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Edge computing is an emerging computing paradigm representing decentralized and distributed information technology architecture [1] . The demand for edge computing is primarily driven by the increased number of smart devices and the Internet of Things (IoT) that generate and transmit a substantial amount of data, that would otherwise be stored on cloud computing services. The edge architecture enables data and computation to be performed in close proximity to users and data sources and acts as the pathway toward upstream data centers [2] . Rather than sending data to the cloud for processing, the analysis and work is done closer to where the source of the data is generated ( Figure 1 ). Edge services leverage local infrastructure resources allowing for reduced network latency, improved bandwidth utilization, and better energy efficiency compared to cloud computing.
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Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge
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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- PAR ID:
- 10098740
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
- Page Range / eLocation ID:
- 511 to 516
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PERCOMW.2019.8730577
