Security and Energy-aware Collaborative Task Offloading in D2D communication

Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions

Security modeling and efficient computation offloading for service workflow in mobile edge computing

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.Comment: published in journal "Future Generation Computer Systems": https://doi.org/10.1016/j.future.2019.03.01