Real-Time Information Derivation from Big Sensor Data via Edge Computing
In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rathe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/1/1/5 |
| _version_ | 1818064775587823616 |
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| author | Kyoung-Don Kang Liehuo Chen Hyungdae Yi Bin Wang Mo Sha |
| author_facet | Kyoung-Don Kang Liehuo Chen Hyungdae Yi Bin Wang Mo Sha |
| author_sort | Kyoung-Don Kang |
| collection | DOAJ |
| description | In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion. |
| first_indexed | 2024-12-10T14:41:22Z |
| format | Article |
| id | doaj.art-7ce1637188c3429abc6bc281c9fa6aed |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-12-10T14:41:22Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-7ce1637188c3429abc6bc281c9fa6aed2022-12-22T01:44:40ZengMDPI AGBig Data and Cognitive Computing2504-22892017-10-0111510.3390/bdcc1010005bdcc1010005Real-Time Information Derivation from Big Sensor Data via Edge ComputingKyoung-Don Kang0Liehuo Chen1Hyungdae Yi2Bin Wang3Mo Sha4Department of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USADepartment of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USADepartment of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USADepartment of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USADepartment of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USAIn data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion.https://www.mdpi.com/2504-2289/1/1/5real-time big sensor data analytics architectureinternet of thingsedge computing |
| spellingShingle | Kyoung-Don Kang Liehuo Chen Hyungdae Yi Bin Wang Mo Sha Real-Time Information Derivation from Big Sensor Data via Edge Computing Big Data and Cognitive Computing real-time big sensor data analytics architecture internet of things edge computing |
| title | Real-Time Information Derivation from Big Sensor Data via Edge Computing |
| title_full | Real-Time Information Derivation from Big Sensor Data via Edge Computing |
| title_fullStr | Real-Time Information Derivation from Big Sensor Data via Edge Computing |
| title_full_unstemmed | Real-Time Information Derivation from Big Sensor Data via Edge Computing |
| title_short | Real-Time Information Derivation from Big Sensor Data via Edge Computing |
| title_sort | real time information derivation from big sensor data via edge computing |
| topic | real-time big sensor data analytics architecture internet of things edge computing |
| url | https://www.mdpi.com/2504-2289/1/1/5 |
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