More Web Proxy on the site http://driver.im/

research-article

Public Access

Five Challenges in Cloud-enabled Intelligence and Control

Authors:

Tarek Abdelzaher,

Kasthuri Jayarajah,

Dulanga Weerakoon,

Karl-Erik ÅrzénAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 20, Issue 1

Article No.: 3, Pages 1 - 19

https://doi.org/10.1145/3366021

Published: 10 February 2020 Publication History

All formats PDF

Abstract

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems, both in cloud applications and infrastructure layers. These problems are centered around empowering individually resource-limited devices to exhibit intelligent behavior, both in sensing and control, thanks to a judicious utilization of cloud resources. Cloud services will enable learning from data, perform inference, and execute control, all with assurances on outcomes. This article discusses such emerging services and outlines five resulting new research directions towards enabling and optimizing intelligent, cloud-assisted sensing and control in the age of the Internet of Things.

References

[1]

Tarek Abdelzaher, Shuochao Yao, Yifan Hao, Yiran Zhao, Ailing Piao, Huajie Shao, Dongxin Liu, Shengzhong Liu, Shaohan Hu, Dulanga Weerakoon, Kasthuri Jayarajah, and Archan Misra. 2019. Eugene: Towards deep intelligence as a service. In Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS’19).

[2]

Karl-Erik Årzén, Per Skarin, William Tärneberg, and Maria Kihl. 2018. Control over the edge cloud—An MPC example. In Proceedings of the 1st International Workshop on Trustworthy and Real-Time Edge Computing for Cyber-Physical Systems.

[3]

Richard Bellman. 1957. Dynamic Programming (1st ed.). Princeton University Press, Princeton, NJ.

Digital Library

[4]

Richard C. Dorf and Robert H. Bishop. 2011. Modern Control Systems. Pearson.

[5]

A. A. Feldbaum. 1965. Optimal Control Systems. Academic Press. Retrieved from: https://books.google.se/books?id=_dEXtQEACAAJ.

[6]

James Ferryman and Ali Shahrokni. 2009. Pets2009: Dataset and challenge. In Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. IEEE.

[7]

B. Han, V. Gopalakrishnan, L. Ji, and S. Lee. 2015. Network function virtualization: Challenges and opportunities for innovations. IEEE Commun. Mag. 53, 2 (Feb. 2015), 90--97.

Digital Library

[8]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.

[9]

Joseph L. Hellerstein, Yixin Diao, Sujay Parekh, and Dawn M. Tilbury. 2004. Feedback Control of Computing Systems. Wiley Online Library.

Digital Library

[10]

Geoffrey Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Sig. Proc. Mag. 29, 6 (2012), 82--97.

[11]

R. E. Kalman. 1960. Contributions to the theory of optimal control. Bolet. Socied. Matem. Mexi. 5, 2 (1960), 102--119.

[12]

Nicholas D. Lane, Petko Georgiev, and Lorena Qendro. 2015. DeepEar: Robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 283--294.

Digital Library

[13]

Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, M. K. Ryu, and Greg Imwalle. 2018. Data center cooling using model-predictive control. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3814--3823.

[14]

Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. 2016. SSD: Single shot MultiBox detector. In Proceedings of the European Conference on Computer Vision (ECCV’16).

[15]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller. 2013. Playing Atari with deep reinforcement learning. CoRR abs/1312.5602 (2013).

[16]

J. B. Rawlings and D. Q. Mayne. 2009. Model Predictive Control: Theory and Design. Nob Hill Publishing.

[17]

Benjamin Recht. 2018. A tour of reinforcement learning: The view from continuous control. CoRR abs/1806.09460 (2018).

[18]

David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. 2018. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 6419 (2018), 1140--1144.

[19]

David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis. 2017. Mastering the game of Go without human knowledge. Nature 550 (Oct. 2017), 354--359.

[20]

Per Skarin, Johan Eker, Maria Kihl, and Karl-Erik Årzén. 2019. An assisting Model Predictive Controller approach to control over the cloud. arXiv e-prints, Article arXiv:1905.06305 (May 2019).

[21]

Richard S. Sutton and Andrew G. Barto. 1998. Introduction to Reinforcement Learning (1st ed.). The MIT Press, Cambridge, MA.

Digital Library

[22]

Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. 2017. DeepSense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee.

Digital Library

[23]

Shuochao Yao, Yiran Zhao, Huajie Shao, ShengZhong Liu, Dongxin Liu, Lu Su, and Tarek Abdelzaher. 2018. FastDeepIoT: Towards understanding and optimizing neural network execution time on mobile and embedded devices. In Proceedings of the ACM International Conference on Embedded Networked Sensor Systems (SenSys’18). ACM, New York, NY, 278--291.

Digital Library

[24]

Shuochao Yao, Yiran Zhao, Huajie Shao, Aston Zhang, Chao Zhang, Shen Li, and Tarek Abdelzaher. 2018. RDeepSense: Reliable deep mobile computing models with uncertainty estimations. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 1, 4 (2018), 173.

Digital Library

[25]

Shuochao Yao, Yiran Zhao, Huajie Shao, Chao Zhang, Aston Zhang, Shaohan Hu, Dongxin Liu, Shengzhong Liu, Lu Su, and Tarek Abdelzaher. 2018. SenseGAN: Enabling deep learning for internet of things with a semi-supervised framework. Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 2, 3, Article 144 (Sept. 2018), 21 pages.

