  It is a big challenge for resource-limited mobile devices (MDs) to execute
various complex and energy-consumed mobile applications. Fortunately, as a
novel computing paradigm, edge computing (MEC) can provide abundant computing
resources to execute all or parts of the tasks of MDs and thereby can greatly
reduce the energy of MD and improve the QoS of applications. However,
offloading workflow tasks to the MEC servers are liable to external security
threats (e.g., snooping, alteration). In this paper, we propose a security and
energy efficient computation offloading (SEECO) strategy for service workflows
in MEC environment, the goal of which is to optimize the energy consumption
under the risk probability and deadline constraints. First, we build a security
overhead model to measure the execution time of security services. Then, we
formulate the computation offloading problem by incorporating the security,
energy consumption and execution time of workflow application. Finally, based
on the genetic algorithm (GA), the corresponding coding strategies of SEECO are
devised by considering tasks execution order and location and security services
selection. Extensive experiments with the variety of workflow parameters
demonstrate that SEECO strategy can achieve the security and energy efficiency
for the mobile applications.
