{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:45:11Z","timestamp":1773845111252,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly. Fortunately, the quick development of mobile cloud computing technologies and wearable sensors has provided a promising way to monitor the real-time health conditions of students and find out the anomalies timely. However, two challenges are present in the above anomaly detection issue. First, the health data monitored by massive wearable sensors are often massive and updated frequently, which probably leads to high sensor-cloud transmission cost for anomaly detection. Second, the health data of students are often sensitive enough, which probably impedes the integration of health data in cloud environment even renders the health data-based anomaly detection infeasible. In view of these challenges, we propose a time-efficient and privacy-aware anomaly detection solution for students with wearable sensors in mobile cloud computing environment. At last, we validate the effectiveness and efficiency of our work via a set of simulated experiments.<\/jats:p>","DOI":"10.1186\/s13677-022-00300-x","type":"journal-article","created":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T18:02:34Z","timestamp":1662400954000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing"],"prefix":"10.1186","volume":"11","author":[{"given":"Yu","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuilin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaizhen","family":"Kou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Jafar","family":"Mokarram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,5]]},"reference":[{"issue":"1","key":"300_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-022-00278-6","volume":"11","author":"N Ferraz Junior","year":"2022","unstructured":"Ferraz Junior N, Silva AA, Guelfi AE, Kofuji ST (2022) Performance evaluation of publish-subscribe systems in IoT using energy-efficient and context-aware secure messages. J Cloud Comput 11(1):1\u201317","journal-title":"J Cloud Comput"},{"issue":"2","key":"300_CR2","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/JBHI.2020.2997760","volume":"25","author":"S Wang","year":"2020","unstructured":"Wang S, Cong Y, Zhu H, Chen X, Qu L, Fan H et al (2020) Multi-scale context-guided deep network for automated lesion segmentation with endoscopy images of gastrointestinal tract. IEEE J Biomed Health Inform 25(2):514\u2013525","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"300_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-020-00215-5","volume":"9","author":"B\u00c7 Uslu","year":"2020","unstructured":"Uslu B\u00c7, Okay E, Dursun E (2020) Analysis of factors affecting IoT-based smart hospital design. J Cloud Comput 9(1):1\u201323","journal-title":"J Cloud Comput"},{"key":"300_CR4","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compind.2019.06.008","volume":"111","author":"WH Cui","year":"2019","unstructured":"Cui WH, Ye J (2019) Logarithmic similarity measure of dynamic neutrosophic cubic sets and its application in medical diagnosis. Comput Ind 111:198\u2013206","journal-title":"Comput Ind"},{"key":"300_CR5","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.jbi.2018.10.003","volume":"87","author":"J Fu","year":"2018","unstructured":"Fu J, Ye J, Cui W (2018) An evaluation method of risk grades for prostate cancer using similarity measure of cubic hesitant fuzzy sets. J Biomed Inform 87:131\u2013137","journal-title":"J Biomed Inform"},{"key":"300_CR6","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.cmpb.2015.10.002","volume":"123","author":"J