Multi-Agent Trust Decision Process for the Internet of Things


Multi-Agent Trust Decision Process for the Internet of Things

The deployment of Internet of Things systems in open and dynamic environments raises several issues related to the reliability of the its components. It is unrealistic to consider that every hardware or software component is reliable, trustworthy and efficient whatever the conditons, especially climatic.


The MaestrIoT project will address these issues by proposing an algorithmic framework for ensuring trust in a multi-agent system handling sensors and actuators of a cyber-physical environment. Trust management has to be ensured from the perception to decision making and integrating the exchange of information between IoT devices.


These theoretical contributions will be applied in two privileged domains, Industry 4.0 and Connected Cooperative Automated Mobility, with demonstrators both in full simulation and hybridizing simulation and real platforms.

The networking of connected devices, also referred to as the Internet of Things (IoT), has been developed significantly in recent years and is expected to increase further with the arrival of 5G. IoT applications are numerous and diverse. They can now be deployed in open, dynamic environments composed of heterogeneous objects. In this type of environment, smart devices have to discover on the fly other devices within their communication range, as well as the information and services they offer. They may also receive requests from these objects.


Interoperability is a key factor to enable communication between heterogeneous objects having different designers (both for hardware and software), different system owners and different users. The field of Web of Things (WoT) proposes to rely on the standard protocols, data format and technologies of the Web to foster this interoperability. This technological support should result in a system that benefits from interesting adaptability capabilities, but it also rises new issues because it has significant vulnerabilities facing failures and attacks.