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.

研究介绍了一种基于去中心化控制边缘模型的场景感知拼车监控系统，该系统使用边缘计算和深度学习，避免了数据传输产生的安全和隐私风险，提高了效率和降低了延迟。