The “Internet of Things” (IoT) provide humans and smart objects with attractive services, based on the advanced features of the IoT devices, like high sensing, real-time acting and reasoning.

In our previous research we have highlighted that these features can be improved by promoting cooperation between smart objects, and we introduced the association between Multi-Agent Systems and IoT devices. In that context, we focused on the issue of accurately choosing the best partners for cooperation, in a scenario composed by several federations. We proposed a reputation model and we have shown that the model leads to detect agents having unreliable or misleading behaviors and that the model itself can be profitably used to form groups of agents that mutually cooperate for improving the effectiveness of their tasks. In this further contribution, we focus on the important issue of the group formation, by arguing that in practical IoT situations it is necessary to improve the group formation strategy to provide it with greater adaptability. To this end we introduce – in a particular IoT context described in this work – a two-phase group formation algorithm to support the reputation model. Experimental results prove that the adoption of the group formation algorithm, along with the proposed reputation model provides a few benefits to the whole IoT ecosystem.

物联网（IoT）基于物联网设备的高级功能（如高感测，实时行为和推理），为人类和智能对象提供有吸引力的服务。

在我们之前的研究中，我们强调了可以通过促进智能对象之间的协作来改善这些功能，并且我们介绍了多代理系统与IoT设备之间的关联。在这种情况下，我们关注的问题是在由多个联合会组成的情况下，准确选择最佳合作伙伴进行合作。我们提出了一种信誉模型，并且我们已经表明，该模型可以发现具有不可靠或误导性行为的代理，并且该模型本身可以有利地用于形成相互协作以提高其任务效率的代理组。在进一步的贡献中，我们认为在物联网的实际情况下，有必要改进群体形成策略以使其具有更大的适应性，从而将重点放在群体形成的重要问题上。为此，在此工作中描述的特定物联网环境中，我们介绍了一个两阶段的组形成算法来支持信誉模型。实验结果证明，采用组形成算法以及所提出的信誉模型为整个物联网生态系统提供了一些好处。