4.5 Article

A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services Through Intelligent Resource-Aware Offloading

Journal

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Volume 16, Issue 4, Pages 1665-1680

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2019.2939221

Keywords

Cloud computing; Mobile handsets; Intrusion detection; Performance evaluation; Servers; Ad hoc networks; Smart offloading; resource-aware service offloading; ad-hoc mobile edge cloud; edge computing; cloud computing; mobile computing; sustainable security services; security-as-a-service; intrusion detection

Funding

  1. Lebanese American University
  2. ETS Montreal
  3. Khalifa University

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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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