| Title |
An Edge Computing-enhanced Internet of Things Framework for Privacy-preserving in Smart City |
| ID_Doc |
37202 |
| Authors |
Gheisari, M; Wang, GJ; Chen, SH |
| Title |
An Edge Computing-enhanced Internet of Things Framework for Privacy-preserving in Smart City |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.compeleceng.2019.106504 |
| Abstract |
To supervise massive generated data by the Internet of Things (IoT) efficiently, we face two issues that should be addressed which are: (1) heterogeneity or satisfying diversity among IoT devices, and (2) privacy-preserving or preventing unintentional disclosure of sensitive data. Through observation, we found that existing solutions apply one common privacy-preserving rule for all devices while they address the heterogeneity issue separately that lead to unappealing performance. In this paper, we propose a framework for addressing the heterogeneity issue and privacy-preserving of IoT devices at the network edge using a novel proposed ontology data model. Besides, it leverages the proposed ontology to obtain a privacy-preserving method by frequently changing the privacy-preserving behaviors of loT devices. Through simulation, we show that our solution overhead is less than 9 percent in the worst situation so that it is affordable to most loT devices in one of its applications that is smart city. (C) 2019 Elsevier Ltd. All rights reserved. |
| Author Keywords |
Privacy-preserving; Smart city; Ontology; Edge computing; Internet of things; Owner; Privacy; IOT; Cloud computing |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000518675000018 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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