research-article

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Authors:

Mark ScmalzAuthors Info & Claims

MSWiM '11: Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

Pages 17 - 26

https://doi.org/10.1145/2068897.2068904

Published: 31 October 2011 Publication History

Abstract

Unlike traditional distributed systems, where the resources/needs of computation and communication dominate the performance equation, sensor-based systems (SBS) raise new metrics and requirements for sensors as well as for computing and communication. This includes sensing latency and energy consumption. In this paper, we present a performance model for SBS based on a three-tier architecture that uses edge devices to connect massive-scale networks of sensors to the cloud. In this architecture, which we call Cloud, Edge, and Beneath (CEB), initial processing of sensor data occurs in- and near-network, in order to achieve system sentience and energy efficiency. To optimize CEB performance, we propose the concept of optimal push/pull envelope (PPE). PPE dynamically and minimally adjusts the base push and pull rates for each sensor, according to the relative characteristics of sensor requests (demand side from the Cloud) and sensor data change (supply side from Beneath). We demonstrate the CEB architecture and its push/pull envelope optimization algorithm in an experimental evaluation that measures energy savings and sentience efficiency over a wide range of practical constraints. In addition, from the experiments we demonstrate that by combining PPE optimization algorithm with lazy sampling algorithm, we can achieve further energy saving.

References

[1]

C. Rolim, F. Koch, C. Westphall, J. Werner, A. Fracalossi, G. Salvador, "A Cloud Computing Solution for Patient Data Collection in Health Care Institutions," in Proc. of ETELEMED. IEEE, 2010, pp. 95--99

Digital Library

[2]

M. Hassan, E. Huh. "A Framework of Sensor-Cloud Integration: Opportunities and Challenges". International Conference on Ubiquitous Information Management and Communication.

Digital Library

[3]

X.H. Le, S. Lee, T. Phan, V. La, A. Khattak, M. Han, H. Dang, M. Hassan, M. Kim, K. Koo, Y.K. Lee, E.N. Huh, "Secured WSN-integrated cloud computing for u-Life care", in: Seventh Annual IEEE Consumer Communication and Networking Conference (CCNC), January 9--12, Las Vegas, 2010, pp. 1--2.

Digital Library

[4]

K Lee and D. Hughes. "System architecture directions for tangible cloud computing." In International Workshop on Information Security and Applications (IWISA 2010), in Qinhuangdao, China, October 22- 25, 2010.

Digital Library

[5]

M. Yuriyama and T. Kushida, "Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing", The 13th International Conference on Network-Based Information Systems (NBiS-2010), 2010

Digital Library

[6]

R. Bose and A. Helal, "Sensor-aware Adaptive Push-Pull Query Processing in Wireless Sensor Networks," Submitted to the 6th International Conference on Intelligent Environments - IE'10, Kuala Lumpur, Malaysia, July 19--22, 2010.

Digital Library

[7]

Y. Kawahara, M. Minami, H. Morikawa, T. Aoyama: "Design and Implementation of a Sensor Network Node for Ubiquitous Computing Environment", In Proc. Of VTC2003-Fall 2003.

[8]

A. Helal, D. Cook, M. Schmalz, "Smart Home-based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients," Journal of Diabetes Science and Technology, Volume 3, Number 1, January 2009. Pp 141--148.

[9]

N. Rosa, C.Ferraz, J. Kelner, E. Souto, G. Guimarães, G. Vasconcelos, M. Vieira. "Mires: a publish/subscribe middleware for sensor networks". Personal and Ubiquitous Computing, Volume 10 Issue 1, December 2005. Springer-Verlag.

Digital Library

[10]

X. Liu, Q. Huang, Y. Zhang. "Balancing Push and Pull for Efficient Information Discovery in Large-Scale Sensor Networks". IEEE Transactions on Mobile Computing, Volume 6, Issue 3, March 2007 Page(s):241 -- 251.

