Registry service selection based secured Internet of Things with imperative control for industrial applications

The Internet of Things (IoT) technology is currently growing various perspectives on human life. Automated liquid industrial measurement is one of the models which can use the IoT services to optimize the composition performance and regularity across the liquid industrial regions, maximizing the quality of the process measurements, and minimize the negative impact using imperative control algorithm. In this work, we present an IoT architecture customized for industrial applications. The proposed register service selection based security architecture collects the required data and transmits it to a cloud-based back-end where it is processed and analyzed. In this work, register service selection (RSS) based cloud data security algorithm with imperative control procedure is developed to get rid of the existing stated problems. The protection, data privacy, and integrity that serve as a comprehensive guide for achieving higher data protection level in the clouds. The models cover a maximum of the features of reliable clouds, including security and privacy conditions, attacks and warnings that clouds are vulnerable to and the risks and concerns about cloud security. Moreover, we advise a generic security model for cloud computing that helps meet their safety demands and defend the clouds into various malicious behaviors. Feedback operations based on the analyzed data can be sent back to the front-end nodes. Finally, a prototype of the proposed structure to show its appearance advantages.

[1]  Kwok-wing Chau,et al.  Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River , 2006 .

[2]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[3]  S. Joshi,et al.  Liquid level sensor using ultrasonic Lamb waves. , 2003, Ultrasonics.

[4]  Erol Gelenbe,et al.  Random Neural Networks with Negative and Positive Signals and Product Form Solution , 1989, Neural Computation.

[5]  Gregory M. P. O'Hare,et al.  A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems , 2014, ACM Trans. Sens. Networks.

[6]  Stelios Timotheou,et al.  The Random Neural Network: A Survey , 2010, Comput. J..

[7]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[8]  Gagandeep Kaur,et al.  Comparative Analysis of Conventional, P, PI, PID and Fuzzy Logic Controllers for the Efficient Control of Concentration in CSTR , 2011 .

[9]  Taskin Koçak,et al.  Learning in the feed-forward random neural network: A critical review , 2011, Perform. Evaluation.

[10]  Desmond Gibson,et al.  A Novel Solid State Non-Dispersive Infrared CO2 Gas Sensor Compatible with Wireless and Portable Deployment , 2013, Sensors.

[11]  B. G. Khusainov.  Float method for measuring liquid levels , 1978 .

[12]  Erol Gelenbe,et al.  Learning in the Recurrent Random Neural Network , 1992, Neural Computation.

[13]  Klaus Schittkowski,et al.  A comparative performance evaluation of 27 nonlinear programming codes , 1983, Computing.

[14]  Jiang Xuehua,et al.  Design and Implementation of Remote Monitoring System Based on GSM , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[15]  Sajal K. Das,et al.  Intelligent Management Systems for Energy Efficiency in Buildings , 2014, ACM Comput. Surv..

[16]  K. Baskaran,et al.  Implementation of a Fuzzy PI Controller for Speed Control of Induction Motors Using FPGA , 2010 .

[17]  Mihaela Raduca,et al.  Fuzzy controller for adjustment of liquid level in the tank , 2011 .

[18]  S. Bhargavi.  Design and implementation of GSM based remote monitoring and control system for industrial process parameters , 2010 .