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Sensorclone: a framework for harnessing smart devices with virtual sensors

Published: 12 June 2018 Publication History

Abstract

IoT services hosted by low-power devices rely on the cloud infrastructure to propagate their ubiquitous presence over the Internet. A critical challenge for IoT systems is to ensure continuous provisioning of IoT services by overcoming network breakdowns, hardware failures, and energy constraints. To overcome these issues, we propose a cloud-based framework namely SensorClone, which relies on virtual devices to improve IoT resilience. A virtual device is the digital counterpart of a physical device that has learned to emulate its operations from sample data collected from the physical one. SensorClone exploits the collected data of low-power devices to create virtual devices in the cloud. SensorClone then can opportunistically migrate virtual devices from the cloud into other devices, potentially underutilized, with higher capabilities and closer to the edge of the network, e.g., smart devices. Through a real deployment of our SensorClone in the wild, we identify that virtual devices can be used for two purposes, 1) to reduce the energy consumption of physical devices by duty cycling their service provisioning between the physical device and the virtual representation hosted in the cloud, and 2) to scale IoT services at the edge of the network by harnessing temporal periods of underutilization of smart devices. To evaluate our framework, we present a use case of a virtual sensor created from an IoT service of temperature. From our results, we verify that it is possible to achieve unlimited availability up to 90% and substantial power efficiency under acceptable levels of quality of service. Our work makes contributions towards improving IoT scalability and resilience by using virtual devices.

