Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.

中文翻译：

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基于区块链的协作边缘智能，用于可信赖的实时视频监控

可信的实时视频监控旨在以保护隐私的方式分析实时摄像机流，以便为行人重新识别和交通监控等各种高级服务做出决策。近年来，边缘计算已被确定为可信赖和实时视频监控的有前途的技术，因为它可以将机密视频数据保存在本地并减少海量数据传输造成的延迟。通常，单个边缘设备很难承担计算密集型视频分析任务。大多数现有解决方案都包含云服务器来处理超载任务。然而，这种边云协作方法仍然存在不可预测的延迟和隐私问题，因为远程云是集中的并且远离摄像头。在这项工作中，我们设计了一种基于区块链的协作边缘智能 (BCEI) 方法，用于可信赖的实时视频监控。在 BCEI 中，地理分布式边缘设备形成一个点对点网络，以维护一个许可的区块链并共享数据和计算资源以执行计算密集型视频分析任务。视频分析结果以不可更改的方式写入区块链，以保证可信度。为了减少任务执行时间，我们制定并解决了联合流映射和任务调度问题，以在边缘设备之间调度视频流和机器学习模型。基于 BCEI 实施和部署了行人再识别原型，并进行了广泛的性能评估，