In recent years, with an increasing number of Internet of Things (IoT) devices, general cloud computing mode is hard to process large amounts of data with high Quality of Service (QoS). Edge computing is put forward to relieve the pressure of cloud servers, but most of them only focused on allocating tasks depending on cloud servers or edge servers with the virtualization technology. Resource-constrained smart mobile terminals (RC-SMTs) produce most of the data to be processed but some of them are usually not able to support even Docker technology. The cooperative computation capacity of RC-SMTs is potential but is often neglected by most researchers. However, there is little research focus on edge computing only among RC-SMTs without computing ability supported by servers. For this reason, this article proposes a framework named data-drive task offloading with a unified resource model (DDTO-URM) to manage the limited resource of IoT which enables the allocation of tasks constantly generated from the edge of the network. Then, a meta-heuristic algorithm called grouped crossover genetic algorithm (GCGA) is designed to obtain task offloading strategy under a resource-constrained environment. As a result, the computation capacity of the system is enhanced to cover the requirement by improving the utilization of RC-SMTs. Through the analysis of simulation, the proposed approach can deal with the problem of DDTO-URM better than benchmark algorithms under constraints, ensuring the real time and ultralightweight of the collaborative edge-computing system.

中文翻译：

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基于统一资源模型的资源受限终端数据驱动任务卸载方法

近年来，随着物联网（IoT）设备的不断增多，一般的云计算模式难以以高服务质量（QoS）处理大量数据。边缘计算是为了缓解云服务器的压力而提出的，但大多只是专注于依赖云服务器或边缘服务器利用虚拟化技术来分配任务。资源受限的智能移动终端 (RC-SMT) 产生大部分要处理的数据，但其中一些通常甚至无法支持 Docker 技术。RC-SMT 的协作计算能力是有潜力的，但往往被大多数研究人员忽视。然而，在没有服务器支持的计算能力的情况下，很少有研究只关注 RC-SMT 中的边缘计算。为此原因，本文提出了一个名为数据驱动任务卸载的框架，它具有统一的资源模型 (DDTO-URM) 来管理物联网的有限资源，从而能够分配不断从网络边缘生成的任务。然后，设计了一种称为分组交叉遗传算法（GCGA）的元启发式算法来获得资源受限环境下的任务卸载策略。结果，通过提高 RC-SMT 的利用率，增强了系统的计算能力以满足需求。通过仿真分析，所提出的方法可以在约束条件下比基准算法更好地处理DDTO-URM问题，确保协作边缘计算系统的实时性和超轻量级。