School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006,China.
School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
Sensors (Basel). 2019 Mar 4;19(5):1105. doi: 10.3390/s19051105.
As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.
作为物联网（IoT）中一种新兴且有前途的计算范例，边缘计算可以显著降低能耗并增强资源受限的 IoT 设备的计算能力。计算卸载最近在边缘计算中受到了相当多的关注。许多现有研究已经研究了具有独立计算任务的计算卸载问题。然而，由于 IoT 系统中各种设备之间通常存在的任务间依赖关系，实现节能的计算卸载决策仍然是一个具有挑战性的问题。在本文中，我们引入了一种具有 IoT 环境中三层网络的云辅助边缘计算框架。在这个框架中，我们首先将最小化能耗问题表述为一个混合整数规划问题，考虑了两个约束，即任务依赖要求和 IoT 服务的完成时间截止日期。为了解决这个问题，我们提出了一种基于半定松弛和随机映射方法的节能协作任务计算卸载（ECTCO）算法，以获得 IoT 传感器的任务计算卸载策略。仿真结果表明，云辅助边缘计算框架是可行的，所提出的 ECTCO 算法可以有效地降低 IoT 传感器的能耗成本。
