# An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms

arXiv: https://arxiv.org/abs/2509.10507 · HTML: https://mlsanity.com/paper/2509.10507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 1 Sep 2025.

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

- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)
- [Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://mlsanity.com/paper/2303.00524/) (2023)

## Abstract (arXiv)

Internet of Intelligent Things (IoIT), an emerging field, combines the utility of Internet of Things (IoT) devices with the innovation of embedded AI algorithms. However, it does not come without challenges, and struggles regarding available computing resources, energy supply, and storage limitations. In particular, many impediments to IoIT are linked to the energy-efficient deployment of machine learning (ML)/deep learning (DL) models in embedded devices. Research has been conducted to design energy-efficient IoIT platforms, but these papers often focus on centralized systems, in which some central entity processes all the data and coordinates actions. This can be problematic, e.g., serve as bottleneck or lead to security concerns. In a decentralized system, nodes/devices would self-organize and make their own decisions. Therefore, to address such issues, we propose a heterogeneous, decentralized sensing and monitoring IoIT peer-to-peer mesh network system model. Nodes in the network will coordinate towards several optimization goals: reliability, energy efficiency, and latency. The system employs federated learning to train nodes in a distributed manner, metaheuristics to optimize task allocation and routing paths, and multi-objective optimization to balance conflicting performance goals.