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HybridIoT: Integration of Hierarchical Multiple Access and Computation Offloading for IoT-Based Smart Cities

The Internet of Things (IoT) is an emerging technology that proffers to connect massive smart devices together and to the Internet. On the basis of IoT, a smart city is endowed with real-time monitoring, ubiquitous sensing, universal connectivity, and intelligent information processing and control....

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Bibliographic Details
Published in:IEEE network 2019-03, Vol.33 (2), p.6-13
Main Authors: Qian, Li Ping, Wu, Yuan, Ji, Bo, Huang, Liang, Tsang, Danny H. K.
Format: Article
Language:English
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Summary:The Internet of Things (IoT) is an emerging technology that proffers to connect massive smart devices together and to the Internet. On the basis of IoT, a smart city is endowed with real-time monitoring, ubiquitous sensing, universal connectivity, and intelligent information processing and control. An IoT-based smart city can offer various smart services to citizens and administrators, thus improving the utilization of public resources regarding transportation, healthcare, environment, entertainment, and energy. The integration of transmitting, computing, and caching is having a profound impact on the development of flexible and efficient IoT in smart cities. However, with the introduction of ultra dense networking (UDN) and mobile edge computing (MEC), we have to carefully consider a joint problem across the physical layer and MAC layer to enable the efficient transmission, computation, and caching of big IoT data generated by massive IoT devices distributed in a city. In doing so, efficient multiple access and computation offloading should be addressed in the physical layer and MAC layer, respectively. In this article, we propose a scalable and sustainable IoT framework that integrates UDN-based hierarchical multiple access and computation offloading between MEC and cloud to support the smart city vision. The proposed integrated framework can substantially reduce the end-to-end delay and energy consumption of computing data from massive IoT devices. Numerical comparison results are presented to show the efficiency of the proposed framework. In addition, we discuss a number of open research issues in implementing the proposed framework. Introduction
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.2019.1800149