IoT intelligence empowered by end-edge-cloud orchestration

To support intelligent Internet of Things (IoT) applications, such as autonomous driving, smart city surveillance, and virtual reality (VR)/augmented reality (AR), cloud services are expected to be pushed to the proximity of IoT devices for quality performance. For instance, to facilitate safe autonomous driving, the service delay of most vehicular applications is required to be within milliseconds, and any information delay may result in dangerous on-road conditions. Edge intelligence aims at processing data/computing-intensive IoT tasks at the edge of network, where a set of IoT devices can work cooperatively for data collection, processing, model training, caching, and data analytics via edge caching, edge training, edge offloading, etc. It empowers intelligent IoT services at the network edge. However, it is challenging to achieve satisfying performance due to the following reasons. On the one hand, as the edge nodes are constrained by storage/computing resources, it is essential to conduct end-edge-cloud resource orchestration and resource sharing, taking into account device mobility, burst & stochastic service requests, and heterogeneous resources. On the other hand, better quality of service/learning of IoT applications is difficult to be guaranteed as the task execution/model training is contributed by distributed IoT devices. As a result, individual quality characterization, participating node selection, multi-level collaboration, robustness against malicious attacks are crucial. At last, to enable IoT intelligence, frequent communication and coordination among IoT devices, edge nodes and cloud servers are required, which can raise significant overhead, delay, and potential disclosure of sensitive information. Overcoming those challenges calls for further in-depth research.