From the reputation point of view we also investigated how in the context of federated identity management trust perception can be exported by using a federated reputation system. We propose a model for deriving trust in online services. In this context, trust is defined as the level of confidence that the service provider holds on the subject interacting with it to behave in a proper way while using the service. Thus, we derive trust by using the reputation values that those users have gained for interacting with these services ^[5].

Then, our research considered the inclusion of trust and reputation management since the beginning of the development of software services by following the Software Development LifeCycle (SDLC). This work was carried out mainly within the scope of the NESSoS EU project, and which intersects with another research area: Secure Service Engineering. The first step towards the holistic inclusion of trust and reputation was the elicitation of a trust conceptual framework where we identified the underlying core concepts of most trust models, abstracting away from the particularities of concrete models ^[6].

Using the conceptual framework, our focus is on a development framework that allows building trust and reputation models in services and applications. We have thus designed different components that include trust and reputation for each of the different phases of the SDLC.  More specifically, we are exploring how trust can be used to make reconfiguration decisions in self-adaptive systems. For the requirements phase we have considered different ways to elicit them by designing an extension of UML ^[7], using extensions of SI* ^[8], patterns ^[9], ^[10]. A very related requirement  to trust is privacy. We have considered these relationship in the following works ^[11], ^[12]. These two last works considered a specific case of trust models which are negotiation models. Other works for these type of models considered also the languages needed in order to include them into the SDLC ^[13] and ^[14].

The development framework that we have proposed is based on the so-called models@run.time. We called our approach trust@run.time ^[15]. This framework integrates trust and reputation into a distributed component-model that implements the models@run.time paradigm, thus allowing the system to include trust in their reasoning process.

As the growth of the IoT paradigm is a fact and this paradigm is being largely used, lately, we have developed a framework for the inclusion of trust and reputation to the design and development of IoT scenarios ^[16]. In order to realise this framework we have to develop each of the layers that it is composed of ^[17], ^[18], ^[19], ^[20], ^[21]. Part of this work have been carried out in the scope of the projects NeCS  and PRECISE. 

In particular, we are considering the applications of IoT for smart home scenarios ^[22]. In order to achieve this task, we have published a survey on IoT trust model frameworks ^[23]. The survey emphasizes the importance of considering trust throughout the System Development Life Cycle (SDLC) for IoT entities. It offers an analysis of various frameworks developed to incorporate trust into IoT, classifying them based on critical parameters such as context, trust characteristics and trust related properties (i.e., security, privacy, identity). This classification highlights significant gaps and proposes potential solutions, offering a roadmap for developing comprehensive frameworks that effectively implement trust in IoT.

Furthermore, the challenge of trust interoperability in the IoT has been addressed in ^[24]. In the heterogeneous IoT paradigm, entities often establish collaborations with others, necessitating a shared understanding of trust despite differing trust models. This paper proposes an interoperability framework designed to bridge the gap between diverse trust models in IoT scenarios. By enabling entities to process trust information from other entities with differing trust systems, the framework fosters seamless trust establishment and management. This approach ensures that trust relationships can be established and maintained even in highly diverse IoT environments, supporting more robust interconnections and collaborative operations. These latest works have been part of the results of the collaboration project with Huawei Technologies [Trust IoT].

Finally, in ^[25], we investigated the application of trust in dynamic routing through the development of the DrATC (Dynamic Routing Algorithm Based on Trust Characteristics). This algorithm leverages various trust attributes—such as direct and indirect experiences, transitivity, directionality, and context dependence—to identify the most trusted path in a network. By tailoring the routing protocol to selectively include these attributes, DrATC adapts dynamically to varying network conditions and requirements. For example, it can prioritize direct trust for simple scenarios or incorporate both direct and indirect trust for more complex scenarios. Additionally, it explores alternative routes based on trust metrics to ensure sensitive data is routed through the most secure paths. Experimental results validate the algorithm’s efficacy in enhancing the reliability and security of data transmission, demonstrating the critical role of adaptability and context in trust-based routing decisions.

This latest research underscores the necessity of integrating trust into the IoT ecosystem and provides new tools and frameworks to address trust challenges, paving the way for secure and reliable IoT deployments.