Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge
Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge
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DOI：
10.1109/percomw.2019.8730577
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发表时间：
2019-03
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影响因子：
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通讯作者：
Geoffrey Pettet;Saroj Sahoo;A. Dubey
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文献类型：
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作者：
Geoffrey Pettet;Saroj Sahoo;A. Dubey
物联网（IoT）需要通过地理上分布的设备进行分布式、大规模的数据收集。虽然物联网设备通常将数据发送到云端进行处理，但这对于带宽受限的应用来说是有问题的。雾计算和边缘计算（在数据收集地附近处理数据，并仅将结果发送到云端）变得更加流行，因为它降低了网络开销和延迟。边缘计算通常使用计算能力较低的设备，因此需要服务框架和中间件来高效地组合服务。虽然许多框架采用自上而下的视角，但服务质量是整个系统的一种涌现特性，往往需要自下而上的方法。我们将服务定义为多模式的，允许在资源和性能之间进行权衡。可以组合不同的模式以满足应用的高级目标，该目标被建模为一个函数。我们研究了一个关于统计纳什维尔交叉路口车辆交通量的案例。由于隐私和带宽限制，我们必须在边缘对交叉路口的视频进行目标检测和跟踪。我们探索了硬件和软件架构，并确定了各种模式。本文为阐述系统所呈现的在线优化问题奠定了基础，该问题在服务数量和受可用资源限制的服务质量之间进行权衡。
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.