Runtime protocol syntax and behavior inference for incompatible updates in IoT environments

Abstract

As interaction protocols are often updated without backward compatibility, interaction opportunities between smart objects may disappear. To overcome this, we can apply existing works which try to adapt one interaction protocol to another at runtime, assuming their specifications are given. However, they cannot generate a valid adapter if the specification of the updated protocol is not available. Several novel methods have been devised to infer the updated protocol without prior knowledge, but they cannot be applied to a runtime scenario or suffer from inefficiency problem. In this dissertation, we propose an intelligent protocol inference scheme using knowledge-based adaptive probing. Based on protocol domain knowledge, the proposed scheme adapts a legacy protocol message and evaluates whether it can be understood by the updated protocol agent. Iterative message adaptations eventually make a legacy protocol agent interact with its updated but incompatible version at runtime and have a meaningful interaction without the corresponding specification. When such protocol syntax inference is done, the proposed scheme infers the updated protocol behavior. To improve efficiency of state-of-the-art behavior learning algorithm, we incorporate the legacy protocol behavior with the its query selection procedure. Based on this knowledge-based query selection, we prevent the protocol behavior learning module from trying infeasible queries and wasting computing resources. The experiment result shows that the use of knowledge is effective to make a meaningful interaction between the incompatible versions with a reasonably small number of message adaptations and membership queries.