Surveillance cameras are ubiquitous nowadays and users’ increasing needs for accessing real-world information (e.g., finding abandoned luggage) have urged object queries in real-time videos. While recent real-time video query processing systems exhibit excellent performance, they lack utility in deployment in practice as they overlook some crucial aspects, including multi-camera exploration, resource contention, and content awareness. Motivated by these issues, we propose a framework Gecko, to provide resource-efficient and accurate real-time object queries of massive videos on edge devices. Gecko (i) obtains optimal models from the model zoo and assigns them to edge devices for executing current queries, (ii) optimizes resource usage of the edge cluster at runtime by dynamically adjusting the frame query interval of each video stream and forking/joining running models on edge devices, and (iii) improves accuracy in changing video scenes by fine-grained stream transfer and continuous learning of models. Our evaluation with real-world video streams and queries shows that Gecko achieves up to 2x more resource efficiency gains and increases overall query accuracy by at least 12% compared with prior work, further delivering excellent scalability for practical deployment.

This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes. Shoggoth uses online knowledge distillation to improve the accuracy of models suffering from data drift and offloads the labeling process to the cloud, alleviating constrained resources of edge devices. At the edge, we design adaptive training using small batches to adapt models under limited computing power, and adaptive sampling of training frames for robustness and reducing bandwidth. The evaluations on the realistic dataset show 15%–20% model accuracy improvement compared to the edge-only strategy and fewer network costs than the cloud-only strategy.

Solid-State Disks (SSD) are widely used in government and security departments owing to its faster speed of data access, more durability, more shock and drop, no noise, lower power consumption, lighter weight compared with Magnetic disk. As a result, the demand of security for storing data has been generated. The Advanced Encryption Standard (AES) is today's key data encryption standard for protecting data, but the implementation of high-speed AES encryption engine needs to consume a large number of hardware resources. This paper presents a low-cost and inner-round pipelined ECB-256-AES encryption engine. Through sharing the resources between the AES encryption module and the AES decryption module and using the look-up table for the SubBytes and InvSubBytes operations, the logic resources have been largely reduced; by using loop rolling and inner-round pipelined techniques, a high throughput of encryption and decryption operations is achieved. A 1.986Gbits/s throughput and 232.748MHz clock frequency are achieved using 614 slices of the Xilinx xc6slx45-3fgg484. The simulation results show that the AES crypto design is able to meet the read and write speed of SATA 1.0 interface.