Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

By Bo Tang, Zhen Chen, Gerald Hefferman, Shuyi Pei, Tao Wei, Haibo He, Qing Yang. 2017

303 citations recorded by the source corpus.

Data intensive analysis is the major challenge in smart cities because of the ubiquitous deployment of various kinds of sensors. The natural characteristic of geodistribution requires a new computing paradigm to offer location-awareness and latency-sensitive monitoring and intelligent control. Fog Computing that extends the computing to the edge of network, fits this need. In this paper, we introduce 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 integrate intelligence in our Fog Computing architecture, e.g., to perform data representation and feature extraction, to identify anomalous and hazardous events, and to offer optimal responses and controls. 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: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “performance can be improved due to the avoidance of transmitting large amounts of raw data to distant cloud nodes [204].”
A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “Thus, edge computing may be a good choice to balance the efficiency and timeliness of IoT data fusion [128-130].”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “Incontrast, Tang et al. [132] adopt the idea of coniguring diferent AI algorithms in the edge and the cloud.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “For instance, the hierarchical distributed edge computing architecture in [122] can support the integration of massive infrastructure components and services in future smart cities.”
Deploying Fog Computing in Industrial Internet of Things and Industry 4.0 (2018, background): “The data provide a means to create tailored services if the required data analytics are applied [8].”