# SAFEMYRIDES: Application of Decentralized Control Edge-Computing to Ridesharing Monitoring Services

arXiv: https://arxiv.org/abs/2301.00888 · HTML: https://mlsanity.com/paper/2301.00888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [End-to-end training of object class detectors for mean average precision](https://mlsanity.com/paper/1607.03476/) (2016)
- [OpenEI: An Open Framework for Edge Intelligence](https://mlsanity.com/paper/1906.01864/) (2019)
- [Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector](https://mlsanity.com/paper/1811.08342/) (2018)
- [A Conceptual Paper on SERVQUAL-Framework for Assessing Quality of Internet of Things (IoT) Services](https://mlsanity.com/paper/2001.01840/) (2020)

## Abstract (arXiv)

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.