Digital Library

[26]

Shuochao Yao, Yiran Zhao, Aston Zhang, Shaohan Hu, Huajie Shao, Chao Zhang, Su Lu, and Tarek Abdelzaher. 2018. Deep learning for the internet of things. Computer 51, 5 (May 2018), 32--41.

[27]

Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, and Tarek Abdelzaher. 2017. DeepIoT: Compressing deep neural network structures for sensing systems with a compressor-critic framework. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. ACM.

Digital Library

[28]

Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P. Jue. 2019. All one needs to know about fog computing and related edge computing paradigms: A complete survey. J. Syst. Arch. 98 (Feb. 2019), 289--330.

Digital Library

Cited By

Gokarn ISabbella HHu YAbdelzaher TMisra AZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)MOSAIC: Spatially-Multiplexed Edge AI Optimization over Multiple Concurrent Video Sensing StreamsProceedings of the 14th Conference on ACM Multimedia Systems10.1145/3587819.3590986(278-288)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3587819.3590986
Jayarajah KGart SGangopadhyay A(2023)AirDropProceedings of the 24th International Workshop on Mobile Computing Systems and Applications10.1145/3572864.3580335(55-60)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3572864.3580335
Tian HPeng FQuan HChang C(2023)Identity-Based Public Auditing for Cloud Storage of Internet-of-Vehicles DataACM Transactions on Internet Technology10.1145/343354322:4(1-24)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3433543
Show More Cited By

Index Terms

Five Challenges in Cloud-enabled Intelligence and Control

Recommendations

Review of artificial intelligence for enhancing intrusion detection in the internet of things
Abstract
Internet of Things is shaping the quality of living standard. With the rapid growth and expansion of adopting IoT-based approaches, their security represents a growing challenge for both manufacturers and consumers. There is a recent rising trend ...
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions
Abstract
The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and ...
Industrial Internet of Things enabled technologies, challenges, and future directions
Highlights
- IIoT-enabled technologies, challenges, and future directions are explored.
- A blockchain-based cement industry security framework can overcome 51% of security issues.
- The performance of major companies depends on well-designed IIoT ...
Abstract
The Industrial Internet of Things (IIoT) is recognized as the fourth industrial revolution as it enhances productivity, dependability, and competitive performance by concentrating on profitability. IIoT-enabled technologies have been reviewed and ...
Graphical abstract

Display Omitted

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 20, Issue 1

Visions and Regular Papers

February 2020

135 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3381410

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 February 2020

Accepted: 01 October 2019

Revised: 01 September 2019

Received: 01 July 2019

Published in TOIT Volume 20, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Army Research Laboratory

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

22
Total Citations
View Citations
1,258
Total Downloads

Downloads (Last 12 months)253
Downloads (Last 6 weeks)34

Reflects downloads up to 10 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gokarn ISabbella HHu YAbdelzaher TMisra AZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)MOSAIC: Spatially-Multiplexed Edge AI Optimization over Multiple Concurrent Video Sensing StreamsProceedings of the 14th Conference on ACM Multimedia Systems10.1145/3587819.3590986(278-288)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3587819.3590986
Jayarajah KGart SGangopadhyay A(2023)AirDropProceedings of the 24th International Workshop on Mobile Computing Systems and Applications10.1145/3572864.3580335(55-60)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3572864.3580335
Tian HPeng FQuan HChang C(2023)Identity-Based Public Auditing for Cloud Storage of Internet-of-Vehicles DataACM Transactions on Internet Technology10.1145/343354322:4(1-24)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3433543
Solnør PPetrovic SFossen T(2023)Towards Oblivious Guidance Systems for Autonomous VehiclesIEEE Transactions on Vehicular Technology10.1109/TVT.2023.323789272:6(7067-7081)Online publication date: Jun-2023
https://doi.org/10.1109/TVT.2023.3237892
Abdelzaher TCaesar MMendis CNahrstedt KSrivastava MYu M(2023)Challenges in Metaverse Research: An Internet of Things Perspective2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom)10.1109/MetaCom57706.2023.00042(161-170)Online publication date: Jun-2023
https://doi.org/10.1109/MetaCom57706.2023.00042
Spatharakis DAvgeris MAthanasopoulos NDechouniotis DPapavassiliou S(2023)Resource-Aware Estimation and Control for Edge Robotics: A Set-Based ApproachIEEE Internet of Things Journal10.1109/JIOT.2022.314126610:3(2003-2020)Online publication date: 1-Feb-2023
https://doi.org/10.1109/JIOT.2022.3141266
Carlsson MVreman NCervin A(2023)Timing-Robust Control over the Cloud Using On-Line Parametric OptimizationIFAC-PapersOnLine10.1016/j.ifacol.2023.10.45756:2(5560-5565)Online publication date: 2023
https://doi.org/10.1016/j.ifacol.2023.10.457
Liu DAbdelzaher T(2023)Self-Supervised Learning from Unlabeled IoT DataArtificial Intelligence for Edge Computing10.1007/978-3-031-40787-1_2(27-110)Online publication date: 4-Aug-2023
https://doi.org/10.1007/978-3-031-40787-1_2
Manjunatha KLakshmiranganatha S(2023)Embedded Edge and Cloud IntelligenceSecurity and Risk Analysis for Intelligent Edge Computing10.1007/978-3-031-28150-1_4(91-110)Online publication date: 27-Feb-2023
https://doi.org/10.1007/978-3-031-28150-1_4
Durga SDaniel EOnesimu JSei Y(2022)Resource Provisioning Techniques in Multi-Access Edge Computing EnvironmentsMobile Information Systems10.1155/2022/72835162022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7283516
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents