Next Article in Journal
Self-Configuring IoT Service QoS Guarantee Using QBAIoT
Previous Article in Journal
Making Sense of the World: Framing Models for Trustworthy Sensor-Driven Systems
Previous Article in Special Issue
Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessArticle
Computers 2018, 7(4), 63; https://doi.org/10.3390/computers7040063

Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing

Math/Computer Science Department, Mercy College, New York, NY 10522, USA
Received: 17 September 2018 / Revised: 3 November 2018 / Accepted: 11 November 2018 / Published: 16 November 2018
(This article belongs to the Special Issue Mobile Edge Computing)
Full-Text   |   PDF [2378 KB, uploaded 16 November 2018]   |  

Abstract

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. View Full-Text
Keywords: sensor collaborations; sensor trustworthiness; dynamic moving-sensor collaboration; sensor calibration sensor collaborations; sensor trustworthiness; dynamic moving-sensor collaboration; sensor calibration
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Yoon, J. Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing. Computers 2018, 7, 63.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top