Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

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  • Abeysiriwardhana W.A. Shanaka P.
    School of Science for Open and Environmental Systems, Keio University Faculty of Science and Technology
  • Wijekoon Janaka L.
    Faculty of Computing, Sri Lanka Institute of Information Technology SLIIT Malabe Campus
  • Nishi Hiroaki
    School of Science for Open and Environmental Systems, Keio University Faculty of Science and Technology

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<p>A smart community utilizes information technology to interconnect and manage community infrastructures. Smart community networks should support a large number of Internet of Things (IoT) devices in community infrastructures to provide services such as smart grids and health monitoring systems. In comparison to cloud-based solutions, smart community services can be deployed in the edge computing area to reduce service latency and to encapsulate private and local information. Furthermore, smart community services can leverage network virtualization technologies to support IoT network services at the edge. A service-oriented container-based solution that processes data streams from IoT sensors using conventional hardware will improve the compatibility and latency of these virtualized network services at the edge. To this end, a software-based edge computing node, namely, the smart community edge (SCE), was proposed to develop a platform for smart community services. SCE supports data-tapping applications, especially for IoT devices, and has a stream processing feature with a comparatively shorter processing delay. This tapping and processing function was named multi-service authorized stream content analysis. SCE captures network stream data and enables service applications using shared memory buffers for a shorter processing delay. SCE supports services as Docker containers to provide remote deployment, service compatibility, and service isolation. SCE allows IoT services to run at the edge through conventional hardware devices, thus, reducing the service latency for delay-sensitive services, which approximately require to sustain latency less than 10 ms. The proposed SCE achieves 10 Gbps bandwidth with a 16 core server when compared to the f-stack library with a 5 Gbps bandwidth. SCE deployment on conventional hardware devices shows its capability of operating at 1-10 Gbps line rates to support up to eight services at 500 Mbps data bandwidth per service, while keeping the overall latency below 1 ms. Therefore, SCE provides a platform for delay-sensitive IoT services at the network edge.</p>

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