Digital Library

[11]

Z. Zhang, M. Ma, Y. Yang. "Energy Efficient Multi-Hop Polling in Clusters of Two-Layered Heterogeneous Sensor Networks". IPDPS '05: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05), April 2005.

Digital Library

[12]

S. Reilly and M. Haahr. "Extending the Event-based programming model to support Sensor-Driven Ubiquitous Computing Applications". Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications (Percom'09).

Digital Library

[13]

Z. Tao, Z. Gong, Z. OuYang, J. Xu. "Two New Push-Pull Balanced Data Dissemination Algorithms for Any-Type Queries in Large-Scale Wireless Sensor Networks". International Symposium on Parallel Architectures, Algorithms, and Networks, 2008. 7--9 May 2008 pp: 111 -- 117.

Digital Library

[14]

S. A. Hashish, A. Karmouch. "Topology-based on-board data dissemination approach for sensor network." Proceedings of the 5th ACM international workshop on Mobility management and wireless access. Chania, Crete Island, Greece, 2007. pp. 33 -- 41.

Digital Library

[15]

C. Chen, Y. Xu, K. Li, and A. Helal, "Reactive Programming Optimizations in Pervasive Computing," in proceedings of 10th Annual International Symposium on Applications and the Internet, Seoul, Korea.

Digital Library

[16]

M. Khanafer, M. Cuennoun, H. T. Mouftah, "WSN Architectures for Intelligent Transportation Systems" Proceedings of 3rd New Technologies, Mobility and Security (NTMS), Cairo, Egypt

Digital Library

[17]

J. King, R. Bose, H. Yang, S. Pickles and A. Helal, "Atlas -- A Service-Oriented Sensor Platform, " Proceedings of the first IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2006). Tampa, Florida, November 2006.

[18]

S. Lai, J. Cao, Y. Zheng, "PSWare: a Publish/Subscribe Middleware Supporting Composite Event in Wireless Sensor Network". IEEE International Conference on Pervasive Computing and Communications, 2009. pp.1--6.

Digital Library

[19]

Y. Cardenas, J. M. Pierson, L. Brunie: "Uniform Distributed Cache Service for Grid Computing." In: Proceedings of the International Workshop on Database and Expert Systems Applications, pp. 351--355 (2005)

[20]

L.K. Au, W.H. Wu, M.A. Batalin, D.H. McIntire and W.J. Kaiser, "MicroLEAP: Energy-aware Wireless Sensor Platform for Biomedical Sensing Applications". IEEE BIOCAS2007. November 2007. pp.158--162.

[21]

V. Sivaraman, A. Vishwanath, Z. Zhao, C. Russell, "Profiling Per-Packet and Per-Byte Energy Consumption in the NetFPGA Gigabit Router," IEEE INFOCOM 2011 Workshop on Green Communications and Networking

Cited By

Haseeb-Ur-Rehman RLiaqat MAman AHamid SAli RShuja JKhan M(2021)Sensor Cloud Frameworks: State-of-the-Art, Taxonomy, and Research IssuesIEEE Sensors Journal10.1109/JSEN.2021.309096721:20(22347-22370)Online publication date: 15-Oct-2021
https://doi.org/10.1109/JSEN.2021.3090967
Zhao QWang GPeng YLu Y(2020)OEDDBOSWireless Communications & Mobile Computing10.1155/2020/43804622020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/4380462
Mukherjee PSwain TDatta A(2019)Issues of Some Task Scheduling Strategies on Sensor Cloud EnvironmentSmart Intelligent Computing and Applications10.1007/978-981-32-9690-9_70(651-663)Online publication date: 4-Oct-2019
https://doi.org/10.1007/978-981-32-9690-9_70
Show More Cited By

Index Terms

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems
1. Networks
  1. Network architectures

Recommendations

Performance and energy efficiency of big data systems: characterization, implication and improvement
ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

