Novel QoS optimization paradigm for IoT systems with fuzzy logic and visual information mining integration
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The Internet of Things is a new round of information technology revolution after computers, the Internet and mobile communications. Internet of Things technology is an important means to improve the level of social information, which will have a profound impact on economic development and social life. IoT can stimulate the economy, increase employment, improve efficiency and make people’s lives and work more convenient. Since fuzzy control can make good use of expert fuzzy information and effectively deal with the complex process of modeling, fuzzy control has received extensive attention once it has been proposed. Fuzzy logic system has become a research hotspot in academic and application fields due to its wide application. Fuzzy system identification includes structure identification and parameter identification. Fuzzy cognitive graph is a kind of soft computing method. It has stronger semantics than neural network because of its intuitive expression ability and powerful reasoning ability. Due to the widespread popularity of visual data acquisition devices, people can use the device to capture a large number of videos and images and spread them over the network in daily learning, production, life, work and entertainment. Computer science and technology, information computing technology, automated detection technology and Internet of Things technology contribute to the research of visual information data. In this paper, we conduct research on the novel QoS optimization paradigm for the IoT systems based on fuzzy logic and visual information mining integration. The experimental results show that the proposed optimization scheme has higher robustness.
KeywordsInternet of Things Information gathering Visual information Data mining
This study was funded by National Natural Science Foundation of China, No. 61402544.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Gates B, Myhrvold N, Rinearson P (1995) The road ahead, 1st edn. Viking Penguin, New York, pp 5–16Google Scholar
- 2.ITU Strategy Policy Unit (SPU) (2005) ITU International Reports 2005: the Internet of Things. International Telecommunication Union (ITU), Geneva, pp 8–20Google Scholar
- 3.Antoine de Saint-Exupery (2009) Internet of Things-Strategic Research Roadmap, pp 56–77Google Scholar
- 10.Feng B, Chen W, Sun J (2006) Chaotic time series forecasting with PSO-trained RBF neural network. In: DCABES 2006 proceedings, vol 2, pp 787–790 (ISTP)Google Scholar
- 14.Louppe G (2014) Understanding random forests: from theory to practice. arXiv preprint arXiv:1407.7502
- 16.Zhang J, Hsu W, Lee M (2001) Image mining: issues, frameworks and techniques. In: Proceedings of the second international workshop on multimedia data miningGoogle Scholar
- 19.Rajendran P, Madheswaran M (2010) Hybrid medical image classification using association rule mining with decision tree algorithm. J Comput 2(1):127–136Google Scholar
- 20.Premchaiswadi W, Tungkatsathan A (2010) On-line content-based image retrieval system using joint querying and relevance feedback scheme. WSEAS Trans Comput 9(5):465–474Google Scholar
- 22.Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018) Centralized fog computing security platform for IoT and cloud in healthcare system. In: Krishna Prasad AV (ed) Exploring the convergence of big data and the internet of things. IGI Global, Hershey, pp 141–154CrossRefGoogle Scholar