{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:48:31Z","timestamp":1769834911820,"version":"3.49.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"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":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>Earthquakes are among the most inevitable natural catastrophes. The uncertainty about the severity of the earthquake has a profound effect on the burden of disaster and causes massive economic and societal losses. Although unpredictable, it can be expected to ameliorate damage and fatalities, such as monitoring and predicting earthquakes using the Internet of Things (IoT). With the resurgence of the IoT, an emerging innovative approach is to integrate IoT technology with Fog and Cloud Computing to augment the effectiveness and accuracy of earthquake monitoring and prediction. In this study, the integrated IoT-Fog-Cloud layered framework is proposed to predict earthquakes using seismic signal information. The proposed model is composed of three layers: (i) at sensor layer, seismic data are acquired, (ii) fog layer incorporates pre-processing, feature extraction using fast Walsh\u2013Hadamard transform (FWHT), selection of relevant features by applying High Order Spectral Analysis (HOSA) to FWHT coefficients, and seismic event classification by K-means accompanied by real-time alert generation, (iii) at cloud layer, an artificial neural network (ANN) is employed to forecast the magnitude of an earthquake. For performance evaluation, K-means classification algorithm is collated with other well-known classification algorithms from the perspective of accuracy and execution duration. Implementation statistics indicate that with chosen HOS features, we have been able to attain high accuracy, precision, specificity, and sensitivity values of 93.30%, 96.65%, 90.54%, and 92.75%, respectively. In addition, the ANN provides an average correct magnitude prediction of 75%. The findings ensured that the proposed framework has the potency to classify seismic signals and predict earthquakes and could therefore further enhance the detection of seismic activities. Moreover, the generation of real-time alerts further amplifies the effectiveness of the proposed model and makes it more real-time compatible.<\/jats:p>","DOI":"10.1145\/3487942","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T17:20:42Z","timestamp":1636996842000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["IoT-Fog-Cloud Centric Earthquake Monitoring and Prediction"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8127-688X","authenticated-orcid":false,"given":"Kanika","family":"Saini","sequence":"first","affiliation":[{"name":"Guru Nanak Dev University, Regional Campus, Jalandhar, Punjab, India"}]},{"given":"Sheetal","family":"Kalra","sequence":"additional","affiliation":[{"name":"Guru Nanak Dev University, Regional Campus, Jalandhar, Punjab, India"}]},{"given":"Sandeep K.","family":"Sood","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Kurukshetra, Haryana, India"}]}],"member":"320","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"An Introduction to Seismology, Earthquakes, and Earth Structure","author":"Stein Seth","year":"2009","unstructured":"Seth Stein and Michael Wysession. 2009. An Introduction to Seismology, Earthquakes, and Earth Structure. John Wiley & Sons."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-017-0082-1"},{"key":"e_1_3_1_4_2","unstructured":"USGS. \u201cNew Earthquake Hazards Program.\u201d Lists Maps and Statistics. Retrieved on December 1 2020 from www.usgs.gov\/naturalhazards\/earthquake-hazards\/lists-maps-and-statistics."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1029\/2008GL034428"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1029\/JB091iB12p12269"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1029\/JB087iB09p07824"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2952593"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2013.01.010"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2015.09.021"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2017.09.002"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/HotWeb.2015.22"},{"issue":"1","key":"e_1_3_1_13_2","first-page":"303","article-title":"Robust TIR satellite techniques for monitoring earthquake active regions: Limits, main achievements and perspectives","volume":"51","author":"Aliano Carolina","year":"2008","unstructured":"Carolina Aliano, Rosita Corrado, Carolina Filizzola, Nicola Genzano, Nicola Pergola, and Valerio Tramutoli. 2008. Robust TIR satellite techniques for monitoring earthquake active regions: Limits, main achievements and perspectives. Annals of Geophysics 51, 1 (2008), 303\u2013317.","journal-title":"Annals of Geophysics"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1029\/2011GL047947"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18113712"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13143693"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.05.043"},{"key":"e_1_3_1_18_2","first-page":"1","volume-title":"Proceedings of the 2013 International Conference on Human Computer Interactions","author":"Prasad L. V. Narasimha","year":"2013","unstructured":"L. V. Narasimha Prasad, P. Shankar Murthy, and C. Kishor Kumar Reddy. 2013. Analysis of magnitude for earthquake detection using primary waves and secondary waves. In Proceedings of the 2013 International Conference on Human Computer Interactions. IEEE, 1\u20136."