Ye","year":"2016","unstructured":"Ye J, Fu J (2016) Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function. Comput Methods Programs Biomed 123:142\u2013149","journal-title":"Comput Methods Programs Biomed"},{"key":"300_CR7","doi-asserted-by":"publisher","first-page":"102425","DOI":"10.1016\/j.jnca.2019.102425","volume":"146","author":"S Feng","year":"2019","unstructured":"Feng S, Shen S, Huang L, Champion AC, Yu S, Wu C et al (2019) Three-dimensional robot localization using cameras in wireless multimedia sensor networks. J Netw Comput Appl 146:102425","journal-title":"J Netw Comput Appl"},{"issue":"2","key":"300_CR8","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TVT.2021.3133696","volume":"71","author":"J Huang","year":"2022","unstructured":"Huang J, Lv B, Wu Y et al (2022) Dynamic Admission Control and Resource Allocation for Mobile Edge Computing Enabled Small Cell Network. IEEE Trans Veh Technol 71(2):1964\u20131973","journal-title":"IEEE Trans Veh Technol"},{"key":"300_CR9","doi-asserted-by":"publisher","unstructured":"Dai Y, Wu J, Fan Y, Wang J, Niu J, Gu F, et\u00a0al. (2022) MSEva: A Musculoskeletal Rehabilitation Evaluation System Based on EMG Signals. ACM Trans Sensor Netw (TOSN). https:\/\/doi.org\/10.1145\/3522739","DOI":"10.1145\/3522739"},{"key":"300_CR10","doi-asserted-by":"publisher","unstructured":"Huang J, Tong Z, Feng Z. Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization. Int J Commun Syst 1-1. https:\/\/doi.org\/10.1002\/dac.5161","DOI":"10.1002\/dac.5161"},{"key":"300_CR11","doi-asserted-by":"publisher","unstructured":"Liu Y, Song Z, Xu X, Rafique W, Zhang X, Shen J, et\u00a0al. (2021) Bidirectional GRU networks-based next POI category prediction for healthcare. Int J Intel Syst. https:\/\/doi.org\/10.1002\/int.22710","DOI":"10.1002\/int.22710"},{"key":"300_CR12","doi-asserted-by":"publisher","unstructured":"Liu H, Xu X, Li E, Zhang S, Li X. (2021) Anomaly detection with representative neighbors. IEEE Trans Neural Netw Learn Syst.\u00a0https:\/\/doi.org\/10.1109\/TNNLS.2021.3109898","DOI":"10.1109\/TNNLS.2021.3109898"},{"key":"300_CR13","doi-asserted-by":"publisher","unstructured":"Kong L, Wang L, Gong W, Yan C, Duan Y, Qi L (2021) LSH-aware multitype health data prediction with privacy preservation in edge environment. World Wide Web 1-16.\u00a0https:\/\/doi.org\/10.1007\/s11280-021-00941-z","DOI":"10.1007\/s11280-021-00941-z"},{"key":"300_CR14","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.asoc.2015.01.038","volume":"30","author":"J Liu","year":"2015","unstructured":"Liu J, Shen S, Yue G, Han R, Li H (2015) A stochastic evolutionary coalition game model of secure and dependable virtual service in sensor-cloud. Appl Soft Comput 30:123\u2013135","journal-title":"Appl Soft Comput"},{"issue":"2","key":"300_CR15","doi-asserted-by":"publisher","first-page":"818903","DOI":"10.1155\/2015\/818903","volume":"11","author":"Y Li","year":"2015","unstructured":"Li Y, Xu H, Cao Q, Li Z, Shen S (2015) Evolutionary game-based trust strategy adjustment among nodes in wireless sensor networks. Int J Distrib Sensor Netw 11(2):818903","journal-title":"Int J Distrib Sensor Netw"},{"key":"300_CR16","doi-asserted-by":"publisher","unstructured":"Zhou X, Liang W, Li W, Yan K, Shimizu S, Kevin I, et\u00a0al. (2021) Hierarchical Adversarial Attacks Against Graph Neural Network Based IoT Network Intrusion Detection System. IEEE Internet Things J.\u00a0https:\/\/doi.org\/10.1109\/JIOT.2021.3130434","DOI":"10.1109\/JIOT.2021.3130434"},{"key":"300_CR17","doi-asserted-by":"publisher","unstructured":"Liu J, Wang X, Shen S, Fang Z, Yu S, Yue G, et\u00a0al. (2021) Intelligent jamming defense using DNN Stackelberg game in sensor edge cloud. IEEE Internet Things J.