Distributed Data Aggregation protocol for improving lifetime of Wireless Sensor Networks

Authors

  • Ali K. M. Al-Qurabat Department of Software, College of Information Technology, University of Babylon - Iraq
  • Ali Kadhum Idrees Department of Computer Science, College of Science for Women, University of Babylon - Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.2.2.22

Keywords:

Wireless Sensor Networks (WSNs), Data aggregation, APCA, Network Lifetime

Abstract

In Wireless Sensor Networks (WSN), the deployed sensor nodes can sense the same measures from the monitored area and forward these redundant measures to the sink node. Although redundant measures provide better accuracy but consume a lot of energy during the communication and the processing at the node and the sink, and thus decrease the lifetime of the WSN. Therefore, the elimination of redundant measures and reducing the communication cost are considered as essential characteristics during design the WSNs. In this article, a Distributed Data Aggregation (DiDA) protocol for prolonging the lifetime of WSNs is suggested. DiDA protocol is an energy efficient approach for a clustered network. DiDA works into cycles and each cycle aggregates and reduces data dimensionality by using an Adaptive Piecewise Constant Approximation (APCA) method. DiDA was successfully evaluated using OMNeT++ network simulator and based on sensed data of a real sensor network. Percentage of Sent data to the Cluster Head (CH), data accuracy, and energy consumption are the performance metrics applied to assess the effectiveness of the DiDA protocol. The conducted simulation results show that the proposed DiDA protocol decreases the consumed energy and extending the network lifetime, in comparison with a method without using data aggregation technique, whilst keeping the sensed data quality at the sink node 

Downloads

Download data is not yet available.

References

S. Misra, I. Zhang, and S. C. Misra, Guide to wireless sensor networks. Springer Science & Business Media, 2009.

A. K. Idrees, K. Deschinkel, M. Salomon, and R. Couturier, “Distributed lifetime coverage optimization protocol in wireless sensor networks,” The Journal of Supercomputing, vol. 71, no. 12, pp. 4578 – 4593, 2015.

X. Zhai and T. Vladimirova, “Data aggregation in wireless sensor networks for lunar exploration,” in 2015 Sixth International Conference on Emerging Security Technologies (EST). IEEE, 2015, pp. 30–37.

S. Pino-Povedano, R. Arroyo-Valles, and J. Cid-Sueiro, “Selective forwarding for energy-efficient target tracking in sensor networks,” Signal Processing, vol. 94, pp. 557– 569, 2014.

L. Yu, N. Wang, and X. Meng, “Real-time forest fire detection with wireless sensor networks,” in Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005., vol. 2. IEEE, 2005, pp. 1214–1217.

M. Dalbro, E. Eikeland, A. J. in’t Veld, S. Gjessing, T. S. Lande, H. K. Riis, and O. Sørasen,˚ “Wireless sensor networks for off-shore oil and gas installations,” in Sensor Technologies and Applications, 2008. SENSORCOMM’08. Second International Conference on. IEEE, 2008, pp. 258–263.

A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, “Wireless sensor networks for habitat monitoring,” in Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. ACM, 2002, pp. 88–97.

B. Yu, J. Li, and Y. Li, “Distributed data aggregation scheduling in wireless sensor networks,” in INFOCOM 2009, IEEE. IEEE, 2009 , pp. 2159–2167.

Y. Zheng, K. Chen, and W. Qiu, “Building representative-based data aggregation tree in wireless sensor networks,” Mathematical Problems in Engineering, vol. 2010, 2010.

J. M. Bahi, A. Makhoul, and M. Medlej, “Data aggregation for periodic sensor networks using sets similarity functions,” in 2011 7th International Wireless Communications and Mobile Computing Conference. IEEE, 2011, pp. 559–564.

M. A. Sharaf, J. Beaver, A. Labrinidis, and P. K. Chrysanthis, “Tina: a scheme for temporal coherency-aware in-network aggregation,” in Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access. ACM, 2003,

pp. 69–76.

F. Ren, J. Zhang, Y. Wu, T. He, C. Chen, and C. Lin, “Attribute-aware data aggregation using potential-based dynamic routing in wireless sensor networks,” IEEE transactions on parallel and distributed systems, vol. 24, no. 5, pp. 881–892, 2013.

H. Luo, H. Tao, H. Ma, and S. K. Das, “Data fusion with desired reliability in wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 3, pp. 501–513, 2011.

X. Xu, X.-Y. Li, P.-J. Wan, and S. Tang, “Efficient scheduling for periodic aggregation queries in multihop sensor networks,” IEEE/ACM Transactions on Networking (TON), vol. 20, no. 3, pp. 690–698, 2012.

W. B. Heinzelman, “Application-specific protocol architectures for wireless networks,” Ph.D. dissertation, Massachusetts Institute of Technology, 2000.

C. Liu, J. Luo, and Y. Song, “Correlation-model-based data aggregation in wireless sensor networks,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on. IEEE, 2015, pp. 822–827.

W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energyefficient communication protocol for wireless microsensor networks,” in System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on. IEEE, 2000, pp. 10 –pp.

I. H. Brahmi, S. Djahel, D. Magoni, and J. Murphy, “A spatial correlation aware scheme for efficient data aggregation in wireless sensor networks,” in Local Computer Networks Conference Workshops (LCN Workshops), 2015 IEEE 40th. IEEE, 2015, pp. 847–854.

O. Younis and S. Fahmy, “Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,” IEEE Transactions on mobile computing, vol. 3, no. 4, pp. 366–379, 2004.

J. Yuan and H. Chen, “The optimized clustering technique based on spatialcorrelation in wireless sensor networks,” in Information, Computing and Telecommunication, 2009. YC-ICT’09. IEEE Youth Conference on. IEEE, 2009, pp. 411– 414.

Z. Liu, W. Xing, Y. Wang, and D. Lu, “An energy-efficient data collection scheme for wireless sensor networks,” in Advanced Communication Technology (ICACT), 2013 15th International Conference on. IEEE, 2013, pp. 60–65.

L. A. Villas, A. Boukerche, H. A. De Oliveira, R. B. De Araujo, and A. A. Loureiro, “A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks,” Ad Hoc Networks, vol. 12, pp. 69–85, 2014.

T. D. Le, N. D. Pham, and H. Choo, “Towards a distributed clustering scheme based on spatial correlation in wsns,” in 2008 International Wireless Communications and Mobile Computing Conference. IEEE, 2008, pp. 529–534.

K. T.-M. Tran and S.-H. Oh, “Uwsns: A round-based clustering scheme for data redundancy resolve,” International Journal of Distributed Sensor Networks, vol. 2014, 2014.

C. Liu, K. Wu, and J. Pei, “An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation,” IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 7, pp. 1010–1023, 2007.

A. Zifan, M. H. Moradi, S. Saberi, and F. Towhidkhah, “Automated segmentation of ecg signals using piecewise derivative dynamic time warping,” International Journal of Biological and Life Sciences, vol. 1 , pp. 181–185, 2007.

Y. Wang, P. Wang, J. Pei, W. Wang, and S. Huang, “A data-adaptive and dynamic segmentation index for whole matching on time series,” Proceedings of the VLDB Endowment, vol. 6, no. 10, pp. 793–804 , 2013.

Y. B. Yahmed, A. A. Bakar, A. R. Hamdan, A. Ahmed, and S. M. S. Abdullah, “Adaptive sliding window algorithm for weather data segmentation,” Journal of Theoretical and Applied Information Technology, vol. 80, no. 2, p. 322, 2015.

“Omnet++ discrete event simulator.” 2016, [Online; accessed 23August-2016].

S. Madden, “Intel berkeley research lab,” 2004, [Online; accessed 25-August-2016]. [Online]. Available: http://db.csail.mit.edu/labdata/ labdata.html.

M. Kamarei, M. Hajimohammadi, A. Patooghy, and M. Fazeli, “An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach,” Wireless Personal Communications, vol. 84, no. 1, pp. 745–764, 2015.

Jesus, P., Baquero, C. and Almeida, P.S., “A survey of distributed data aggregation algorithms,” I

EEE Communications Surveys & Tutorials, vol. 17, no. 1, pp. 381-404, 2015.

Idrees AK, Deschinkel K, Salomon M, Couturier R. Coverage and lifetime optimization in heterogeneous energy wireless sensor networks. ICN 2014. 2014 Feb 23:60.

Idrees AK, Deschinkel K, Salomon M, Couturier R., “Perimeter-based coverage optimization to improve lifetime in wireless sensor networks,” Engineering Optimization, vol. 48, no. 11, pp. 1951-72, 2016.

Bahi, J.M., Makhoul, A., Medlej, M.: A two tiers data aggregation scheme for periodic sensor networks. Adhoc & Sensor Wireless Networks, vol. 21, no. 1, 2014.

Harb, H., Makhoul, A., Couturier, R. and Medlej, M., “ATP: An Aggregation and Transmission Protocol for Conserving Energy in Periodic Sensor Networks,” In IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 134-139, 2015.

Downloads

Published

2021-01-24

How to Cite

Ali K. M. Al-Qurabat, & Ali Kadhum Idrees. (2021). Distributed Data Aggregation protocol for improving lifetime of Wireless Sensor Networks. QALAAI ZANIST JOURNAL, 2(2), 204–215. https://doi.org/10.25212/lfu.qzj.2.2.22

Issue

Section

Articles