Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities
In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and d...
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| Format: | Online |
| Język: | angielski |
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InTechOpen
2021
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| Hasła przedmiotowe: | |
| Dostęp online: | ONIX_20210602_10.5772/intechopen.75203_392 |
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| _version_ | 1869516523229413376 |
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| author | Kovacevic, Ana Urosevic, Vladimir Kaddachi, Firas |
| author_browse | Kaddachi, Firas Kovacevic, Ana Urosevic, Vladimir |
| author_facet | Kovacevic, Ana Urosevic, Vladimir Kaddachi, Firas |
| author_sort | Kovacevic, Ana |
| collection | Directory of Open Access Books |
| description | In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and deployment of ubiquitous systems for assessment and prediction of early risks of elderly Mild Cognitive Impairments (MCI) and frailty, and for supporting generation and delivery of optimal personalized preventive interventions that mitigate those risks, utilizing smart city datasets and IoT infrastructure. Low level data collected from IoT devices are preprocessed as sequences of activities, with temporal and causal variations in sequences classified as normal or anomalous behavior. The goals of proposed methodology are to (1) recognize significant behavioral variation patterns and (2) support early identification of pattern changes. Temporal clustering models are applied in detection and prediction of the following variation types: intra-activity (single activity, single citizen) and inter-activity (multiple-activities, single citizen). Identified behavioral variations and anomalies are further mapped to MCI/frailty onset behavior and risk factors, following the developed geriatric expert model. |
| format | Online |
| id | doab-20.500.12854ir-70489 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-704892025-01-24T16:34:02Z Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities Kovacevic, Ana Urosevic, Vladimir Kaddachi, Firas temporal clustering, IoT, smart cities, behavior recognition, anomaly detection thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and deployment of ubiquitous systems for assessment and prediction of early risks of elderly Mild Cognitive Impairments (MCI) and frailty, and for supporting generation and delivery of optimal personalized preventive interventions that mitigate those risks, utilizing smart city datasets and IoT infrastructure. Low level data collected from IoT devices are preprocessed as sequences of activities, with temporal and causal variations in sequences classified as normal or anomalous behavior. The goals of proposed methodology are to (1) recognize significant behavioral variation patterns and (2) support early identification of pattern changes. Temporal clustering models are applied in detection and prediction of the following variation types: intra-activity (single activity, single citizen) and inter-activity (multiple-activities, single citizen). Identified behavioral variations and anomalies are further mapped to MCI/frailty onset behavior and risk factors, following the developed geriatric expert model. 2021-02-10T12:58:18Z 2021-06-02T10:11:13Z 2018 chapter ONIX_20210602_10.5772/intechopen.75203_392 https://library.oapen.org/handle/20.500.12657/49278 https://directory.doabooks.org/handle/20.500.12854/70489 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/49278/1/60011.pdf https://library.oapen.org/bitstream/20.500.12657/49278/1/60011.pdf https://library.oapen.org/bitstream/20.500.12657/49278/1/60011.pdf InTechOpen 10.5772/intechopen.75203 10.5772/intechopen.75203 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | temporal clustering, IoT, smart cities, behavior recognition, anomaly detection thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining Kovacevic, Ana Urosevic, Vladimir Kaddachi, Firas Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities |
| title | Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities |
| title_full | Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities |
| title_fullStr | Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities |
| title_full_unstemmed | Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities |
| title_short | Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities |
| title_sort | chapter temporal clustering for behavior variation and anomaly detection from data acquired through iot in smart cities |
| topic | temporal clustering, IoT, smart cities, behavior recognition, anomaly detection thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining |
| topic_facet | temporal clustering, IoT, smart cities, behavior recognition, anomaly detection thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining |
| url | ONIX_20210602_10.5772/intechopen.75203_392 |
| work_keys_str_mv | AT kovacevicana chaptertemporalclusteringforbehaviorvariationandanomalydetectionfromdataacquiredthroughiotinsmartcities AT urosevicvladimir chaptertemporalclusteringforbehaviorvariationandanomalydetectionfromdataacquiredthroughiotinsmartcities AT kaddachifiras chaptertemporalclusteringforbehaviorvariationandanomalydetectionfromdataacquiredthroughiotinsmartcities |