METHOD OF TRANSMITTING INFORMATION ON THE INTERNET OF THINGS
DOI:
https://doi.org/10.20535/2411-2976.12021.41-47Keywords:
IoT, energy efficiency, life expectancy of the IoT networkAbstract
Background. The IoT technology covers devices and appliances, such as thermostats, home security systems and cameras, lighting fixtures as well as other household appliances that support one or more shared ecosystems, and can be controlled by devices associated with that ecosystem, for example with smartphones and smart speakers. However, there are a lot of problems to be solved. One of these problems is the power supply of wireless sensors on the Internet of Things.
Objective. The purpose of the study is to reduce energy consumption of IoT devices in the process of transmitting the collected data by regulating the number of transmission transactions.
Methods. The analysis of the existing energy saving methods in IoT devices shows that the problem of choosing the optimal buffer size has not yet been solved. An optimization problem has been formulated, which allows considering the requirements for the quality of transmission of both information flows and communication systems that provide this transfer.
Results. The article presents the modified method of information transmission to improve the energy efficiency of the network. The need to allocate a queue buffer at each of the nodes and explain the operation of the node using the queue buffer has been highlighted. The scheme of the project with the use of the modified Sleep / Wake algorithm has been created.
Conclusions. The main idea of the method is to allocate a buffer at each node with a certain threshold value, and if the latter is exceeded, the transmission of information packets will begin. This increases the service life of WSN by 14.8… 20.6% compared to the IoT sensor networks that use an asynchronous queue cycle.
Keywords: IoT; energy efficiency; life expectancy of the IoT network.
References
Dhar D. Energy Efficient Routing Algorithm with sleep scheduling in Wireless Sensor Network [Electronic resource] / D. Dhar, K. Praveen // International Journal of Computer Science and Information Technologies – Retrieved from https://pdfs.semanticscholar.org/a7b3/bd02a2ceb6b6f7b4ba1c897d38055146a137.pdf.
Gottheil A. Energy Efficiency with IoT [Electronic resource] / Avrohom Gottheil. – 2017. – Retrieved from https://theiotmagazine.com/energy-efficiency-with-iot-99afb953579a .
How can we improve energy efficiency in IOT? [Electronic resource]. – 2019. – Retrieved from https://www.quora.com/How-can-we-improve-energy-efficiency-in-IOT.
Narsingh G. Lifetime Improvement Of Wireless Sensor Network Using Co-Ordinated Duty Cycle And Queue Detect Technique [Electronic resource] / G. Narsingh, K. Rajeev // International Research Journal of Engineering and Technology (IRJET). – 2016. – Retrieved from https://pdfs.semanticscholar.org/2c85/f004a4373bf4b41fe5070eb7ba44f7e4eff2.pdf.
Runze W. An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks [Electronic resource] / W. Runze, X. Naixue. – 2018. – Retrieved from https://hcis-journal.springeropen.com/articles/10.1186/s13673-018-0141-x.
Contron. Internet of things: hyper-association of infrastructure [Electronic resource] / Kontron – Retrieved from https://vkt.ua/articles/internet-veshhej-giperobedinenie-infrastruktury/.
Overview of the Most Popular Smart Home Devices. – 2019. – http://iotlineup.com/ .
Pupena O. Fundamentals of the Internet of Things [Electronic resource] / Oleksandr Pupena. - 2019. - Retrieved from http://edu.asu.in.ua/mod/book/tool/print/index.php?id=112#ch230.
Rouse M. IoT devices (internet of things devices) [Electronic resource] / Margaret Rouse. - 2018. – Retrieved from https://internetofthingsagenda.techtarget.com/definition/IoT-device.
Development of IOT devices [Electronic resource]. – 2019. – Retrieved from https://evergreens.com.ua/ru/products/development/iot-devices.html .
LARYSA, Globa; MARIIA, Skulysh; SVITLANA, Sulima. Method for resource allocation of virtualized network functions in hybrid environment. In: 2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). IEEE, 2016. p.p. 1-5.
