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.

提出一个完整框架，根据用户需求，在物联网中定义适当的隐私和安全需求级别，通过采用基于强化学习的解决方案来训练用户代理，选择最佳的智能对象以满足目标服务，并确保满足截止日期要求、用户的安全和隐私需求。