Towards an Adaptive Multi-modal Traffic Analytics Framework at the Edge

TitleTowards an Adaptive Multi-modal Traffic Analytics Framework at the Edge
Publication TypeConference Paper
Year of Publication2019
AuthorsPettet, G., S. Sahoo, and A. Dubey
Refereed DesignationUnknown
Conference NameInternational Workshop on Pervasive Flow of Things
Date Published03/2019
Conference LocationKyoto, Japan

The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application's high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources.

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