SAFEMYRIDES: Application of Decentralized Control Edge-Computing to Ridesharing Monitoring Services
This addresses safety and privacy concerns for ridesharing users, though it appears incremental as it builds on existing edge computing and deep learning approaches.
The researchers tackled the problem of security and privacy risks in ridesharing monitoring by introducing a decentralized-control edge model that moves computation to IoT devices, resulting in decreased communication, enhanced efficiency, and reduced latency without data transfer.
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.