Context-awareness of the IoT through the on-the-fly preference modeling

TL;DR

This paper proposes a formal framework using graph transformations and temporal logic for modeling user preferences in IoT, enabling dynamic, on-the-fly context-awareness and behavior prediction in multi-agent systems.

Contribution

It introduces a novel formal approach combining graph structures and logical inference for real-time preference modeling in IoT environments.

Findings

Preference models are built on-the-fly from observed behaviors.

Logical inference predicts future IoT system behaviors.

The approach enhances trustworthiness of context-aware IoT systems.

Abstract

The context-awareness of things that belong to IoT networks have to be considered in a distributed computation paradigm. In the paper we suggest the use of graph transformations and temporal logic as a formal framework for a knowledge representation of user/inhabitant behaviors in multi-agent systems. IoT networks are considered as graph structures. Dynamic preference models, understood as a priority in the selecting, is also introduced. Preference models as a result of observed behaviors base on formal logic, and they are built on-the-fly by software agents. Software agents gather knowledge about user preferences expressed in terms of logical specifications as well as suggest on-the-fly future behavior basing on the logical inference process using the semantic tableaux method. The predictive processes are result of some new and important events in the context of IoT systems that should meet a response. Due to the ubiquitous availability of cyber systems that interact with physical environments, there is a great need to develop technologies that target the whole IoT system as a context-awareness system. Formal approach increases the trustworthy of a system. A simple yet illustrative example is provided.