基于Agent的IoT网络分布式决策支持建模方法
Agent-Based Modelling Approach for Distributed Decision Support in an IoT Network
摘要：
越来越多的新兴应用，例如，物联网、车辆通信、增强现实以及由于这些系统的互操作性要求而导致的日益增长的复杂性，导致需要改变用于这些网络的建模和分析的工具。基于Agent的建模（ABM）是一种自底向上的建模方法，它考虑了一个由多个自主智能体组成的网络之间的相互作用，为理解复杂环境中异构节点之间的相互作用提供了一个理想的框架。在这里，我们调查的ABM建模道路交通管理系统的通信方面的适用性，作为物联网（IoT）网络的一个例子。我们的模型，分析和比较各种媒体访问控制（MAC）层协议的两种不同的情况下，即不协调和协调。此外，我们使用三种不同的方法：集中式决策者，DESYNC和分散式学习MAC（L-MAC）的调度机制的协调方案作为一个高层次的MAC协议建模。结果清楚地表明，多个决策者之间的协调，以提高信息的准确性和频谱利用率的系统的重要性。
英文摘要：
An increasing number of emerging applications, e.g., internet of things, vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system, as an example of an Internet of Things (IoT) network. We model, analyze and compare various Medium Access Control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high level MAC protocol by using three different approaches: Centralized Decision Maker, DESYNC and decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple decision makers in order to improve the accuracy of information and spectrum utilization of the system.
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