Samer SAWALHA, Ghazi AL-NAYMAT

Internet of things data compression based on successive data grouping

Internet of things (IoT) is a useful technology in different aspects, and it is widely used in many applications; however, this technology faces some major challenges which need to be solved, such as data management and energy saving. Sensors generate a huge amount of data that need to be transferred to other IoT layers in an eﬀicient way to save the energy of the sensor because most of the energy is consumed in the data transmission process. Sensors usually use batteries to operate; thus, saving energy is very important because of the diﬀiculty of replacing batteries of widely distributed sensors. Reducing the total amount of transmitted data from the perception layer to the network layer in the IoT architecture will save the energy of the sensor. This paper proposes a new IoT data compression method; it is based on grouping similar successive data together and sending them as one row with a total number of occurrences. The decision of similarity is done by comparing the root-mean-square successive difference calculated on the training dataset. The evaluation of the proposed method was performed on the Intel Lab dataset and the compression performance of the proposed method was compared with other compression methods, where a great enhancement was achieved; the compression ratio was 10.953 with a reconstruction of temperature data error equal to 0.0313 °C

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1] Patel KK, Patel SM. Internet of Things-IoT: definition, characteristics, architecture, enabling technologies, ap- plication & future challenges. International Journal of Engineering Science Computing 2016; 6: 6122-6131. doi: 10.4010/2016.1482
[2] Janani P, Siddhant V, Aditya K. Survey of data aggregation techniques in Internet of Things (IoT). International Journal of Recent Scientific Research 2018; 9: 23675-23679. doi: 10.24327/ijrsr.2018.0901.1514
[3] Zeinab KAM, Elmustafa SAA. Internet of Things applications, challenges and related future technologies. World Scientific News 2017; 2: 126-148.
[4] Al-Doghman F, Chaczko Z, Jiang J. A Review of aggregation algorithms for the Internet of Things. In: Proceedings of the 2017 25th International Conference on Systems Engineering (ICSEng 2017); Las Vegas, NV, USA; 2017. pp. 480-487. doi: 10.1109/ICSEng.2017.43
[5] Mattern F, Floerkemeier C. From the internet of computers to the Internet of Things. In: Sachs K, Petrov I, Guerrero P (editors). From Active Data Management to Event-Based Systems and More. Berlin/Heidelberg, Germany: Springer, 2010, pp. 242-259. doi: 10.1007/978- 3-642-17226-7_15
[6] Jia-Heng L, Xiao-Lin Z, Rong-Chao P, Feng L. An adaptive compression algorithm for Wireless Sensor Network based on piecewise Linear Regression. In: International Conference on Biomedical and Health Informatics, IFMBE Proceedings; Singapore; 2015. pp. 43-47. doi: 10.1007/978-981-10-4505-9_7
[7] Basheer A, Sha K. Cluster-based quality-aware adaptive data compression for streaming data. Journal of Data Information Quality 2017; 9 (2): 1-15. doi: 10.1145/3122863
[8] Azar J, Darazi R, Habib C, Makhoul A, Demerjian J. Using DWT Lifting Scheme for lossless data compression in wireless body sensor networks. In: Proceedings of the 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC 2018); Limassol, Cyprus; 2018. pp. 1465-1470. doi: 10.1109/IWCMC.2018.8450459
[9] Feng L, Kortoçi P, Liu Y. A multi-tier data reduction mechanism for IoT sensors. In: Proceedings of the 7th Interna- tional Conference on the Internet of Things (IoT 2017); Linz, Austria; 2017. pp. 1-8. doi: 10.1145/3131542.3131557
[10] Campobello G, Segreto A, Zanafi S, Serrano S. RAKE: A simple and eﬀicient lossless compression algorithm for the Internet of Things. In: Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO); Kos Island, Greece; 2017. pp. 2581-2585. doi: 10.23919/EUSIPCO.2017.8081677
