Congestion-Aware Suspicious Object Detection System Using Information-Centric Networking

• Published in: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) pp. 1 - 6
• Main Authors: Qi, Xin, Sato, Toshio, Yu, Keping, Wen, Zheng, Myint, San Hlaing, Katsuyama, Yutaka, Tokuda, Kiyohito, Sato, Takuro
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.01.2021
• Subjects: 5G mobile communication; decentralization; ICN; Information-centric networking; next-gen network; Surveillance; surveillance system; System performance; Systems architecture; Terrorism; Traffic control
• ISSN: 2331-9860
• DOI: 10.1109/CCNC49032.2021.9369510

Deadly diseases and terrorist attacks are greatly threatening human safety, which challenges global security. To address this issue, urban surveillance systems are being applied at a rapid pace with mature but inefficient solutions in large scale networks. When a surveillance network is managing the data generated from multiple edge nodes, it is easy to create congestions due to concentrated data traffic and inefficient data delivery mechanism. In parallel, 5G technology, cope with explosive mobile data traffic growth and massive device connections, can realize a true "Internet of Everything" and build the social and economical digital transformation. In this paper, in the context of 5G technology, we propose an Information-Centric Networking (ICN) surveillance system based on our designed Suspicious Object Network System (SONS) over the concept of next-generation networking. In this solution, the edge nodes in the network distribute the computing and data storage requirements. We first describe the current surveillance issues and our proposed system architecture. Then we use simulation to verify and evaluate the system performance between legacy all-to-one centralized surveillance system and ICN based decentralized surveillance system.