skip to main content
10.1145/2811411.2811513acmconferencesArticle/Chapter ViewAbstractPublication PagesracsConference Proceedingsconference-collections
research-article

Energy-aware selective compression scheme for solar energy based wireless sensor networks

Published: 09 October 2015 Publication History

Abstract

Data compression involves a trade-off between delay time and data size. More the delay time, lesser the data size and vice versa. There have been many studies performed in the field of wireless sensor networks about increasing network life cycle durations that involve minimizing energy consumption by reducing data size; however, reducing data size results in increased delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy based wireless sensor networks, the redundant energy is often generated that is sufficient to run a node. In this study, the excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy less than the threshold, transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.

References

[1]
Sudevalayam, S., and Kulkarni, P. 2011. Energy harvesting sensor nodes: Survey and implications. Communications Surveys & Tutorials. IEEE, 13, 3 (Jul. 2010), 443--461. DOI=http://dx.doi.org/10.1109/SURV.2011.060710.00094.
[2]
Yong, Y., Wang, L., Noh, D., Le H. K., and Abdelzaher, T. F. 2009. SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks. In Proceedings of the 7th Annual International Conference on Mobile Systems, Applications, and Services. ACM, New York, NY, USA, 333--346. DOI=http://dx.doi.org/10.1145/1555816.1555850.
[3]
Noh, D., and Hur, J. 2012. Using a dynamic backbone for efficient data delivery in solar-powered WSNs. Journal of Network and Computer Applications. 35, 4 (Jul. 2012), 1277--1284. DOI=http://dx.doi.org/10.1016/j.jnca.2012.01.012.
[4]
Lee, H., Kim, H., and Chang, I. J. 2014. CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks. Sensors, 14, 4 (Apr. 2014), 6419--6442. DOI=http://dx.doi.org/10.3390/s140406419.
[5]
Ting Liu, Christopher M. Sadler, Pei Zhang and Margaret Martonosi. 2004. Implementing Software on Resource-Constrained Mobile Sensors: Experiences with Impala and ZebraNet. 2nd Annual Mobile Systems, Applications, and Services. ACM, (Jul. 2004), 256--269. DOI=http://doi.acm.org/10.1145/990064.990095.
[6]
Gilman Tolle, Joseph Polastre, Robert Szewczyk, David Culler, Neil Turner, Kevin Tu, Stephen Burgess, Todd Dawson, Phil Buonadonna, David Gay, and Wi Hong. 2005. A Macroscope in the Redwoods. 3rd Annual ACM International Conference on Embedded Networked Sensor Systems. ACM, (Nov. 2005), 51--63. DOI=http://doi.acm.org/10.1145/1098918.1098925.
[7]
Aman Kansal, Jason Hsu, Mani Srivastava and Vijay Raghunathan. 2006. Harvesting aware power management for sensor networks. 43rd Annual Design Automation Conference. ACM, (Jul. 2006), 651--656. DOI=http://doi.acm.org/10.1145/1146909.1147075.
[8]
Christopher M., Vigorito, Deepak Ganesan, and Andrew G. Barto. 2007. Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks. 4th Annual IEEE Communications Society. USA, (Jun. 2007), 21--30. DOI=http://dx.doi.org/10.1109/SAHCN.2007.4292814.
[9]
Cesare Alippi and Cristian Galperti. 2008. An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes. IEEE Trans. on Circuits and Systems. 55-I, 6 (Jul. 2008), 1742--1750. DOI=http://dx.doi.org/10.1109/TCSI.2008.922023.
[10]
Jay Taneja, Jaein Jeong and David Culler. 2008. Design, Modeling, and Capacity Planning for Micro-solar Power Sensor Networks. 7th Annual IEEE International Conference on Information Processing in Sensor Networks. USA, (Apr. 2008), 407--418. DOI=http://dx.doi.org/10.1109/IPSN.2008.67.
[11]
Xiaofan Jiang, Joseph Polastre and David Culler. 2005. Perpetual environmentally powered sensor networks. 4th Annual IEEE International Conference on Information Processing in Sensor Networks. USA, (Apr. 2005), 463--468. DOI=http://dx.doi.org/10.1109/IPSN.2005.1440974.