References

[1]
Vaneet Aggarwal, Emir Halepovic, Jeffrey Pang, Shobha Venkataraman, and He Yan. 2014. Prometheus: toward quality-of-experience estimation for mobile apps from passive network measurements. In Proceedings of the 15th ACM Workshop on Mobile Computing Systems and Applications (HotMobile 2014). Santa Barbara, California, US.
[2]
Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010. The internet of things: A survey. Computer networks 54, 15 (2010), 2787--2805.
[3]
Richard G Baraniuk. 2007. Compressive sensing {lecture notes}. IEEE signal processing magazine 24, 4 (2007), 118--121.
[4]
Yin Chen, Takuro Yonezawa, Kazunori Takashio, Yutaro Kyono, Jin Nakazawa, and Hideyuki Tokuda. 2015. A public vehicle-based urban sensing system. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015): Adjunct. Osaka, Japan.
[5]
Zipei Fan, Xuan Song, Ryosuke Shibasaki, Tao Li, and Hodaka Kaneda. 2016. CityCoupling: bridging intercity human mobility. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016). Heidelberg, Germany.
[6]
Huber Flores, Denzil Ferreira, Chu Luo, Vassilis Kostakos, PanHui, Rajesh Sharma, Sasu Tarkoma, and Yong Li. 2016. Social-aware device-to-device communication: a contribution for edge and fog computing?. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016): Adjunct. Heidelberg, Germany.
[7]
Huber Flores, Pan Hui, Sasu Tarkoma, Yong Li, Satish Srirama, and Rajkumar Buyya. 2015. Mobile Code Offloading: From Concept to Practice and Beyond. IEEE Communications Magazine 4 (2015).
[8]
Huber Flores, Rajesh Sharma, Denzil Ferreira, Vassilis Kostakos, Jukka Manner, Sasu Tarkoma, Pan Hui, and Yong Li. 2017. Social-aware hybrid mobile offloading. Pervasive and Mobile Computing 36 (2017), 25--43.
[9]
Ray J Frank, Neil Davey, and Stephen P Hunt. 2001. Time series prediction and neural networks. Journal of intelligent and robotic systems 31, 1-3 (2001), 91--103.
[10]
Raghu Ganti, Mudhakar Srivatsa, Anand Ranganathan, and Jiawei Han. 2013. Inferring human mobility patterns from taxicab location traces. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013). Zurich, Switzerland.
[11]
Marisol García-Valls, Javier Ampuero-Calleja, and Luis Lino Ferreira. 2017. Integration of Data Distribution Service and Raspberry Pi. In Proceedings of the International Conference on Green, Pervasive, and Cloud Computing (GPC 2017). Cetara, Amalfi Coast, Italy.
[12]
Bo Han, Pan Hui, VS Anil Kumar, Madhav V Marathe, Jianhua Shao, and Aravind Srinivasan. 2012. Mobile data offloading through opportunistic communications and social participation. IEEE Transactions on Mobile Computing 11, 5 (2012), 821--834.
[13]
Samuli Hemminki, Kai Zhao, Aaron Yi Ding, Martti Rannanjärvi, Sasu Tarkoma, and Petteri Nurmi. 2013. Cosense: A collaborative sensing platform for mobile devices. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013). Rome, Italy.
[14]
Christopher K Hess, Manuel Román, and Roy H Campbell. 2002. Building applications for ubiquitous computing environments. In International Conference on Pervasive Computing (Pervasive 2002). Zurich, Switzerland.
[15]
Geoff Hulten, Laurie Spencer, and Pedro Domingos, {n. d.}. Mining time-changing data streams. In Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2000). San Francisco, Ca, USA.
[16]
Michael O Jewell, Enrico Costanza, and Jacob Kittley-Davies. 2015. Connecting the things to the internet: an evaluation of four configuration strategies for wi-fi devices with minimal user interfaces. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015). Osaka, Japan.
[17]
Hongbo Jiang, Shudong Jin, and Chonggang Wang. 2011. Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems 22, 6 (2011), 1064--1071.
[18]
Karthik Kumar and Yung-Hsiang Lu. 2010. Cloud computing for mobile users: Can offloading computation save energy? Computer 43, 4 (2010), 51--56.
[19]
Nicholas D Lane and Petko Georgiev. 2015. Can deep learning revolutionize mobile sensing?. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications (HotMobile 2015). Santa Fe, New Mexico.
[20]
Youngki Lee, Younghyun Ju, Chulhong Min, Seungwoo Kang, Inseok Hwang, and Junehwa Song. 2012. Comon: Cooperative ambience monitoring platform with continuity and benefit awareness. In Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2012). Low Wood Bay, Lake District, United Kingdom.
[21]
Sanjay Madria, Vimal Kumar, and Rashmi Dalvi. 2014. Sensor cloud: A cloud of virtual sensors. IEEE software 31, 2 (2014), 70--77.
[22]
Adam J Oliner, Anand P Iyer, Ion Stoica, Eemil Lagerspetz, and Sasu Tarkoma. 2013. Carat: Collaborative energy diagnosis for mobile devices. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys 2013). Rome, Italy.
[23]
Shumao Ou, Kun Yang, Antonio Liotta, and Liang Hu. 2007. Performance analysis of offloading systems in mobile wireless environments. In Proceedings of the IEEE International Conference on Communications (ICC 2007). Glasgow, Scotland, UK.
[24]
Luciana Pelusi, Andrea Passarella, and Marco Conti. 2006. Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Communications Magazine 44, 11 (2006).
[25]
Kiran K Rachuri, Christos Efstratiou, Ilias Leontiadis, Cecilia Mascolo, and Peter J Rentfrow. 2014. Smartphone sensing offloading for efficiently supporting social sensing applications. Pervasive and Mobile Computing 10 (2014), 3--21.
[26]
Suman Sankar Bhunia. 2015. Adopting internet of things for provisioning healthcare. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015): Adjunct. Osaka, Japan.
[27]
Mahadev Satyanarayanan. 2001. Pervasive computing: Vision and challenges. IEEE Personal Communications 8, 4 (2001), 10--17.
[28]
Mojca Volk, Janez Sterle, Urban Sedlar, and Andrej Kos. 2010. An approach to modeling and control of QoE in next generation networks. IEEE Communications Magazine 48, 8 (2010), 126--135.
[29]
Royu Want, Trevor Pering, Gunner Danneels, Muthu Kumar, Murali Sundar, and John Light. 2002. The personal server: Changing the way we think about ubiquitous computing. In International Conference on Ubiquitous Computing (Pervasive 2002). Zurich, Switzerland.
[30]
Fengli Xu, Pengyu Zhang, and Yong Li. 2016. Context-aware real-time population estimation for metropolis. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016). Heidelberg, Germany.

Cited By

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  • (2019)LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoTIEEE Journal on Selected Areas in Communications10.1109/JSAC.2019.292709937:8(1783-1793)Online publication date: Aug-2019
  • (2019)Verifying nondeterministic processes driven by broadcasts on Android2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)10.1109/ITNEC.2019.8729296(341-348)Online publication date: Mar-2019

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Published In

cover image ACM Conferences
MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference
June 2018
604 pages
ISBN:9781450351928
DOI:10.1145/3204949
  • General Chair:
  • Pablo Cesar,
  • Program Chairs:
  • Michael Zink,
  • Niall Murray
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]

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Publication History

Published: 12 June 2018

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Author Tags

  1. cloud computing
  2. edge computing
  3. internet of things
  4. opportunistic migration
  5. resilience
  6. virtual sensor

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MMSys '18
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MMSys '18: 9th ACM Multimedia Systems Conference
June 12 - 15, 2018
Amsterdam, Netherlands

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Overall Acceptance Rate 176 of 530 submissions, 33%

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View all
  • (2019)LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoTIEEE Journal on Selected Areas in Communications10.1109/JSAC.2019.292709937:8(1783-1793)Online publication date: Aug-2019
  • (2019)Verifying nondeterministic processes driven by broadcasts on Android2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)10.1109/ITNEC.2019.8729296(341-348)Online publication date: Mar-2019

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