One reason for the success of the IoT paradigm is certainly the introduction of miniaturized devices, which are able to interact and acquire information from the environment where they are placed in. The huge amount of data generated and shared every second is in constant increment, thus raising significant scalability issues. The spreading and continuous development of Internet of Things (IoT) technologies and services introduces a new way of conceiving and managing the information transmitted over the network.
The final goal is to reveal the best approach to be used depending on the application’s requirements. Hence, the effectiveness of the presented solutions is validated by means of a real test-bed in the smart home scenario, in terms of storage occupancy, CPU load, and data retrieval delay. In this paper, we compare the advantages and the drawbacks in terms of performance and robustness of such two techniques by means of their integration within the prototype of an IoT middleware, named networked smart object. Sticky policies also guarantee a highly distributed and customizable enforcement of access control rules. On the other side, approaches based on sticky policies allow to attach an access policy directly to the data itself, and to employ a trusted authority to evaluate and enforce the policy itself. ABE guarantees a high level of customization due to the variety of attributes which can be defined, and it is also flexible enough to be adapted to different kinds of scenarios. On the one side, approaches based on attribute-based encryption (ABE) allow one to encrypt data for multiple recipients, in such a way that only those recipients whose attributes satisfy a given access policy can decrypt afterward. The present paper considers the specific context of a smart home, which represents one of the main IoT application domains, and it focuses on two solutions proposed in the literature to cope with the aforementioned issues. Regulating the access to the Internet of Things (IoT) network’s resources is a complex-prone task, which requires to pay a great attention on how policies are defined, shared, and enforced.
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