Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

中文翻译：

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EDLIoT：一种在物联网中使用调度算法降低能耗和延迟的方法


在资源有限的网络（如物联网）中降低能耗一直是保证网络性能的主要挑战之一。本文采用合作博弈论来改进雾计算资源的合作模式。EDLIoT 方法包括两个主要步骤：“拓扑构建”和“确定最佳雾计算资源以处理 IoT 对象任务”。在所提出的方法的第一步中，确定了网络中一组可靠的通信，以树状结构的形式在物联网对象和雾计算资源之间建立连接。然后，在第二步中，使用基于合作博弈论和成本函数的模型来确定雾层中最优的计算资源，用于外包 IoT 对象的处理任务;在 EDLIoT 中，主动 IoT 对象在雾层而不是本地执行计算，以节省能源。这样做是为了让 IoT 对象在可能的情况下，根据计算资源的能耗、延迟和处理能力等特征在雾中发现最合适的处理资源。在仿真环境中评估了所提出的方法的效率，并将结果与以前的算法进行了比较。结果表明，使用 EDLIoT 方法，除了降低能耗和延迟外，还可以通过雾资源处理更多的计算任务，从而提高 IoT 用户的服务质量。