# Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things

arXiv: https://arxiv.org/abs/2107.05828 · HTML: https://mlsanity.com/paper/2107.05828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy](https://mlsanity.com/paper/1806.07840/) (2018)
- [Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition](https://mlsanity.com/paper/1607.05427/) (2016)
- [Internet of Things Security and Forensics: Challenges and Opportunities](https://mlsanity.com/paper/1807.10438/) (2018)
- [On the Feasibility of Attribute-Based Encryption on Internet of Things Devices](https://mlsanity.com/paper/1611.08098/) (2016)

## Abstract (arXiv)

In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices. By partitioning and pipelining the CNN, it horizontally distributes the computation load among resource-constrained devices (called horizontal collaboration), which in turn increases the throughput. Through partitioning, we can decrease the computation and energy consumption on individual IoT devices and increase the throughput without sacrificing accuracy. Also, by processing the data at the generation point, data privacy can be achieved. The results show that throughput can be increased by 1.55x to 1.75x for sharing the CNN into two and three resource-constrained devices, respectively.