Compression-based Data Reduction Technique for IoT Sensor Networks

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Suha Abdulhussein Abdulzahra
Ali Kadhum M. Al-Qurabat
Ali Kadhum Idrees


Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature.


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Abdulzahra SA, Al-Qurabat AKM, Idrees AK. Compression-based Data Reduction Technique for IoT Sensor Networks. Baghdad Sci.J [Internet]. [cited 2021Jan.25];18(1):0184. Available from:


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; 33(11);

Al-Qurabat AK, Jaoude CA, 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.

Xu G, Shi Y, Sun X, Shen W. Internet of Things in Marine Environment Monitoring: A Review. Sensors. 2019 Jan;19(7):1711.

Liu X, Sheng Z, Yin C. Routing Protocol for Low Power and Lossy IoT Networks. In From Internet of Things to Smart Cities 2017 Sep 1 (pp. 89-118). Chapman and Hall/CRC.

Al-Qurabat AK, Idrees AK. Energy-efficient adaptive distributed data collection method for periodic sensor networks. IJITST. 2018;8(3):297-335.

Jon Y. Adaptive sampling in wireless sensor networks for air monitoring system. 2016(Dissertation). Retrieved from http://urn.

Al-Qurabat A, Idrees A. Distributed data aggregation protocol for improving lifetime of wireless sensor networks. QZSJ . 2017;2(2):204-15.

McAnlis C, Haecky A. Understanding compression: Data compression for modern developers. " O'Reilly Media, Inc."; 2016 Jul 13.

Bahi JM, Makhoul A, Medlej M. A two tiers data aggregation scheme for periodic sensor networks. AD HOC SENS WIREL NE. 2014 Jan 1;21(1-2):77-100.

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.

Al-Qurabat AK, Idrees AK. Two level data aggregation protocol for prolonging lifetime of periodic sensor networks. WIREL NETW. 2019 Aug 1;25(6):3623-41.

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

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 S1):4644-53.

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

Idrees AK, Al-Qurabat AK, Jaoude CA, 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.

Qian J, Tiwari P, Gochhayat SP, Pandey HM. A noble double dictionary based ECG Compression Technique for IoTH. IEEE Internet Things J. 2020 Feb 18.

Lin CH, Wang WJ, Chen JC, Lin CW. Code Compression for Embedded Systems. In Embedded, Cyber-Physical, and IoT Systems 2020 (pp. 115-147). Springer, Cham.

Azar J, Makhoul A, Darazi R, Demerjian J, Couturier R. On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks. In2018 IEEE Middle East and North Africa Communications Conference (MENACOMM) 2018 Apr 18 (pp. 1-6). IEEE.

Schoellhammer T, Greenstein B, Osterweil E, Wimbrow M, Estrin D. Lightweight temporal compression of microclimate datasets. In 29th Annual IEEE International Conference on Local Computer Networks 2004 (pp. 516-524). IEEE.

Arrabi S, Lach J. Adaptive lossless compression in wireless body sensor networks. In Proceedings of the Fourth International Conference on Body Area Networks 2009 Apr 1 (pp. 1-8).

Aboelela E. Liftingwise: A lifting-based efficient data processing technique in wireless sensor networks. Sensors. 2014 Aug;14(8):14567-85.

Marcelloni F, Vecchio M. An efficient lossless compression algorithm for tiny nodes of monitoring wireless sensor networks. Comput. J. 2009 Nov 1;52(8):969-87.

Harb H, Makhoul A, Laiymani D, Bazzi O, Jaber A. An analysis of variance-based methods for data aggregation in periodic sensor networks. In Transactions on large-scale data-and knowledge-centered systems XXII 2015 (pp. 165-183). Springer, Berlin, Heidelberg.

Fomina M, Antipov S, Vagin V. Methods and algorithms of anomaly searching in collections of time series. In Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16) 2016 (pp. 63-73). Springer, Cham.

Eichinger F, Efros P, Karnouskos S, Böhm K. A time-series compression technique and its application to the smart grid. The VLDB J. 2015 Apr 1;24(2):193-218.

Bondu A, Boullé M, Cornuéjols A. Symbolic representation of time series: A hierarchical coclustering formalization. In International Workshop on Advanced Analysis and Learning on Temporal Data 2015 Sep 11 (pp. 3-16). Springer, Cham.

Sayood K. Introduction to data compression. Morgan Kaufmann; 2017 Oct 23.

Liu C, Luo J, Song Y. Correlation-model-based data aggregation in wireless sensor networks. In2015 12th international conference on fuzzy systems and knowledge discovery (FSKD) 2015 Aug 15 (pp. 822-827). IEEE.

Heinzelman WR, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences 2000 Jan 7 (pp. 10-pp). IEEE.