A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities

By Bo Tang, Zhen Chen, Gerald Hefferman, Tao Wei, Haibo He, Qing Yang. ASE BigData & SocialInformatics, 2015

298 citations recorded by the source corpus.

The ubiquitous deployment of various kinds of sensors in smart cities requires a new computing paradigm to support Internet of Things (IoT) services and applications, and big data analysis. Fog Computing, which extends Cloud Computing to the edge of network, fits this need. In this paper, we present a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to build large-scale, geospatial sensing networks, perform big data analysis, identify anomalous and hazardous events, and offer optimal responses in real-time. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.

Subfields: Electrical and Electronic Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/6aec26e2bc3423c71375956bcd2de6b064ebacc0

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In Electrical and Electronic Engineering

In Water Science and Technology

Ahmed M. Shahat Osman

Citations in the corpus

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, unknown): “Applications [20, 51, 61, 100, 154, 159, 174, 175, 190, 191, 247, 263, 271, 276, 287, 306, 320, 321, 323, 330–379]”
A novel big data analytics framework for smart cities (2019, methodology): “ Fog computing (also known as Edge computing) a technology which provides computing and storage between end devices and cloud data centers are used for data preprocessing to detect anomalous and hazardous events [31].”
Fog computing security: a review of current applications and security solutions (2017, methodology): “The Cisco IOx APIs enable Fog applications to communicate with IoT devices and Cloud systems by any user-defined protocol.”
The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, methodology): “to be identified by city authorities in real-time using fog computing nodes within the city boundaries, as for example for detection of various types of events that could threaten the pipelines integrity in the city [134].”
Resource Management in Fog/Edge Computing (2018, background): “Tasks that require prompt reaction are processed in fog/edge nodes, whereas complex or long-term analysis tasks are performed at more powerful cloud nodes [77, 125, 167].”
Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities (2017, methodology): “In this paper, we introduce a hierarchical distributed Fog Computing architecture for big data analysis in smart cities [14].”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, methodology): “Several references demonstrated this reduced latency such as Li et al. [46], Krishnan et al. [58], Gia et al. [68], Tang et al. [75], and Sarkar et al. [105], [106].”
From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework (2017, background): “[70], a hierarchical distributed fog computing that supports a huge number of infrastructural component and services for future smart cities was presented.”