Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities

Abstract

Fog computing is emerging an attractive paradigm for both academics and industry alike. Fog computing holds potential for new breeds of services and user experience. However, Fog computing is still nascent and requires strong groundwork to adopt as practically feasible, cost-effective, efficient and easily deployable alternate to currently ubiquitous cloud. Fog computing promises to introduce cloud-like services on local network while reducing the cost. In this paper, we present a novel resource efficient framework for distributed video summarization over a multi-region fog computing paradigm. The nodes of the Fog network is based on resource constrained device Raspberry Pi. Surveillance videos are distributed on different nodes and a summary is generated over the Fog network, which is periodically pushed to the cloud to reduce bandwidth consumption. Different realistic workload in the form of a surveillance videos are used to evaluate the proposed system. Experimental results suggest that even by using an extremely limited resource, single board computer, the proposed framework has very little overhead with good scalability over off-the-shelf costly cloud solutions, validating its effectiveness for IoT-assisted smart cities.