\u00a0https:\/\/doi.org\/10.1109\/JIOT.2021.3103196","DOI":"10.1109\/JIOT.2021.3103196"},{"key":"300_CR18","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.comcom.2020.08.009","volume":"162","author":"H Zhou","year":"2020","unstructured":"Zhou H, Shen S, Liu J (2020) Malware propagation model in wireless sensor networks under attack-defense confrontation. Computer Communications 162:51\u201358","journal-title":"Computer Communications"},{"key":"300_CR19","doi-asserted-by":"publisher","unstructured":"Zhou X, Yang X, Ma J, Kevin I, Wang K (2021) Energy efficient smart routing based on link correlation mining for wireless edge computing in IoT. IEEE Internet Things J.\u00a0https:\/\/doi.org\/10.1109\/JIOT.2021.3077937","DOI":"10.1109\/JIOT.2021.3077937"},{"issue":"11","key":"300_CR20","doi-asserted-by":"crossref","first-page":"155014772097294","DOI":"10.1177\/1550147720972944","volume":"16","author":"H Zhang","year":"2020","unstructured":"Zhang H, Shen S, Cao Q, Wu X, Liu S (2020) Modeling and analyzing malware diffusion in wireless sensor networks based on cellular automaton. Int J Distrib Sensor Netw 16(11):1550147720972944","journal-title":"Int J Distrib Sensor Netw"},{"key":"300_CR21","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.future.2017.10.037","volume":"81","author":"J Liu","year":"2018","unstructured":"Liu J, Wang X, Yue G, Shen S (2018) Data sharing in VANETs based on evolutionary fuzzy game. Futur Gener Comput Syst 81:141\u2013155","journal-title":"Futur Gener Comput Syst"},{"key":"300_CR22","doi-asserted-by":"publisher","unstructured":"Chen Y, Gu W, Li K. Dynamic task offloading for Internet of Things in mobile edge computing via deep reinforcement learning. Int J Commun Syst https:\/\/doi.org\/10.1002\/dac.5154","DOI":"10.1002\/dac.5154"},{"issue":"2","key":"300_CR23","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1177\/1475921718757405","volume":"18","author":"Y Bao","year":"2019","unstructured":"Bao Y, Tang Z, Li H, Zhang Y (2019) Computer vision and deep learning-based data anomaly detection method for structural health monitoring. Structural Health Monitoring 18(2):401\u2013421","journal-title":"Structural Health Monitoring"},{"issue":"1","key":"300_CR24","doi-asserted-by":"publisher","first-page":"e2296","DOI":"10.1002\/stc.2296","volume":"26","author":"Z Tang","year":"2019","unstructured":"Tang Z, Chen Z, Bao Y, Li H (2019) Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct Control Health Monit 26(1):e2296","journal-title":"Struct Control Health Monit"},{"key":"300_CR25","doi-asserted-by":"crossref","unstructured":"Lee K, Kim JK, Kim J, Hur K, Kim H (2018) CNN and GRU combination scheme for bearing anomaly detection in rotating machinery health monitoring. In: 2018 1st IEEE International conference on knowledge innovation and invention (ICKII). IEEE, pp 102-105","DOI":"10.1109\/ICKII.2018.8569155"},{"issue":"4","key":"300_CR26","doi-asserted-by":"publisher","first-page":"e2136","DOI":"10.1002\/stc.2136","volume":"25","author":"LH Nguyen","year":"2018","unstructured":"Nguyen LH, Goulet JA (2018) Anomaly detection with the switching kalman filter for structural health monitoring. Struct Control Health Monit 25(4):e2136","journal-title":"Struct Control Health Monit"},{"key":"300_CR27","doi-asserted-by":"publisher","first-page":"106495","DOI":"10.1016\/j.ymssp.2019.106495","volume":"140","author":"H Sarmadi","year":"2020","unstructured":"Sarmadi H, Karamodin A (2020) A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mech Syst Signal Process 