منوی کاربری
  • پشتیبانی: ۴۲۲۷۳۷۸۱ - ۰۴۱
  • سبد خرید

دانلود رایگان مقاله انگلیسی توصیف تاثیر توپولوژی در پردازش جریان اینترنت اشیا - IEEE 2018

عنوان فارسی
توصیف تاثیر توپولوژی در پردازش جریان اینترنت اشیا
عنوان انگلیسی
Characterizing the impact of topology on IoT stream processing
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
6
سال انتشار
2018
نشریه
آی تریپل ای - IEEE
فرمت مقاله انگلیسی
PDF
نوع مقاله
ISI
پایگاه
اسکوپوس
کد محصول
E9187
رشته های مرتبط با این مقاله
مهندسی فناوری اطلاعات
گرایش های مرتبط با این مقاله
اینترنت و شبکه های گسترده، شبکه های کامپیوتری
مجله
چهارمین انجمن جهانی در اینترنت اشیا - 4th World Forum on Internet of Things
دانشگاه
Intelligent Platforms & Architecture Lab - University of Washington - Tacoma - WA - USA
کلمات کلیدی
اینترنت اشیا، سرور، توپولوژی، سنسورها، یادگیری ماشین
doi یا شناسه دیجیتال
https://doi.org/10.1109/WF-IoT.2018.8355119
۰.۰ (بدون امتیاز)
امتیاز دهید
چکیده

Abstract


The Internet of Things (IoT) extends traditional cyber-physical systems by linking sensor based edge devices to network accessible services and resources. In most current IoT deployments, sensor data is streamed from edge devices to servers for storage. Analytical pipelines are then used to translate this raw sensor data into actionable information in real-time. As additional IoT devices are deployed, the volume and rate of data received on the server side can increase dramatically. This has a possibility of offsetting the response latencies beyond acceptable limits for IoT analytical systems. In this paper, we compare the impact of alternative serverside stream processing topologies for ingesting and analyzing IoT sensor data in real-time. We use real building sensor data with our real-time IoT platform called Namatad. We have characterized and analyzed the latency and QoS impact due to the different levels of granularity of the ingestion and routing process by which we transmit data into the analytical pipelines. Our results show that as IoT systems continue to scale in density, server-side topology management for IoT data streams is critical for latency-sensitive control and analysis applications.

نتیجه گیری

VII. CONCLUSION


In this paper, we explored the impact of alternative realtime streaming topologies within the edge server of IoT analytical systems. We evaluated these topologies in terms of the time to insight from our machine learning models as well as the quality of predictions. Our results show that topology impacts stream processing in multiple ways and real world parameters like missing values, out of order arrivals, varying sparsity have a significant impact as we scale up the density of sensor deployments.


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