GLOBECOM\u201919)","author":"Sekine Masatoshi","year":"2019","unstructured":"Masatoshi Sekine and Satoshi Ikada. 2019. LACSLE: Lightweight and adaptive compressed sensing based on deep learning for edge devices. In 2019 IEEE Global Communications Conference (GLOBECOM\u201919). IEEE, 1\u20137."},{"issue":"3","key":"e_1_3_2_88_2","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon Claude Elwood","year":"1948","unstructured":"Claude Elwood Shannon. 1948. A mathematical theory of communication. Bell System Technical Journal 27, 3 (1948), 379\u2013423.","journal-title":"Bell System Technical Journal"},{"issue":"6","key":"e_1_3_2_89_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3459664","article-title":"Machine learning into metaheuristics: A survey and taxonomy","volume":"54","author":"Talbi El-Ghazali","year":"2021","unstructured":"El-Ghazali Talbi. 2021. Machine learning into metaheuristics: A survey and taxonomy. ACM Computing Surveys (CSUR) 54, 6 (2021), 1\u201332.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_90_2","doi-asserted-by":"crossref","first-page":"40969","DOI":"10.1109\/ACCESS.2019.2907808","article-title":"Distributed decomposed data analytics in fog enabled IoT deployments","volume":"7","author":"Taneja Mohit","year":"2019","unstructured":"Mohit Taneja, Nikita Jalodia, and Alan Davy. 2019. Distributed decomposed data analytics in fog enabled IoT deployments. IEEE Access 7 (2019), 40969\u201340981.","journal-title":"IEEE Access"},{"key":"e_1_3_2_91_2","first-page":"1","volume-title":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP\u201920)","author":"Tmamna Jihene","year":"2020","unstructured":"Jihene Tmamna, Emna Ben Ayed, and Mounir Ben Ayed. 2020. Deep learning for internet of things in fog computing: Survey and open issues. In 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP\u201920). IEEE, 1\u20136."},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.04.015"},{"issue":"12","key":"e_1_3_2_93_2","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.3390\/electronics9122035","article-title":"Dimensionality reduction for smart IoT sensors","volume":"9","author":"Viz\u00e1rraga Jorge","year":"2020","unstructured":"Jorge Viz\u00e1rraga, Roberto Casas, \u00c1lvaro Marco, and J. David Buldain. 2020. Dimensionality reduction for smart IoT sensors. Electronics 9, 12 (2020), 2035.","journal-title":"Electronics"},{"issue":"1","key":"e_1_3_2_94_2","first-page":"614","article-title":"Informed machine learning\u2013A taxonomy and survey of integrating prior knowledge into learning systems","volume":"35","author":"Rueden Laura Von","year":"2021","unstructured":"Laura Von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, et\u00a0al. 2021. Informed machine learning\u2013A taxonomy and survey of integrating prior knowledge into learning systems. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 614\u2013633.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"e_1_3_2_95_2","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/COMST.2020.2970550","article-title":"Convergence of edge computing and deep learning: A comprehensive survey","volume":"22","author":"Wang Xiaofei","year":"2020","unstructured":"Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, and Xu Chen. 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials 22, 2 (2020), 869\u2013904.","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3123218"},{"key":"e_1_3_2_97_2","first-page":"1","volume-title":"2019 ACM\/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS\u201919)","author":"Wang Yuqin","year":"2019","unstructured":"Yuqin Wang, Jingce Xu, and Wen Ji. 2019. A feature-based video transmission framework for visual IoT in fog computing systems. In 2019 ACM\/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS\u201919). IEEE, 1\u20138."},{"key":"e_1_3_2_98_2","first-page":"0572","volume-title":"2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC\u201921)","author":"Welagedara Lahiru","year":"2021","unstructured":"Lahiru Welagedara, Janani Harischandra, and Nuwan Jayawardene. 2021. A review on edge intelligence based collaborative learning approaches. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC\u201921). IEEE, 0572\u20130577."},{"key":"e_1_3_2_99_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-29044-2","volume-title":"Experimentation in Software Engineering","author":"Wohlin Claes","year":"2012","unstructured":"Claes Wohlin, Per Runeson, Martin H\u00f6st, Magnus C. Ohlsson, Bj\u00f6rn Regnell, and Anders Wessl\u00e9n. 2012. Experimentation in Software Engineering. Springer Science & Business Media."