Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing
Wireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For the accuracy i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/7/4/63 |
| _version_ | 1798040326125912064 |
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| author | John Yoon |
| author_facet | John Yoon |
| author_sort | John Yoon |
| collection | DOAJ |
| description | Wireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing, or disappearing. If the abrupt appearance of sensors is a zero-day attack, and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The predefined sensor requirements or meta-data-based sensor verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only. The proposed verification on sensors can be done without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes (1) quality preservation of sensor data for mining analytics. The sensor data are trained to identify their characteristics of outliers: whether they are attack outliers, or outlier-like abrupt changes in environments; and (2) authenticity verification of dynamically moving sensors, which was possible. Previous unknown sensors are also identified by deep-learning approach. |
| first_indexed | 2024-04-11T22:06:03Z |
| format | Article |
| id | doaj.art-6a7494c4949a4fcb94b22f1ebc53bc85 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-11T22:06:03Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-6a7494c4949a4fcb94b22f1ebc53bc852022-12-22T04:00:43ZengMDPI AGComputers2073-431X2018-11-01746310.3390/computers7040063computers7040063Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge ComputingJohn Yoon0Math/Computer Science Department, Mercy College, New York, NY 10522, USAWireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing, or disappearing. If the abrupt appearance of sensors is a zero-day attack, and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The predefined sensor requirements or meta-data-based sensor verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only. The proposed verification on sensors can be done without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes (1) quality preservation of sensor data for mining analytics. The sensor data are trained to identify their characteristics of outliers: whether they are attack outliers, or outlier-like abrupt changes in environments; and (2) authenticity verification of dynamically moving sensors, which was possible. Previous unknown sensors are also identified by deep-learning approach.https://www.mdpi.com/2073-431X/7/4/63sensor collaborationssensor trustworthinessdynamic moving-sensor collaborationsensor calibration |
| spellingShingle | John Yoon Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing Computers sensor collaborations sensor trustworthiness dynamic moving-sensor collaboration sensor calibration |
| title | Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing |
| title_full | Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing |
| title_fullStr | Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing |
| title_full_unstemmed | Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing |
| title_short | Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing |
| title_sort | trustworthiness of dynamic moving sensors for secure mobile edge computing |
| topic | sensor collaborations sensor trustworthiness dynamic moving-sensor collaboration sensor calibration |
| url | https://www.mdpi.com/2073-431X/7/4/63 |
| work_keys_str_mv | AT johnyoon trustworthinessofdynamicmovingsensorsforsecuremobileedgecomputing |