Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach

Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.背景：计算机网络正以前所未有的规模持续扩张。现代网络不仅包含计算机设备，还涵盖了从移动电话到搭载传感器的各类家用互联设备。这种物联网（Internet of Things, IoT）愿景给问题建模带来了固有挑战。 目的：针对大规模复杂自适应通信网络与环境（Complex Adaptive Communication Networks and Environments, CACOONS）而言，实际中无法完成其所有场景的部署与测试。本研究旨在探索将基于智能体建模（Agent-based Modeling）应用于认知基于智能体计算（Cognitive Agent-based Computing, CABC）框架，以对复杂通信网络问题开展建模。 方法：本研究依托CABC框架，采用探索式基于智能体建模（Exploratory Agent-based Modeling, EABM），开发了用于企业网络碳足迹管理的自主多智能体架构。为评估复杂性在实际场景中的应用效果，本研究同时引入了企业制定的计算机使用规范。 结果：本次实验得出两项重要结论：其一，基于CABC的建模方法（如基于智能体建模）可有效解决物联网领域的复杂问题建模难题；其二，碳足迹管理这一特定问题可通过多智能体系统方法实现解决。