},{"key":"e_1_3_2_100_2","first-page":"38","volume-title":"The 6th International Conference on Control Engineering and Artificial Intelligence","author":"Wu Hailiang","year":"2022","unstructured":"Hailiang Wu, Yuyun Kang, Baiyang Wang, Dongyue Huo, and Dongping Chen. 2022. A CNN-based fog node data processing method and application in wearable heart detection equipment. In The 6th International Conference on Control Engineering and Artificial Intelligence. 38\u201341."},{"key":"e_1_3_2_101_2","doi-asserted-by":"crossref","DOI":"10.1109\/TII.2023.3245673","article-title":"To transmit or predict: An efficient industrial data transmission scheme with deep learning and cloud-edge collaboration","author":"Wu Yu","year":"2023","unstructured":"Yu Wu, Bo Yang, Dafeng Zhu, Qi Liu, Cheng Li, Cailian Chen, and Xinping Guan. 2023. To transmit or predict: An efficient industrial data transmission scheme with deep learning and cloud-edge collaboration. IEEE Transactions on Industrial Informatics 19, 11 (2023), 11322\u201311332.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"15","key":"e_1_3_2_102_2","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.3390\/electronics12153229","article-title":"Binary-convolution data-reduction network for edge\u2013cloud IIoT anomaly detection","volume":"12","author":"Xie Cheng","year":"2023","unstructured":"Cheng Xie, Wenbiao Tao, Zuoying Zeng, and Yuran Dong. 2023. Binary-convolution data-reduction network for edge\u2013cloud IIoT anomaly detection. Electronics 12, 15 (2023), 3229.","journal-title":"Electronics"},{"key":"e_1_3_2_103_2","first-page":"1","volume-title":"The 25th Annual International Conference on Mobile Computing and Networking","author":"Xie Xiufeng","year":"2019","unstructured":"Xiufeng Xie and Kyu-Han Kim. 2019. Source compression with bounded DNN perception loss for IoT edge computer vision. In The 25th Annual International Conference on Mobile Computing and Networking. 1\u201316."},{"issue":"2","key":"e_1_3_2_104_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang Qiang","year":"2019","unstructured":"Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1\u201319.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_105_2","first-page":"476","volume-title":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","author":"Yao Shuochao","year":"2020","unstructured":"Shuochao Yao, Jinyang Li, Dongxin Liu, Tianshi Wang, Shengzhong Liu, Huajie Shao, and Tarek Abdelzaher. 2020. Deep compressive offloading: Speeding up neural network inference by trading edge computation for network latency. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 476\u2013488."},{"key":"e_1_3_2_106_2","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.knosys.2021.106775","article-title":"A survey on federated learning","volume":"216","author":"Zhang Chen","year":"2021","unstructured":"Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. 2021. A survey on federated learning. Knowledge-based Systems 216 (2021), 106775.","journal-title":"Knowledge-based Systems"},{"issue":"8","key":"e_1_3_2_107_2","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge intelligence: Paving the last mile of artificial intelligence with edge computing","volume":"107","author":"Zhou Zhi","year":"2019","unstructured":"Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang. 2019. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE 107, 8 (2019), 1738\u20131762.","journal-title":"Proceedings of the IEEE"},{"key":"e_1_3_2_108_2","first-page":"51","volume-title":"2022 IEEE International Conference on e-Business Engineering (ICEBE\u201922)","author":"Zhu Yini","year":"2022","unstructured":"Yini Zhu and Cheng Xie. 2022. Edge-cloud hybrid tiny data reduction model for anomaly detection. In 2022 IEEE International Conference on e-Business Engineering (ICEBE\u201922). IEEE, 51\u201357."},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1977.1055714"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3656338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T13:20:33Z","timestamp":1714051233000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,25]]},"references-count":108,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3656338"],"URL":"http:\/\/dx.doi.org\/10.1145\/3656338","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,25]]},"assertion":[{"value":"2023-01-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-31","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}
