scholarly journals Industrial Automation using Wireless Sensor Networks

Author(s):  
R. Varun Arvind ◽  
R. Rohith Raj ◽  
R. Ranjithh Raj ◽  
N. Krishna Prakash
2014 ◽  
Vol 573 ◽  
pp. 407-411
Author(s):  
Chelliah Pandeeswaran ◽  
Natrajan Papa ◽  
Sundar G. Jayesh

MAC protocol design in Wireless sensor networks becomes vibrant research field for the past several years. In this paper an EE-Hybrid MAC protocol (Energy efficient hybrid Medium Access Control) has been proposed, which is energy efficient and low latency MAC protocol, which uses interrupt method to assign priority for certain wireless sensor nodes assumed to be present in critical loops of industrial process control domain. EE-Hybrid MAC overcomes some of the limitations in the existing approaches. Industrial wireless sensor network require a suitable MAC protocol which offers energy efficiency and capable of handling emergency situations in industrial automation domain. Time critical and mission critical applications demands not only energy efficiency but strict timeliness and reliability. Harsh environmental condition and dynamic network topologies may cause industrial sensor to malfunction, so the developed protocol must adapt to changing topology and harsh environment. Most of the existing MAC protocols have number of limitations for industrial application domain In industrial automation scenario, certain sensor loops are found to be time critical, where data’s have to be transferred without any further delay. The proposed EE-Hybrid MAC protocol is simulated in NS2 environment, from the result it is observed that proposed protocol provides better performance compared to the conventional MAC protocols.


2021 ◽  
Vol 10 (1) ◽  
pp. 245-254
Author(s):  
Xiaoran Zhang ◽  
Kantilal Pitambar Rane ◽  
Ismail Kakaravada ◽  
Mohammad Shabaz

Abstract Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.


Author(s):  
R. Varun Arvind ◽  
R. Rohith Raj ◽  
R. Ranjithh Raj ◽  
N. Krishna Prakash

Sign in / Sign up

Export Citation Format

Share Document