## 2 Related Works
### Trust and Reputation for IoT  
Providing models and tools to measure trustworthiness in IoT is a well-known problem for which several authors have proposed several solutions at various levels [11; 12]. For example, in [13], the authors take into account the various security challenges that come with IoT and CPS (Cyber Phisical systems), such as the detection of malicious attacks in the context of direct communication using, for instance, the 6 LoWPAN (IPv6 over low-power wireless personal area networks) protocol. The authors designed a trust and reputation TRM-based model for IoT to facilitate the cooperation between IoT devices in a network of IoT/CPS based on the behaviours of IoT devices. They measured the accuracy, robustness, and lightness of the proposed model using a wide set of simulations.  
In a more recent work [14], the authors studied the problem of the heterogeneity of IoT vs the security requirement in the same environment. They designed a novel IoT trust and reputation model built over distributed PNNs (probabilistic neural networks) to classify trustworthy nodes from malicious ones. The model particularly addresses the cold start problem in IoT environments: the characteristics of newly joined devices are analysed to give a starting trust value of reputation, and then, their behaviour is analysed over time. The processing is completely distributed along the nodes of the network to provide better availability. The authors also illustrate that the various capabilities and types of IoT devices are supported by the model itself by providing different levels of security depending on the sensitivity of the data being transmitted.  
In [12], the authors discuss the typical problems related to the integration of the existing trust and reputation models into the IoT environment: the large number of physical entities, the limited computation ability of physical entities, and the highly dynamic nature of the network. To address the limitations above, they present the design of IoTrust--a trust architecture that integrates SDN (Soft Defined Network) into IoT--and a cross-layer authorisation protocol based on IoTrust. They also provide--to address the problem of trust establishment by a particular model--a Behaviour-based Reputation Evaluation Scheme (BES) for the node and an Organisation Reputation Evaluation Scheme (ORES). Finally, they present a theoretical analysis and a few simulation results to validate the efficiency of BES and ORES.  
In [15], the authors discuss the various security and privacy issues related to machine-to-machine interactions (M2M) and H2M (human-to-machine) interactions. They consider factors like the trustworthiness of a user or the detection of a malicious user in the particular context of fog/edge computing. They propose a context-aware multi-user trust evaluation model to evaluate the trustworthiness of a user in a Fog-based IoT (FloT). In particular, they propose context-aware feedback and feedback crawler systems to make trust evaluation unbiased, effective, and reliable. They also introduce monitor mode for malicious/untrustworthy users to effectively monitor the behaviour and trustworthiness of a user. In the design of their approach, they provide several tunable factors, which can be tuned based on the system's requirements, and discuss a few simulation results to validate their approach. A few aspects of their work are interesting, such as the contextualisation of the problem of trust in edge/fog computing to support IoT devices. Nevertheless, in our work, we consider trust in the context of M2M interactions.