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

TL;DR

This paper presents SAFEMYRIDES, a decentralized edge computing system that uses local deep learning on IoT devices to improve ridesharing safety, privacy, and efficiency by reducing communication and data transfer.

Contribution

It introduces a novel decentralized-control edge model for IoT-based decision making, applied to real-time ridesharing violation detection.

Findings

Reduced communication and latency in ridesharing monitoring

Enhanced privacy and security by local data processing

Effective violation detection with optimized deep learning

Abstract

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.