[11] Donoho D. Compressed sensing. IEEE Transactions on Information Theory 2006; 52: 1289-1306. doi: 10.1109/TIT.2006.871582
[12] Norouzi M, Ranjbar M, Mori G. Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning. In: Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Miami, FL, USA; 2009. pp. 2735-2742. doi: 10.1109/CVPR.2009.5206577
[13] Liu J, Chen F, Wang D. Data compression based on Stacked RBM-AE model for Wireless Sensor Networks. Sensors 2018; 18 (4273): 1-20. doi: 10.3390/s18124273
[14] Wu M, Tan L, Xiong N. Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Information Sciences 2016; 329: 800-818. doi: 10.1016/j.ins.2015.10.004
[15] Abu Alsheikh M, Lin S, Niyato D, Tan HP. Rate-distortion balanced data compression for Wireless Sensor Networks. IEEE Sensors Journal 2016; 16: 5072-5083. doi: 10.1109/JSEN.2016.2550599
[16] Incebacak D, Zilan R, Tavli B, Barceló-Ordinas JM, Garcia-Vidal J. Optimal data compression for lifetime maximization in wireless sensor networks operating in stealth mode. Ad Hoc Networks 2015; 24: 134-147. doi: 10.1016/j.adhoc.2014.07.019
[17] Liang Y, Li Y. An eﬀicient and robust data compression algorithm in Wireless Sensor Networks. IEEE Communi- cations Letters 2014; 18: 439-442. doi: 10.1109/LCOMM.2014.011214.132319
[18] Ruxanayasmin B, Krishna BA, Subhashini T. Implementation of data compression techniques in mobile ad hoc networks. International Journal of Computer Applications 2013; 80: 8-12. doi: 10.5120/13879-1764
[19] Antonopoulos CP, Voros NS. Resource eﬀicient data compression algorithms for demanding, WSN based biomedical applications. Journal of Biomedical Informatics 2016; 59: 1-14. doi: 10.1016/j.jbi.2015.10.015
[20] Thomas S, Mathew T. In-network data compression in Wireless Sensor Networks using distributed block truncation coding. In: Proceedings of the 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC); Bangalore, India; 2018. pp. 1-7. doi: 10.1109/ICAECC.2018.8479478
[21] Yildirim O, Tan RS, Acharya UR. An eﬀicient compression of ECG signals using deep convolutional autoencoders. Cognitive Systems Research 2018; 52: 198-211. doi: 10.1016/j.cogsys.2018.07.004
[22] Deepika M, Belwin AF, Sherin AL, Thanamani AS. An eﬀicient compressive sensing data gathering using modified Ant Colony and Diffusion Wavelets in WSN. International Journal of Computer Science and Mobile Computing (IJCSMC) 2018; 7: 216-230.
[23] Desislava N, Petia M, Agata M, Petia G. ECG-based human emotion recognition across multiple subjects. In: Future Access Enablers for Ubiquitous and Intelligent Infrastructures: 4th EAI International Conference, FABULOUS 2019; Sofia, Bulgaria; 2019. pp. 25-36. doi: 10.1007/978-3-030-23976-3_3
[24] Jha CK, Kolekar MH. Electrocardiogram data compression using DCT based discrete orthogonal Stockwell trans- form. Biomedical Signal Processing and Control 2018; 46: 174-181. doi: 10.1016/j.bspc.2018.06.009
[25] Burgués J, Marco S. Multivariate estimation of the limit of detection by orthogonal partial least squares in temperature-modulated MOX sensors. Analytica Chimical Acta 2018; 1019: 49-64. doi: 10.1016/j.aca.2018.03.005
[26] Liu X, Wu J. A method for energy balance and data transmission optimal routing in Wireless Sensor Networks. Sensors 2019; 19 (3017): 1-20. doi: 10.3390/s19133017
[27] Huang J, Qian F, Gerber A, Mao ZM, Sen S et al. A close examination of performance and power characteristics of 4G LTE networks. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services; Ambleside, UK; 2012. pp. 225-238. doi: 10.1145/2307636.2307658
[28] Jha SC, Koc AT, Vannithamby R. Device power saving mechanisms for low cost MTC over LTE networks. In: Proceedings of the 2014 IEEE International Conference on Communications Workshops (ICC); Sydney, Australia; 2014. pp. 412-417. doi: 10.1109/ICCW.2014.6881233