کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11021078 1715041 2018 40 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Energy efficient distributed analytics at the edge of the network for IoT environments
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
پیش نمایش صفحه اول مقاله
Energy efficient distributed analytics at the edge of the network for IoT environments
چکیده انگلیسی
Due to the pervasive diffusion of personal mobile and IoT devices, many “smart environments” (e.g., smart cities and smart factories) will be, generators of huge amounts of data. Currently, analysis of this data is typically achieved through centralised cloud-based services. However, according to many studies, this approach may present significant issues from the standpoint of data ownership, as well as wireless network capacity. In this paper, we exploit the fog computing paradigm to move computation close to where data is produced. We exploit a well-known distributed machine learning framework (Hypothesis Transfer Learning), and perform data analytics on mobile nodes passing by IoT devices, in addition to fog gateways at the edge of the network infrastructure. We analyse the performance of different configurations of the distributed learning framework, in terms of (i) accuracy obtained in the learning task and (ii) energy spent to send data between the involved nodes. Specifically, we consider reference wireless technologies for communication between the different types of nodes we consider, e.g. LTE, Nb-IoT, 802.15.4, 802.11, etc. Our results show that collecting data through the mobile nodes and executing the distributed analytics using short-range communication technologies, such as 802.15.4 and 802.11, allows to strongly reduce the energy consumption of the system up to 94% with a loss in accuracy w.r.t. a centralised cloud solution up to 2%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pervasive and Mobile Computing - Volume 51, December 2018, Pages 27-42
نویسندگان
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