JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Energy-aware Selective Compression Scheme for Solar-powered Wireless Sensor Networks
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of KIISE
  • Volume 42, Issue 12,  2015, pp.1495-1502
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.12.1495
 Title & Authors
Energy-aware Selective Compression Scheme for Solar-powered Wireless Sensor Networks
Kang, Min Jae; Jeong, Semi; Noh, Dong Kun;
 
 Abstract
Data compression involves a trade-off between delay time and data size. Greater delay times require smaller data sizes and vice versa. There have been many studies performed in the field of wireless sensor networks on increasing network life cycle durations by reducing data size to minimize energy consumption; however, reductions in data size result in increases of 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, redundant energy is often generated in amounts sufficient to run a node. In this study, this 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 below the threshold transfer data compressed to reduce energy consumption, and nodes with residual energy above 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.
 Keywords
solar energy;sensor networks;data compression;end-to-end delay;energy adaptive;
 Language
Korean
 Cited by
 References
1.
S. Sudevalayam and P. Kulkarni, "Energy harvesting sensor nodes: Survey and implications," Journal of IEEE Communications Surveys & Tutorials, Vol. 13, No. 3, pp. 443-461, Jul. 2010.

2.
Y. Yang, L. Wang, D. K. Noh, H. K. Le and T. F. Abdelzaher, "SolarStore: Enhancing Data Reliability in Solar-powered Storage-centric Sensor Networks," Proc. of the 7th Annual International Conference on Mobile Systems, Applications, and Services, pp. 333-346, Jun. 2009.

3.
H. J. Lee, H. Kim and I. J. Chang, "CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks," Journal of Sensors 2014, Vol. 14, No. 4, pp. 6419-6442, Apr. 2014. crossref(new window)

4.
I. Stojmenovic. Handbook of Sensor Networks. Wiley-Interscience, 2005.

5.
C. Alippi, G. Anastasi, M. D. Francesco, and M. Roveri, "Energy Management in Wireless Sensor Networks with Energy-hungry Sensors," Journal of IEEE Instrumentation & Measurement Magazine, Vol. 12, No. 2, pp. 16-23, Apr. 2009.

6.
A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava, "Power management in energy harvesting sensor networks," Journal of ACM Transactions on Embedded Computing Systems, Vol. 6, No. 4, Sep. 2007.

7.
X. Jiang, J. Polastre, and D. Culler, "Perpetual environmentally powered sensor networks," Forth International Symposium on Information Processing in Sensor Networks 2005, pp. 463-468, Apr. 2005.

8.
A. Kansal, J. Hsu, M. Srivastava and V. Raghunathan, "Harvesting aware power management for sensor networks," Proc. of 43rd Annual Design Automation Conference, pp. 651-656, Jul. 2006.

9.
C. Alippi. and C, Galperti, "An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes," IEEE Trans. on Circuits and Systems, Vol. 55, No. 6, pp. 1742-1750, Jul. 2008. crossref(new window)

10.
J. Taneja, J. Jeong and D. Culler, "Design, Modeling, and Capacity Planning for Micro-solar Power Sensor Networks," Proc. of 7th international conference on Information processing in sensor networks, pp. 407-418, Apr. 2008.

11.
D. Noh, D. Lee, H. Shin, "QoS-Aware Geographic Routing for Solar-Powered Wireless Sensor Networks," Journal of The Institute of Electronics, Information and Communication Engineers Transactions on Communications, Vol. 90-B, No. 12, pp. 3373-3382, Jan. 2007.

12.
D. Noh, I. Yoon and H. Shin, "Low-latency geographic routing for asynchronous energy-harvesting WSNs," Journal of Networks, Vol. 3, No. 1, pp. 78-85, Jan. 2008.

13.
D. Petrović, R. C. Shah, K. Ramchandran and J. Rabaey, "Data Funneling: Routing with Aggregation and Compression for Wireless Sensor Networks," Proc. of First IEEE International Workshop on Sensor Network Protocols and Applications, pp. 156-162, May 2003.

14.
T. Srisooksai, K. Keamarungsi, P. Lamsrichan and K. Araki, "Practical data compression in wireless sensor networks: A survey," Journal of Network and Computer Applications, Vol. 35, No. 1, pp. 37-59, Jan. 2012. crossref(new window)

15.
C. M. Sadler and M. Martonosi, "Data compression algorithms for energy-constrained devices in delay tolerant networks," Proc. of the 4th international conference on Embedded networked sensor systems, pp. 265-278, Nov. 2006.

16.
S. Madden, Intel Lab Data, Intel Research Lab at Berkeley, [online]. Available: http://db.lcs.mit. edu/ labdata/labdata.html(downloaded 2013, Aug.16).

17.
J. Yi, M. Kang and D. Noh, "SolarCastalia - Solar Energy Harvesting Wireless Sensor Network Simulator," Journal of Distributed Sensor Networks, Vol. 2015, pp. 1-10, May. 2015.

18.
M. Kang, J. Kim, H. Yang and D. K. Noh, "Energy-aware Transmission Power Control for Solar Energy harvesting Wireless sensor system and Its Effects on Network-wide Performance," Proc. of the 34th KIICE Fall Conference, pp. 316, Nov. 2013. (in Korean)

19.
R. Wu, M. Chen, Y. Su and H. J. Siddiqui, "A Novel Location-Based Routing Algorithm or Energy balance in Wireless Sensor Networks," Proc. of the IEEE Communications and Mobile Computing, pp. 568-572, Jan. 2009.