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.

翻译：共同边缘计算模型正在改变许多行业和服务的面貌; 常见边缘计算模型正在卸载容易造成安全风险和侵犯隐私的计算机; 然而,深层次学习的进展使得Things(IoTs)互联网能够在当地作出决定和开展认知任务; 这项研究引入了分散控制边际模型,大多数计算和决定都转移到IoT一级; 该模型旨在将通信降低到边缘,从而提高效率和降低潜伏度; 该模型还避免了增加安全和隐私风险的数据传输; 为了审查模型,我们开发了SAFEMYRIDES, 这是一种景色共享监测系统,智能手机在运行时发现违规现象。 目前实时监测系统成本高昂,需要持续的网络连通性。 该系统优化了本地在IoTs上运行的深度学习,以探测在共享交通工具和记录违规事件中的违规现象。 该系统将加强在不侵犯隐私的情况下共享交通工具的安全和安保。