Swinging Door Trending Compression Algorithm for IoT Environments

  • Juan David Arias Correa UFSC
  • Alex Sandro Roschildt Pinto UFSC
  • Carlos Montez UFSC
  • Erico Meneses Leão UFPI


The transmission and storage of data collected by the devices are essential components of the Internet of Things (IoT). When devices send unnecessary or redundant information, it spends more energy, unnecessarily using the communication channel, besides processing at the destination, data that make a small contribution to the application. Data compression is a possible solution for the significant quantity of information generated by IoT devices. Data compression is the process of reducing the quantity of data necessary to represent some volume of data. This paper proposes the use of Swinging Door Trending (SDT) into an IoT environment and a new calibration step to select its major parameter: the compression deviation. A prototype was built, and experimental results show the effectivity of the proposal.

Palavras-chave: Data compression, IoT, Internet of Things


J. Uthayakumar, T. Vengattaraman, and P. Dhavachelvan, “A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications,” Journal of King Saud University - Computer and Information Sciences, 2018.

W. Yu, F. Liang, X. He, W. G. Hatcher, C. Lu, J. Lin, and X. Yang, “A survey on the edge computing for the internet of things,” IEEE Access, vol. 6, pp. 6900–6919, 2018.

A. R. Biswas and R. Giaffreda, “Iot and cloud convergence: Opportunities and challenges,” in 2014 IEEE World Forum on Internet of Things (WF-IoT), March 2014, pp. 375–376.

J. Azar, A. Makhoul, M. Barhamgi, and R. Couturier, “An energy efficient iot data compression approach for edge machine learning,” Future Generation Computer Systems, vol. 96, pp. 168-175. 2019.

B. R. Stojkoska and Z. Nikolovski, “Data compression for energy efficient iot solutions,” in 2017 25th Telecommunication Forum (TELFOR), Nov 2017, pp. 1–4.

O. MAHDI, M. Mohammed, and A. Jasim Mohamed, “Implementing a novel approach an convert audio compression to text coding via hybrid technique,” IJCSI, 11 2013.

E. H. Bristol, “Swinging door trending: adaptive trend recording,” in Proc. of the ISA National Conf., 1990, pp. 749–753.

K. P. Shravana and D. S. V. Veena, “Review on lossless data compression using x-matchpro algorithm,” in 2017 2nd IEEE Int. Conf. on Recent Trends in Electronics, Inf. Comm. Technology (RTEICT), May 2017, pp. 1095–1100.

D. C. S. L. A. G. Edson J. M. Neto, Luiz Augusto, “Adaptive swinging door trending: Um algoritmo adaptativo para compressao de dados em tempo real,” Anais do XX Congresso Brasileiro de Automática, 2014.

E. H. Bristol, “Data compression for display and storage,” May 1987, uS Patent 4,669,097.

V. Alieksieiev, “One approach of approximation for incoming data stream in iot based monitoring system,” in 2018 IEEE Second Int. Conf. on Data Stream Mining Processing (DSMP), Aug 2018, pp. 94–97.

K. Hossain and S. Roy, “A data compression and storage optimization framework for iot sensor data in cloud storage,” in 2018 21st Int. Conf. of Computer and Inf. Technology (ICCIT), Dec 2018, pp. 1–6.

C. Deepu, C.-H. Heng, and Y. Lian, “A hybrid data compression scheme for power reduction in wireless sensors for iot,” IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 2, pp. 245–254, 2017, cited By 24.

F. Xiaodong, C. Changling, L. Changling, and S. Huihe, “An improved process data compression algorithm,” in Proc. of the 4th World Congress on Intelligent Control and Automation, vol. 3, June 2002, pp. 2190–2193.

J. K. Zhao, L. S. Mu, H. Ouyang, P. F. Zhu, Y. K. Li, L. H. Yang, and L. J. Chen, “In-network time-series data compression for electric internet of things,” in Applied Mechanics and Materials, vol. 241, 2013, pp. 3213–3223.

X. M. Liu, S. Han, Y. J. Zhou, and Y. Yao, “An energy-efficiency wireless sensing method for mechanical failure signal,” in Key Engineering Materials, vol. 572, 2014, pp. 451–454.

I. M. D. Silva, L. A. Guedes, and F. Vasques, “Performance evaluation of a compression algorithm for wireless sensor networks in monitoring applications,” in 2008 IEEE Int. Conf. on Emerging Technologies and Factory Automation, Sep. 2008, pp. 672–678.

E. M. Leao, L. A. Guedes, and F. Vasques, “An event-triggered smart sensor network architecture,” in 2007 5th IEEE Int. Conf. on Ind. Inf., vol. 1, June 2007, pp. 523–528.

Ai-Thinker, “Esp-12e wifi module,” 2015, v1.0. [Online]. Available: https://www.kloppenborg.net/images/blog/esp8266/esp8266-esp12e-specs.pdf.

R. A. Light, “Mosquitto: server and client implementation of the mqtt protocol,” Journal of Open Source Software, 2017.

L. Schrickte, C. Montez, R. De Oliveira, and A. Pinto, “Design and implementation of a 6LoWPAN gateway for wireless sensor networks integration with the internet of things,” International Journal of Embedded Systems, vol. 8, no. 5-6, pp. 380–390, 2016.

L. Ashcroft, J. R. Coll, A. Gilabert, P. Domonkos, E. Aguilar, J. Sigro, M. Castella, P. Unden, I. Harris, P. Jones, and M. Brunet, “Air temperature and Relative humidity measurements in Slovenia,” 2018. [Online]. Available: 10.1594/PANGAEA.887091.
Como Citar

Selecione um Formato
CORREA, Juan David Arias; PINTO, Alex Sandro Roschildt; MONTEZ, Carlos; LEÃO, Erico Meneses. Swinging Door Trending Compression Algorithm for IoT Environments. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 9. , 2019, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 143-148. ISSN 2763-9002. DOI: https://doi.org/10.5753/sbesc_estendido.2019.8650.