# Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

arXiv: https://arxiv.org/abs/2107.05949 · HTML: https://mlsanity.com/paper/2107.05949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Context Aware Computing for The Internet of Things: A Survey](https://mlsanity.com/paper/1305.0982/) (2013)
- [SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT](https://mlsanity.com/paper/2105.14915/) (2021)

## Abstract (arXiv)

As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of its services and devices is becoming a fundamental requirement for addressing the uncertainties of the environment in decision-making processes. Self-adaptation of HCIoT aims to manage run-time changes in a dynamic environment and to adjust the functionality of IoT objects in order to achieve desired goals during execution. SMASH is a semantic-enabled multi-agent system for self-adaptation of HCIoT that autonomously adapts IoT objects to uncertainties of their environment. SMASH addresses the self-adaptation of IoT applications only according to the human values of users, while the behavior of users is not addressed. This article presents Q-SMASH: a multi-agent reinforcement learning-based approach for self-adaptation of IoT objects in human-centered environments. Q-SMASH aims to learn the behaviors of users along with respecting human values. The learning ability of Q-SMASH allows it to adapt itself to the behavioral change of users and make more accurate decisions in different states and situations.