A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services Through Intelligent Resource-Aware Offloading
While the usage of smart devices is increasing, security attacks and malware affecting such terminals are briskly evolving as well. Mobile security suites exist to defend devices against malware and other intrusions. However, they require extensive resources not continuously available on mobile term...
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| Other Authors: | , , , |
| Format: | article |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10725/12695 https://doi.org/10.1109/TNSM.2019.2939221 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/8823954 |
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| Summary: | While the usage of smart devices is increasing, security attacks and malware affecting such terminals are briskly evolving as well. Mobile security suites exist to defend devices against malware and other intrusions. However, they require extensive resources not continuously available on mobile terminals, hence affecting their relevance, efficiency and sustainability. In this paper, we address the aforementioned problem while taking into account the devices limited resources such as energy and CPU usage as well as the mobile connectivity and latency. In this context, we propose an ad-hoc mobile edge cloud that takes advantage of Wi-Fi Direct as means of achieving connectivity, sharing resources, and integrating security services among nearby mobile devices. The proposed scheme embeds a multi-objective resource-aware optimization model and genetic-based solution that provide smart offloading decision based on dynamic profiling of contextual and statistical data from the ad-hoc mobile edge cloud devices. The carried experiments illustrate the relevance and efficiency of exchanging security services while maintaining their sustainability with or without the availability of Internet connection. Moreover, the results provide optimal offloading decision and distribution of security services while significantly reducing energy consumption, execution time, and number of selected computational nodes without sacrificing security. |
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