Abstract
As an emerging and promising computing paradigm in the Internet of things (IoT), edge computing can significantly reduce energy consumption and enhance computation capability for resource-constrained IoT devices. Computation offloading has recently received considerable attention in edge computing. Many existing studies have investigated the computation offloading problem with independent computing tasks. However, due to the inter-task dependency in various devices that commonly happens in IoT systems, achieving energy-efficient computation offloading decisions remains a challengeable problem. In this paper, a cloud-assisted edge computing framework with a three-tier network in an IoT environment is introduced. In this framework, we first formulated an energy consumption minimization problem as a mixed integer programming problem considering two 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 Computation Offloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approach to obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstrated that the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithm could effectively reduce the energy cost of IoT sensors.
Highlights
With the explosive development of the Internet of Things (IoT), enormous sensors are connected through IoT techniques, and these sensors generate massive amounts of data and demand [1,2,3,4].due to the limitations of size, battery life and heat dissipation in IoT sensors, severely constrained resources cannot meet the increasingly complex application requirements [5]
Execution-energy greedy offloading strategy (EGOS): For each computing task on IoT sensors, it was greedily offloaded to the computation node that resulted in the minimizing executing energy consumption
We investigate an energy conservation problem of IoT sensors in cloud-assisted edge computing framework by optimization of the computation offloading strategy
Summary
With the explosive development of the Internet of Things (IoT), enormous sensors are connected through IoT techniques, and these sensors generate massive amounts of data and demand [1,2,3,4]. To tackle the inter-task dependency problem mentioned above, we modeled the task computation offloading problem of IoT sensors as an energy optimization problem while satisfying inter-task dependency and service completion time constraint. These tasks with dependency among various sensors were referred to as the collaborative task. Taking inter-task dependency and service completion time constraint into consideration, we formulated the computation offloading strategy problem as a mixed integer optimization problem on the cloud-assisted edge computing framework, aimed at minimizing the energy consumption of IoT sensors. The bold lowercase letter denotes a vector, while the bold uppercase letter denotes a matrix. The trace function of matrix G is denoted by Tr(G)
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