140:106495","journal-title":"Mech Syst Signal Process"},{"key":"300_CR28","doi-asserted-by":"publisher","unstructured":"Qi L, Yang Y, Zhou X, Rafique W, Ma J (2021) Fast Anomaly Identification Based on Multi-Aspect Data Streams for Intelligent Intrusion Detection Toward Secure Industry 4.0. IEEE Trans Ind Inform https:\/\/doi.org\/10.1109\/TII20213139363","DOI":"10.1109\/TII20213139363"},{"issue":"6","key":"300_CR29","doi-asserted-by":"publisher","first-page":"821","DOI":"10.26599\/TST.2020.9010041","volume":"26","author":"W Wang","year":"2021","unstructured":"Wang W, Wang Z, Zhou Z, Deng H, Zhao W, Wang C et al (2021) Anomaly detection of industrial control systems based on transfer learning. Tsinghua Sci Technol 26(6):821\u2013832","journal-title":"Tsinghua Sci Technol"},{"issue":"1","key":"300_CR30","doi-asserted-by":"publisher","first-page":"18","DOI":"10.26599\/BDMA.2020.9020019","volume":"4","author":"A Guezzaz","year":"2021","unstructured":"Guezzaz A, Asimi Y, Azrour M, Asimi A (2021) Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection. Big Data Mining and Analytics 4(1):18\u201324","journal-title":"Big Data Mining and Analytics"},{"key":"300_CR31","unstructured":"Ying C, Hua X, Zhuo M, et\u00a0al. (2022) Cost-Efficient Edge Caching for NOMA-enabled IoT Services. China Commun"},{"issue":"2","key":"300_CR32","doi-asserted-by":"publisher","first-page":"244","DOI":"10.26599\/TST.2021.9010015","volume":"27","author":"K Zhang","year":"2021","unstructured":"Zhang K, Tian Z, Cai Z, Seo D (2021) Link-privacy preserving graph embedding data publication with adversarial learning. Tsinghua Sci Technol 27(2):244\u2013256","journal-title":"Tsinghua Sci Technol"},{"issue":"1","key":"300_CR33","doi-asserted-by":"publisher","first-page":"32","DOI":"10.26599\/BDMA.2021.9020016","volume":"5","author":"AK Sandhu","year":"2021","unstructured":"Sandhu AK (2021) Big data with cloud computing: Discussions and challenges. Big Data Mining Analytics 5(1):32\u201340","journal-title":"Big Data Mining Analytics"},{"issue":"6","key":"300_CR34","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1109\/TII.2020.3012157","volume":"17","author":"L Qi","year":"2021","unstructured":"Qi L, Hu C, Zhang X, Khosravi MR, Sharma S, Pang S et al (2021) Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Trans Ind Inform 17(6):4159\u20134167","journal-title":"IEEE Trans Ind Inform"},{"issue":"3","key":"300_CR35","doi-asserted-by":"publisher","first-page":"581","DOI":"10.26599\/TST.2021.9010052","volume":"27","author":"Q Yuan","year":"2021","unstructured":"Yuan Q, Wang D, Zhao Y, Sang Y, Wang F, Liu Y et al (2021) Privacy-aware examination results ranking for the balance between teachers and mothers. Tsinghua Sci Technol 27(3):581\u2013588","journal-title":"Tsinghua Sci Technol"},{"issue":"1","key":"300_CR36","first-page":"1","volume":"5","author":"F Li","year":"2021","unstructured":"Li F, Yu X, Ge R, Wang Y, Cui Y, Zhou H (2021) BCSE: Blockchain-based trusted service evaluation model over big data. Big Data Min Analytics 5(1):1\u201314","journal-title":"Big Data Min Analytics"},{"issue":"8","key":"300_CR37","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/TPDS.2021.3131680","volume":"33","author":"L Yuan","year":"2021","unstructured":"Yuan L, He Q, Chen F, Zhang J, Qi L, Xu X et al (2021) CSEdge: Enabling Collaborative Edge Storage for Multi-Access Edge Computing Based on Blockchain. IEEE Trans Parallel Distrib Syst 33(8):1873\u20131887","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"300_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2022.109218","author":"J Xu","year":"2022","unstructured":"Xu J, Li D, Gu W et al (2022) UAV-assisted Task Offloading for IoT in Smart Buildings and Environment via Deep Reinforcement Learning. Building and Environment. https:\/\/doi.org\/10.1016\/j.buildenv.2022.109218","journal-title":"Building and Environment"},{"issue":"2","key":"300_CR39","doi-asserted-by":"publisher","first-page":"235","DOI":"10.26599\/TST.2021.9010018","volume":"27","author":"X Zheng","year":"2021","unstructured":"Zheng X, Zhang L, Li K, Zeng X (2021) Efficient publication of distributed and overlapping graph data under differential privacy. Tsinghua Sci Technol 27(2):235\u2013243","journal-title":"Tsinghua Sci Technol"},{"issue":"1","key":"300_CR40","doi-asserted-by":"publisher","first-page":"53","DOI":"10.26599\/BDMA.2021.9020018","volume":"5","author":"H Wang","year":"2021","unstructured":"Wang H, Cao Z, Zhou Y, Guo ZK, Ren Z (2021) Sampling with prior knowledge for high-dimensional gravitational wave data analysis. Big Data Min Analytics 5(1):53\u201363","journal-title":"Big Data Min Analytics"},{"key":"300_CR41","doi-asserted-by":"publisher","unstructured":"Chen Y, Zhao F, Lu Y, Chen X. Dynamic task offloading for mobile edge computing with hybrid energy supply. Tsinghua Science and Technology https:\/\/doi.org\/10.26599\/TST.2021.9010050","DOI":"10.26599\/TST.2021.9010050"},{"key":"300_CR42","doi-asserted-by":"publisher","unstructured":"Zhou X, Hu Y, Wu J, Liang W, Ma J, Jin Q (2022) Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT. IEEE Trans Ind Inform.\u00a0https:\/\/doi.org\/10.1109\/TII.2022.3170149","DOI":"10.1109\/TII.2022.3170149"},{"key":"300_CR43","doi-asserted-by":"publisher","first-page":"106952","DOI":"10.1016\/j.knosys.2021.106952","volume":"220","author":"Z Wu","year":"2021","unstructured":"Wu Z, Shen S, Zhou H, Li H, Lu C, Zou D (2021) An effective approach for the protection of user commodity viewing privacy in e-commerce website. Knowl-Based Syst 220:106952","journal-title":"Knowl-Based Syst"},{"issue":"8","key":"300_CR44","doi-asserted-by":"publisher","first-page":"5087","DOI":"10.1109\/TII.2021.3116085","volume":"18","author":"W Liang","year":"2021","unstructured":"Liang W, Hu Y, Zhou X, Pan Y, Kevin I, Wang K (2021) Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial IoT. IEEE Trans Ind Inform 18(8):5087\u20135095","journal-title":"IEEE Trans Ind Inform"},{"issue":"11","key":"300_CR45","doi-asserted-by":"publisher","first-page":"2616","DOI":"10.1109\/JSAC.2017.2760458","volume":"35","author":"L Qi","year":"2017","unstructured":"Qi L, Zhang X, Dou W, Ni Q (2017) A Distributed Locality-Sensitive Hashing based Approach for Cloud Service Recommendation from Multi-Source Data. IEEE J Sel Areas Commun 35(11):2616\u20132624","journal-title":"IEEE J Sel Areas Commun"},{"issue":"7","key":"300_CR46","doi-asserted-by":"publisher","first-page":"4925","DOI":"10.1109\/TII.2020.3028963","volume":"17","author":"Y Chen","year":"2021","unstructured":"Chen Y, Liu Z, Zhang Y, Wu Y, Chen X, Zhao L (2021) Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things. IEEE Trans Ind Inform 17(7):4925\u20134934","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"300_CR47","doi-asserted-by":"publisher","first-page":"28","DOI":"10.23919\/JSC.2020.0004","volume":"1","author":"YS Su","year":"2020","unstructured":"Su YS, Ruan Y, Sun S, Chang YT (2020) A Pattern Recognition Framework for Detecting Changes in Chinese Internet Management System. J Soc Comput 1(1):28\u201339","journal-title":"J Soc Comput"},{"issue":"1","key":"300_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.23919\/ICN.2020.0005","volume":"1","author":"T Li","year":"2020","unstructured":"Li T, Li C, Luo J, Song L (2020) Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities. Intell Converged Netw 1(1):1\u201317","journal-title":"Intell Converged Netw"},{"issue":"1","key":"300_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.23919\/JSC.2020.0002","volume":"1","author":"J Evans","year":"2020","unstructured":"Evans J (2020) Social computing unhinged. J Soc Comput 1(1):1\u201313","journal-title":"Social computing unhinged. J Soc Comput"},{"key":"300_CR50","doi-asserted-by":"publisher","unstructured":"Qi L, Lin W, Zhang X, Dou W, Xu X, Chen J (2022) A Correlation Graph based Approach for Personalized and Compatible Web APIs Recommendation in Mobile APP Development. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2022.3168611.","DOI":"10.1109\/TKDE.2022.3168611"},{"issue":"1","key":"300_CR51","doi-asserted-by":"publisher","first-page":"40","DOI":"10.23919\/JSC.2020.0005","volume":"1","author":"X Gao","year":"2020","unstructured":"Gao X, Luo JD, Yang K, Fu X, Liu L, Gu W (2020) Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks. J Soc Comput 1(1):40\u201352","journal-title":"J Soc Comput"},{"issue":"1","key":"300_CR52","doi-asserted-by":"publisher","first-page":"14","DOI":"10.23919\/JSC.2020.0003","volume":"1","author":"C Catlett","year":"2020","unstructured":"Catlett C, Beckman P, Ferrier N, Nusbaum H, Papka ME, Berman MG et al (2020) Measuring cities with software-defined sensors. J Soc Comput 1(1):14\u201327","journal-title":"J Soc Comput"},{"issue":"3","key":"300_CR53","doi-asserted-by":"publisher","first-page":"243","DOI":"10.23919\/ICN.2020.0020","volume":"1","author":"GS Rahman","year":"2020","unstructured":"Rahman GS, Dang T, Ahmed M (2020) Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks. Intelligent and Converged Networks. 1(3):243\u2013257","journal-title":"Intelligent and Converged Networks."},{"issue":"6","key":"300_CR54","doi-asserted-by":"publisher","first-page":"4219","DOI":"10.1109\/TII.2020.2995348","volume":"17","author":"S Meng","year":"2020","unstructured":"Meng S, Huang W, Yin X, Khosravi MR, Li Q, Wan S et al (2020) Security-aware dynamic scheduling for real-time optimization in cloud-based industrial applications. IEEE Trans Ind Inform 17(6):4219\u20134228","journal-title":"IEEE Trans Ind Inform"},{"issue":"2","key":"300_CR55","doi-asserted-by":"publisher","first-page":"181","DOI":"10.23919\/ICN.2020.0014","volume":"1","author":"S Nath","year":"2020","unstructured":"Nath S, Wu J (2020) Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems. Intell Converged Netw 1(2):181\u2013198","journal-title":"Intell Converged Netw"},{"issue":"5","key":"300_CR56","doi-asserted-by":"publisher","first-page":"4584","DOI":"10.1109\/TVT.2021.3133586","volume":"71","author":"Y Chen","year":"2022","unstructured":"Chen Y, Zhao F, Chen X, Wu Y (2022) Efficient Multi-Vehicle Task Offloading for Mobile Edge Computing in 6G Networks. IEEE Trans Veh Technol 71(5):4584\u20134595","journal-title":"IEEE Trans Veh Technol"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00300-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-022-00300-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00300-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T22:06:42Z","timestamp":1662674802000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-022-00300-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,5]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["300"],"URL":"https:\/\/doi.org\/10.1186\/s13677-022-00300-x","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,5]]},"assertion":[{"value":"8 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"38"}}