Large volume of data is produced by various applications in the world, processing such scale of data has great challenges in not only performance but also energy efficiency. Researchers propose various techniques to either improve the performance or the ...
Experimental analysis of task-based energy consumption in cloud computing systems
ICPE '13: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

Cloud computing delivers IT solutions as a utility to users. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A common objective of cloud providers is to develop ...
Performance and energy efficiency of big data applications in cloud environments

The exponential growth of scientific and business data has resulted in the evolution of the cloud computing environments and the MapReduce parallel programming model. The focus of cloud computing is increased utilization and power savings through ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MSWiM '11: Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

October 2011

462 pages

ISBN:9781450308984

DOI:10.1145/2068897

General Chair:
Ahmed Helmy
University of Florida, USA
,
Program Chairs:
Bjorn Landfeldt
University of Sydney, Australia
,
Luciano Bononi
University of Bologna, Italy

Copyright © 2011 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]

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MSWiM '11

Sponsor:

SIGSIM

MSWiM '11: The 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 31 - November 4, 2011

Florida, Miami, USA

Acceptance Rates

Overall Acceptance Rate 398 of 1,577 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
424
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)4

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Haseeb-Ur-Rehman RLiaqat MAman AHamid SAli RShuja JKhan M(2021)Sensor Cloud Frameworks: State-of-the-Art, Taxonomy, and Research IssuesIEEE Sensors Journal10.1109/JSEN.2021.309096721:20(22347-22370)Online publication date: 15-Oct-2021
https://doi.org/10.1109/JSEN.2021.3090967
Zhao QWang GPeng YLu Y(2020)OEDDBOSWireless Communications & Mobile Computing10.1155/2020/43804622020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/4380462
Mukherjee PSwain TDatta A(2019)Issues of Some Task Scheduling Strategies on Sensor Cloud EnvironmentSmart Intelligent Computing and Applications10.1007/978-981-32-9690-9_70(651-663)Online publication date: 4-Oct-2019
https://doi.org/10.1007/978-981-32-9690-9_70
Li CXue YWang JZhang WLi T(2018)Edge-Oriented Computing ParadigmsACM Computing Surveys10.1145/315481551:2(1-34)Online publication date: 17-Apr-2018
https://dl.acm.org/doi/10.1145/3154815
Yongxuan LLin HGao XYang FYin S(2018)Request Answering in Vehicular Ad-Hoc Networks Based on Adaptive Filters2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld.2018.00180(1031-1038)Online publication date: Oct-2018
https://doi.org/10.1109/SmartWorld.2018.00180
Iskandar K(2018)A KMS Architecture: Push New Content from Cloud Storage2018 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech.2018.8528153(356-360)Online publication date: Sep-2018
https://doi.org/10.1109/ICIMTech.2018.8528153
Priya KRathan KPavani K(2018)A Novel 3G/LTE Data Assortment System in Conveyance Networks-A review2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)10.1109/ICICCT.2018.8473180(578-584)Online publication date: Apr-2018
https://doi.org/10.1109/ICICCT.2018.8473180
Atif MShah M(2017)OptiSEC: In search of an optimal sensor cloud architecture2017 23rd International Conference on Automation and Computing (ICAC)10.23919/IConAC.2017.8082087(1-6)Online publication date: Sep-2017
https://doi.org/10.23919/IConAC.2017.8082087
Farias CLi WDelicato FPirmez LZomaya APires PSouza J(2016)A Systematic Review of Shared Sensor NetworksACM Computing Surveys10.1145/285151048:4(1-50)Online publication date: 9-Feb-2016
https://dl.acm.org/doi/10.1145/2851510
Drira WAhn KRakha HFilali F(2016)Development and Testing of a 3G/LTE Adaptive Data Collection System in Vehicular NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2015.246479217:1(240-249)Online publication date: Jan-2016
https://doi.org/10.1109/TITS.2015.2464792
Show More Cited By

View Options

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten