Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks

Main Article Content

Iman Dakhil Idan Saeedi
Ali Kadhum M. Al-Qurabat
http://orcid.org/0000-0002-8522-290X

Abstract

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Saeedi IDI, Al-Qurabat AKM. Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks. Baghdad Sci.J [Internet]. 2022 Aug. 1 [cited 2022 Nov. 30];19(4):0875. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6086
Section
article

References

Al‐Qurabat AK, Idrees AK. Data gathering and aggregation with selective transmission technique to optimize the lifetime of Internet of Things networks. Int. J. Commun. Syst. 2020 Jul 25;33(11):e4408.

Abdulzahra SA, Al-Qurabat AKM., Idrees AK. Data Reduction Based on Compression Technique for Big Data in IoT. In2020 International Conference on Emerging Smart Computing and Informatics (ESCI) 2020 Mar 12 (pp. 103-108). IEEE.

Idrees AK, Al-Qurabat AK, Abou Jaoude C, Al-Yaseen WL. Integrated divide and conquer with enhanced k-means technique for energy-saving data aggregation in wireless sensor networks. In2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) 2019 Jun 24 (pp. 973-978). IEEE.

Al-Qurabat AK, Idrees AK. Two level data aggregation protocol for prolonging lifetime of periodic sensor networks. Wirel. netw. 2019 Aug;25(6):3623-41.

Idrees AK, Al-Qurabat AK. Distributed Adaptive Data Collection Protocol for Improving Lifetime in Periodic Sensor Networks. IAENG Int. J.Com. Sci. 2017 Sep 1;44(3).

Al-Qurabat AK, Idrees AK. Energy-efficient adaptive distributed data collection method for periodic sensor networks. Int. j. internet technol. secur. trans. 2018;8(3):297-335.

Al-Qurabat AK, Idrees AK. Distributed data aggregation and selective forwarding protocol for improving lifetime of wireless sensor networks. J. Eng. Appl. Sci. 2018;13(5):4644-53.

Bahi JM, Makhoul A, Medlej M. A two tiers data aggregation scheme for periodic sensor networks. Adhoc & Sens. Wirel. Ne. 2014 Jan 1;21(1).

Harb H, Makhoul A, Couturier R, Medlej M. ATP: An aggregation and transmission protocol for conserving energy in periodic sensor networks. In2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises 2015 Jun 15 (pp. 134-139). IEEE.

Idrees AK, Al-Qurabat AK. Energy-Efficient Data Transmission and Aggregation Protocol in Periodic Sensor Networks Based Fog Computing. J. Netw. Syst. Manag. 2021 Jan;29(1):1-24.

Al-Qurabat AK, Abou Jaoude C, Idrees AK. Two tier data reduction technique for reducing data transmission in IoT sensors. In2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) 2019 Jun 24 (pp. 168-173). IEEE.

Idrees AK, Abou Jaoude C, Al-Qurabat AK. Data Reduction and Cleaning Approach for Energy-saving in Wireless Sensors Networks of IoT. In2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)(50308) 2020 Oct 12 (pp. 1-6). IEEE.

Al-Qurabat AK, Idrees AK, Abou Jaoude C. Dictionary-Based DPCM Method for Compressing IoT Big Data. In2020 International Wireless Communications and Mobile Computing (IWCMC) 2020 Jun 15 (pp. 1290-1295). IEEE.

Jawad GA, Al-Qurabat AK, Idrees AK. Compression-based Block Truncation Coding technique to Enhance the Lifetime of the Underwater Wireless Sensor Networks. InIOP Conference Series: Materials Science and Engineering 2020 Nov 1 (Vol. 928, No. 3, p. 032005). IOP Publishing.

Al-Qurabat AK, Idrees AK. Adaptive data collection protocol for extending lifetime of periodic sensor networks. Qalaai Zanist Sci. J. 2017 Apr 10;2(2):83-92.

