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IoT in the Fog: A Roadmap for Data-Centric IoT Development

Authors:

Sharief M. A. Oteafy,

Hossam S. HassaneinAuthors Info & Claims

IEEE Communications Magazine, Volume 56, Issue 3

Pages 157 - 163

https://doi.org/10.1109/MCOM.2018.1700299

Published: 01 March 2018 Publication History

Abstract

Our interactions with the world are increasingly dependent on context-aware services, and the future of smart cities is coupled with how efficiently and reliably we can deliver these services to end users. In this article we present the premise of personalized IoT systems, by leveraging novel advancements in user-centric technologies under the fog computing architecture. This means leveraging the connectivity and processing potential of the fog to bring IoT control and analytics closer to the user, and improve the coupling of services with local IoT components in user-centric contexts. The potential gain in access latency and context-sensitive service matching will enable a multitude of smart city services. On one hand, data management (collection, pruning, denaturing [1], and encryption) can take place closer to the edge, thereby leveraging network load and service times. On the other hand, service matching in smart city applications will witness higher responsiveness and resource visibility in areas with intermittent connectivity or high mobility. We first present the challenges in migrating cloud-IoT architectures to the network edge, and detail the hindrances in transitioning the control and management of IoT systems to the user end. As a remedy, we survey recent advancements in the IoT, ubiquitous computing, and user-centric services, which enable us to advance personalized IoT architectures. We finally present a framework for IoT in the fog to synergize these advancements, and present a proof-of-concept use case to highlight its utility and impact. We conclude this article with prime directions for future work to realize a personalized IoT architecture, and highlight the potential gain in prioritizing five high-yield potential research issues.

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Cited By

Kumar STiwari R(2023)Dynamic Partitioning and Popularity based Caching for Optimized Performance in content-centric fog networksPervasive and Mobile Computing10.1016/j.pmcj.2022.10174088:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2022.101740
Zhu YLu D(2023)Satellite-assisted edge computing management based on deep reinforcement learning in industrial internet of thingsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.110050237:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.110050
Satheesh KSree T(2023)Enhancing Security Using Secure Authentication Model in Fog Computing ModelWireless Personal Communications: An International Journal10.1007/s11277-023-10313-7130:2(909-933)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1007/s11277-023-10313-7
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cover image IEEE Communications Magazine

IEEE Communications Magazine Volume 56, Issue 3

March 2018

210 pages

ISSN:0163-6804

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Copyright © 2018.

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IEEE Press

Publication History

Published: 01 March 2018

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Cited By

Kumar STiwari R(2023)Dynamic Partitioning and Popularity based Caching for Optimized Performance in content-centric fog networksPervasive and Mobile Computing10.1016/j.pmcj.2022.10174088:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2022.101740
Zhu YLu D(2023)Satellite-assisted edge computing management based on deep reinforcement learning in industrial internet of thingsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.110050237:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.110050
Satheesh KSree T(2023)Enhancing Security Using Secure Authentication Model in Fog Computing ModelWireless Personal Communications: An International Journal10.1007/s11277-023-10313-7130:2(909-933)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1007/s11277-023-10313-7
López Escobar JDíaz Redondo RGil-Castiñeira F(2022)In-depth analysis and open challenges of Mist ComputingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00354-x11:1Online publication date: 19-Nov-2022
https://dl.acm.org/doi/10.1186/s13677-022-00354-x
Liu XQi DLi WZhang H(2022)Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural networkThe Journal of Supercomputing10.1007/s11227-021-04041-778:4(5029-5049)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11227-021-04041-7
Chandrika PMekala MSrivastava G(2022)Edge resource slicing approaches for latency optimization in AI-edge orchestrationCluster Computing10.1007/s10586-022-03817-726:2(1659-1683)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/s10586-022-03817-7
Raza SLiu WAhmed MAnwar MMirza MSun QWang S(2020)An efficient task offloading scheme in vehicular edge computingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-020-00175-w9:1Online publication date: 2-Jun-2020
https://dl.acm.org/doi/10.1186/s13677-020-00175-w
Kim S(2020)New Application Task Offloading Algorithms for Edge, Fog, and Cloud Computing ParadigmsWireless Communications & Mobile Computing10.1155/2020/88880742020Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1155/2020/8888074
Yousefpour AFung CNguyen TKadiyala KJalali FNiakanlahiji AKong JJue J(2019)All one needs to know about fog computing and related edge computing paradigmsJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2019.02.00998:C(289-330)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.sysarc.2019.02.009
Karatas FKorpeoglu I(2019)Fog-Based Data Distribution Service (F-DAD) for Internet of Things (IoT) applicationsFuture Generation Computer Systems10.1016/j.future.2018.10.03993:C(156-169)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1016/j.future.2018.10.039
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