Decentralized Smart Surveillance through Microservices Platform
This work addresses public safety technology needs for connected societies by offering a flexible and efficient edge computing solution, though it appears incremental as it builds on existing microservices and edge paradigms.
The paper tackles the challenge of limited computing power at the edge for dynamic data processing in surveillance by proposing a lightweight IoT-based smart public safety framework on a microservices architecture, demonstrating its feasibility in a real-world monitoring scenario for detecting, tracking, and identifying suspicious activities.
Connected societies require reliable measures to assure the safety, privacy, and security of members. Public safety technology has made fundamental improvements since the first generation of surveillance cameras were introduced, which aims to reduce the role of observer agents so that no abnormality goes unnoticed. While the edge computing paradigm promises solutions to address the shortcomings of cloud computing, e.g., the extra communication delay and network security issues, it also introduces new challenges. One of the main concerns is the limited computing power at the edge to meet the on-site dynamic data processing. In this paper, a Lightweight IoT (Internet of Things) based Smart Public Safety (LISPS) framework is proposed on top of microservices architecture. As a computing hierarchy at the edge, the LISPS system possesses high flexibility in the design process, loose coupling to add new services or update existing functions without interrupting the normal operations, and efficient power balancing. A real-world public safety monitoring scenario is selected to verify the effectiveness of LISPS, which detects, tracks human objects and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.