[12]
S. Roundy, B. P. Otis, Y. Chee, J. M. Rabaey, and P. Wright. 2003. A 1.9GHz RF Transmit Beacon Using Environmentally Scavenged Energy Luminance Scaling. International Symposium on Low Power Electronics and Design, Korea.
[13]
Thiemo Voigt, Hartmut Ritter and Jochen Schiller. 2003. Utilizing solar power in wireless sensor networks. In Proceeding of the 28th Annual IEEE Conference on Local Computer Networks. Germany, (Oct. 2003), 416--422. DOI=http://doi.ieeecomputersociety.org/10.1109/LCN.2003.1243167.
[14]
Donggeon Noh, Dongeun Lee, Heonshik Shin. 2007. QoS-Aware Geographic Routing for Solar-Powered Wireless Sensor Networks. The Institute of Electronics, Information and Communication Engineers Transactions on Communications. 90-B, 12 (Jan. 2007), 3373--3382. DOI=http://dx.doi.org/10.1093/ietcom/e90-b.12.3373.
[15]
Donggeon Noh, Ikjune Yoon and Heonshilk Shin. 2008. Low-latency geographic routing for asynchronous energy-harvesting WSNs. Journal of Networks. 3, 1 (Jan. 2008), 78--85. DOI=http://dx.doi.org/10.4304/jnw.3.1.78-85.
[16]
Tossaporn Srisooksai, Kamol Keamarungsi, Poonlap Lamsrichan and Kiyomichi Araki. 2012. Practical data compression in wireless sensor networks: A survey. Collaborative Computing and Applications. 35, 1 (Jan. 2012), 37--59. DOI=http://doi.acm.org/10.1016/j.jnca.2011.03.001.
[17]
Dragan Petrović, Rahul C. Shah, Kannan Ramchandran and Jan Rabaey. 2003. Data Funneling: Routing with Aggregation and Compression for Wireless Sensor Networks. In Proceedings of First IEEE International Workshop on Sensor Network Protocols and Applications. (May 2003), 156--162. DOI=http://dx.doi.org/10.1109/SNPA.2003.1203366.
[18]
Tarik Arici, Bugra Gedik, Yucel Altunbasak and Ling Liu. 2003. PINCO: a Pipelined In-Network Compression Scheme for Data Collection in Wireless Sensor Networks. In Proceedings of 12th International Conference on Computer Communications and Networks. (Oct. 2003), 539--544. DOI=http://dx.doi.org/10.1109/ICCCN.2003.1284221.
[19]
Francesco Marcelloni and Massimo Vecchio. 2008. A Simple Algorithm for Data Compression in Wireless Sensor Networks. Communications Letter, IEEE, 12, 6 (Jun. 2008), 411--413. DOI=http://dx.doi.org/10.1109/LCOMM.2008.080300.
[20]
Sadler, C. M., and Martonosi, M. 2006. Data compression algorithms for energy-constrained devices in delay tolerant networks. In Proceedings of the 4th international conference on Embedded networked sensor systems. ACM, 265--278. DOI=http://doi.acm.org/10.1145/1182807.1182834.
[21]
Kang, M., Kim. J., Yang, H., and Noh, D. 2013. The energy adaptive transmission power control for solar energy based sensor network and network performance analysis. KIICE, 17, 2 (Oct. 2013), 316.
[22]
Wu, R., Chen, M., Su, Y., and Siddiqui, H. J. 2009. A novel location-based routing algorithm for energy balance in wireless sensor networks. In Proceedings of the Communications and Mobile Computing, IEEE, 568--572. DOI=http://doi.ieeecomputersociety.org/10.1109/CMC.2009.218.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RACS '15: Proceedings of the 2015 Conference on research in adaptive and convergent systems
October 2015
540 pages
ISBN:9781450337380
DOI:10.1145/2811411
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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 October 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. data compression
  2. end-to-end delay
  3. energy adaptive
  4. sensor networks
  5. solar energy

Qualifiers

  • Research-article

Conference

RACS '15
Sponsor:

Acceptance Rates

RACS '15 Paper Acceptance Rate 75 of 309 submissions, 24%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 62
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 07 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media