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.11128\/sne.19.on.09941"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/WiSPNET.2016.7566327"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2017.2699169"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/978-3-030-01168-0_48","volume-title":"Internet of Things, Smart Spaces, and Next Generation Networks and Systems","author":"Pirmagomedov Rustam","year":"2018","unstructured":"Rustam Pirmagomedov, Mikhail Blinnikov, Alexey Amelyanovich, Ruslan Glushakov, Svyatoslav Loskutov, Andrey Koucheryavy, Ruslan Kirichek, and Ekaterina Bobrikova. 2018. IoT based earthquake prediction technology. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems. O. Galinina, S. Andreev, S. Balandin, and Y. Koucheryavy (Eds.), Lecture Notes in Computer Science, Vol. 11118, Springer, 535\u2013546."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/19475683.2018.1450785"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2017.10.011"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2018.1512010"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-019-09293-6"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-014-1264-7"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.1969.5214175"},{"issue":"1","key":"e_1_3_1_29_2","first-page":"1","article-title":"The simulation results of the high-pass and low-pass filter effect on the quality of micropotential recordings on the electrocardiogram","volume":"6","author":"Avdeeva Diana Konstantinovna","year":"2014","unstructured":"Diana Konstantinovna Avdeeva, Veniamin Yur\u2019evich Kazakov, Nataliya Mihajlovna Natalinova, Maxim Leonidovich Ivanov, Mariya Alexandrovna Yuzhakova, and Nikita Vladimirovich Turushev. 2014. The simulation results of the high-pass and low-pass filter effect on the quality of micropotential recordings on the electrocardiogram. Biology and Medicine 6, 1 (2014), 1.","journal-title":"Biology and Medicine"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSNT.2011.108"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.923195"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.5555\/971115"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.1976.1674569"},{"key":"e_1_3_1_34_2","volume-title":"Feature Selection for Knowledge Discovery and Data Mining","author":"Liu Huan","year":"2012","unstructured":"Huan Liu and Hiroshi Motoda. 2012. Feature Selection for Knowledge Discovery and Data Mining. Vol. 454, Springer Science & Business Media."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-3758(02)00166-0"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.1997.0098"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/79.221324"},{"key":"e_1_3_1_38_2","unstructured":"B. Boashash and B. Ristic. 1995. A time-frequency perspective of higher-order spectra as a tool for non-stationary signal analysis. Longman Australia."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.1997.0145"},{"key":"e_1_3_1_40_2","first-page":"22","article-title":"Higher-order spectral analysis toolbox","volume":"3","author":"Swami Ananthram","year":"1998","unstructured":"Ananthram Swami, Jerry M. Mendel, and Chrysostomos L. Nikias. 1998. Higher-order spectral analysis toolbox. The Mathworks Inc 3 (1998), 22\u201326.","journal-title":"The Mathworks Inc"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-92841-6_56"},{"key":"e_1_3_1_42_2","first-page":"1","article-title":"IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification","author":"Singh Kuldeep","year":"2019","unstructured":"Kuldeep Singh and Jyoteesh Malhotra. 2019. IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification. Journal of Ambient Intelligence and Humanized Computing (2019), 1\u201316.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2909218"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/ANZIIS.1994.396988"},{"key":"e_1_3_1_45_2","unstructured":"Retrieved December 1 2020 from http:\/\/www.kyoshin.bosai.go.jp National Research Institute for Earth Science and Disaster Prevention."},{"key":"e_1_3_1_46_2","unstructured":"Retrieved December 1 2020 from https:\/\/ngawest2.berkeley.edu\/ Peer Ground Motion Database Pacific Earthquake Engineering Research Center."},{"key":"e_1_3_1_47_2","unstructured":"United States Geological Survey. Retrieved December 1 2020 from https:\/\/www.usgs.gov\/natural-hazards\/earthquake-hazards\/earthquakes."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.871199"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1982.tb00339.x"},{"key":"e_1_3_1_50_2","unstructured":"David M. W. Powers. 2020. Evaluation: From precision recall and F-measure to ROC informedness markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)."},{"issue":"4","key":"e_1_3_1_51_2","first-page":"135","article-title":"An approach of the Naive Bayes classifier for the document classification","volume":"14","author":"Pop Ioan","year":"2006","unstructured":"Ioan Pop. 2006. An approach of the Naive Bayes classifier for the document classification. General Mathematics 14, 4 (2006), 135\u2013138.","journal-title":"General Mathematics"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.09.032"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.10.009"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.5555\/3103568"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3487942","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3487942","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:56Z","timestamp":1750191116000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3487942"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":53,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11,30]]}},"alternative-id":["10.1145\/3487942"],"URL":"https:\/\/doi.org\/10.1145\/3487942","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"value":"1539-9087","type":"print"},{"value":"1558-3465","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,15]]},"assertion":[{"value":"2021-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}