  The novel Internet of Things (IoT) paradigm is composed of a growing number
of heterogeneous smart objects and services that are transforming architectures
and applications, increasing systems' complexity, and the need for reliability
and autonomy. In this context, both smart objects and services are often
provided by third parties which do not give full transparency regarding the
security and privacy of the features offered. Although machine-based Service
Level Agreements (SLA) have been recently leveraged to establish and share
policies in Cloud-based scenarios, and also in the IoT context, the issue of
making end users aware of the overall system security levels and the
fulfillment of their privacy requirements through the provision of the
requested service remains a challenging task. To tackle this problem, we
propose a complete framework that defines suitable levels of privacy and
security requirements in the acquisition of services in IoT, according to the
user needs. Through the use of a Reinforcement Learning based solution, a user
agent, inside the environment, is trained to choose the best smart objects
granting access to the target services. Moreover, the solution is designed to
guarantee deadline requirements and user security and privacy needs. Finally,
to evaluate the correctness and the performance of the proposed approach we
illustrate an extensive experimental analysis.