GLOBA, L., et al. Quality control for mobile communication management services in hybrid environment. In: The International Conference on Information and Telecommunication Technologies and Radio Electronics. Springer, Cham, 2018. p.p. 76-100.
LARISA, Globa; MARIIA, Skulysh; ANNA, Zastavenko. The method of resources allocation for processing requests in online charging system. In: The Experience of Designing and Application of CAD Systems in Microelectronics. IEEE, 2015. p.p. 211-213.
SKULYSH, Mariia; SULIMA, Svitlana. Management of multiple stage queuing systems. In: The Experience of Designing and Application of CAD Systems in Microelectronics. IEEE, 2015. p.p. 431-433.
M. Bhardwaj, T. Garnett, and A. Chandrakasan, “Upper bounds on the lifetime of sensor networks,” in Proc. 2001 IEEE ICC, pp. 785–790.
D. Estrin, R. Govindan, J. Heidemann, S. Kumar, Next century challenges: scalable coordination in sensor networks, in: Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, Washington, USA, August 1999, pp. 263-270.
C. F. Hsin and M. Liu, “Randomly duty-cycled wireless sensor networks: dynamic of coverage,” IEEE Trans. Wireless Commun., vol. 5, no. 11, pp. 3182– 3192, 2006.
H. Zhang and J. C. Hou, “On the upper bound of α−lifetime for large sensor networks,” ACM Trans. Sen. Netw., vol. 1, no. 2, pp. 272–300, 2005.
S. Slijepcevic. Power efficient organization of wireless sensor networks [Electronic resource] / S. Slijepcevic, M. Potkonjak. – 2002. – Retrieved from https://ieeexplore.ieee.org/abstract/document/936985/authors#authors .
Bhattacharyya, S. (2017). Handbook of Research on Recent Developments in Intelligent Communication Application. Retrieved from https://books.google.co.in
H. Schaffers, N. Komninos, M. Pallot, B. Trousse, M. Nilsson, and A. Oliveira, “Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation”, The Future Internet, Lecture Notes in Computer Science Volume 6656, pp. 431-446, 2011.
H. Kagermann, W. Wahlster, and J. Helbig, "Recommendations for implementing the strategic initiative INDUSTRIE 4.0 Final report of the Industrie 4.0 Working Group," 2013.
J. Davis, T. Edgar, J. Porter, J. Bernaden, and M. Sarli, "Smart manufacturing, manufacturing intelligence and demand-dynamic performance," Computers & Chemical Engineering, vol. 47, pp. 145- 156, 2012.
P.C.Evans, M.Annunziata, Industrial Internet: Pushing the Boundaries of Minds and Machines, November 2012, Available online at http://www.ge.com/docs/chapters/ Industrial_Internet.pdf.
W. Ye, J. Heidemann, and D. Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks,” Proc. IEEE INFOCOM pp. 1567-1576, 2002.
Mrs. Rakhi Khedikar1, Dr. Avichal Kapur2 and Yogesh Survanshi3,” Maximizing a Lifetime of Wireless Sensor Network by Scheduling”,2011.
J. Kim, X. Lin, N. B. Shroff, P. Sinha, “Minimizing delay and maximizing lifetime for wireless sensor networks with anycast”, Journal IEEE/ACM Transactions on Networking, vol.19, no.6, pp.851-864, June 2008.
W.L. Lee, A. Datta , R. Cardell-Oliver , "FlexiTP: A FlexibleSchedule-Based TDMA Protocol for Fault-Tolerant and Energy Efficient Wireless Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, , vol.19, no.6, pp.851-864, June 2008.
A Abbasi, K Akkaya, M. Younis , A distributed connectivity restoration algorithm. wireless sensor and actor networks. In: Proceedings of the 32nd IEEE conference on local computer networks (LCN 2007), Dublin, Ireland, October 2007.
Zheng, S. Radhakrishnan, V. Sarangan, PMAC: An adaptive energy efficient MAC protocol for wireless sensor networks, in: Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2005, pp. 65–72
