# Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior

arXiv: https://arxiv.org/abs/2109.07239 · HTML: https://mlsanity.com/paper/2109.07239/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids](https://mlsanity.com/paper/2006.16100/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)
- [TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing](https://mlsanity.com/paper/2210.10147/) (2022)
- [Needs and Artificial Intelligence](https://mlsanity.com/paper/2203.03715/) (2022)

## Abstract (arXiv)

Pandemics and natural disasters over the years have changed the behavior of people, which has had a tremendous impact on all life aspects. With the technologies available in each era, governments, organizations, and companies have used these technologies to track, control, and influence the behavior of individuals for a benefit. Nowadays, the use of the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) have made it easier to track and change the behavior of users through changing IoT behavior. This article introduces and discusses the concept of the Internet of Behavior (IoB) and its integration with Explainable AI (XAI) techniques to provide trusted and evident experience in the process of changing IoT behavior to ultimately improving users' behavior. Therefore, a system based on IoB and XAI has been proposed in a use case scenario of electrical power consumption that aims to influence user consuming behavior to reduce power consumption and cost. The scenario results showed a decrease of 522.2 kW of active power when compared to original consumption over a 200-hours period. It also showed a total power cost saving of 95.04 Euro for the same period. Moreover, decreasing the global active power will reduce the power intensity through the positive correlation.