Al-Qurabat AK, Idrees AK. Distributed data aggregation protocol for improving lifetime of wireless sensor networks. Qalaai Zanist Sci. J. 2017;2(2):204-15.

Abdulzahra SA, Al-Qurabat AK, Idrees AK. Compression-based Data Reduction Technique for IoT Sensor Networks. Baghdad Sci. J. 2021;18(1):0184-.

Shobana M, Sabitha R, Karthik S. Cluster-based systematic data aggregation model (CSDAM) for real-time data processing in large-scale WSN. Wirel. Pers. Commun. 2020 Jan 13:1-9.

Ramezanifar H, Ghazvini M, Shojaei M. A new data aggregation approach for WSNs based on open pits mining. Wirel. netw. 2020 Aug 5:1-3.

Alam MK, Aziz AA, Latif SA, Awang A. Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks. Sensors. 2020 Jan;20(4):1011.

Verma N, Singh D. Local Aggregation Scheme for Data Collection in Periodic Sensor Network. Int. J. Eng. Adv. Technol. 2019; 9(2):3583–3588.

Kumar S, Kim H. Energy efficient scheduling in wireless sensor networks for periodic data gathering. IEEE access. 2019 Jan 10;7:11410-26.

Sarangi K, Bhattacharya I. A study on data aggregation techniques in wireless sensor network in static and dynamic scenarios. Innov. Syst. Softw. Eng. 2019 Mar;15(1):3-16.

Yadav S, Yadav RS. Redundancy elimination during data aggregation in wireless sensor networks for IoT systems. InRecent trends in communication, computing, and electronics 2019 (pp. 195-205). Springer, Singapore.

SreeRanjani NY, Ananth AG, Reddy LS. An energy efficient data gathering scheme in wireless sensor networks using adaptive optimization algorithm. J. Comput. Theor. Nanosci. 2018 Nov 1;15(11-12):3456-61.

Khriji S, Raventos GV, Kammoun I, Kanoun O. Redundancy elimination for data aggregation in wireless sensor networks. In2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) 2018 Mar 19 (pp. 28-33). IEEE.

Atoui I, Ahmad A, Medlej M, Makhoul A, Tawbe S, Hijazi A. Tree-based data aggregation approach in wireless sensor network using fitting functions. In2016 Sixth international conference on digital information processing and communications (ICDIPC) 2016 Apr 21 (pp. 146-150). IEEE.

Karim L, Al-kahtani MS. Sensor data aggregation in a multi-layer big data framework. In2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2016 Oct 13 (pp. 1-7). IEEE.

Xiao S, Li B, Yuan X. Maximizing precision for energy-efficient data aggregation in wireless sensor networks with lossy links. Ad Hoc Netw. 2015 Mar 1;26:103-13.

Mottaghi S, Zahabi MR. Optimizing LEACH clustering algorithm with mobile sink and rendezvous nodes. AEU- Int. J. Electron. Commun. 2015 Feb 1;69(2):507-14.

Han Z, Wu J, Zhang J, Liu L, Tian K. A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Trans. Nucl. Sci. 2014 Apr 2;61(2):732-40.

Fu TC, Chung FL, Ng CM. Financial Time Series Segmentation based on Specialized Binary Tree Representation. DMIN. 2006 Jun;2006:26-9.

Zhang Z, Jiang J, Wang H. A new segmentation algorithm to stock time series based on pip approach. In2007 International Conference on Wireless Communications, Networking and Mobile Computing 2007 Sep 21 (pp. 5609-5612). IEEE.

Jiménez P, Nogal M, Caulfield B, Pilla F. Perceptually important points of mobility patterns to characterise bike sharing systems: The Dublin case. J. Transp. Geogr. 2016 Jun 1;54:228-39.

Bodik P. Intel berkeley research lab. 2004, Accessed on: Jan. 1, 2021, [Online] Available: http://db.csail.mit.edu/labdata/labdata.html.