Wireless Sensor Network for Industrial Monitoring and Controlling

Author(s):  
N Dharani ◽  
Kirupa Krishnan ◽  
Komati Vishwa Mohan
2019 ◽  
Vol 20 (4) ◽  
pp. 591-598 ◽  
Author(s):  
Ashish Kumar Luhach ◽  
Aditya Khamparia ◽  
Ravindra Sihag ◽  
Raj Kumar

Wireless Sensor Network (WSN) has emerged as one of the most important technologies serving an array of solutions for critical applications such as defense, industrial monitoring and decision purposes. Data routing in WSN is effective or non-effective depending upon the energy saving for nodes while transferring data packets to the sink. Mainly WSN divided into two modes; heterogeneous and homogeneous. Heterogeneous network in WSN mainly focused on the cluster head selection. Sink mobility in the heterogeneous network has still many open research issues, it is observed that it makes the network more energy efficient. The optimization in the network leads to the stability of the network at a much higher level. In this paper, the sink mobility is optimized for WSN using Honey Bee Optimization (HBO) technique by considering the parameters such as energy and distance. The proposed protocol shows significant improvement in the stability period by 33 % by covering 2928 rounds and enhanced network lifetime by 1500 rounds in compared with 2033 and 14084 rounds for iMBEENISH protocol respectively.


Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 182 ◽  
Author(s):  
Juan Aponte-Luis ◽  
Juan Gómez-Galán ◽  
Fernando Gómez-Bravo ◽  
Manuel Sánchez-Raya ◽  
Javier Alcina-Espigado ◽  
...  

Author(s):  
Ashish K. Maurya ◽  
Dinesh Singh ◽  
Anil K. Sarje

Wireless Sensor Network (WSN) consists of spatially distributed self-organizing, low-powered sensing devices with limited computational and communication resources to cooperatively monitor conditions, such as temperature, sound, vibration, pressure and humidity over a specific area for some specific purposes like target tracking, area monitoring, industrial monitoring, health monitoring, surveillance, environmental monitoring etc and report the collected data of all sensors to the user for analysis. Energy is a primary constraint in the design of sensor networks. This fundamental energy constraint further limits everything from data sensing rates and link bandwidth, to node size and weight. In most cases, the radio is the main energy consumer of the system; lifetime of sensor node may extended by reducing transmissions/receptions of data. It is useful to apply data compression to reduce the volume of data, and the associated energy consumption of transmission. In this paper, we propose a simple and efficient data compression algorithm which is lossless and particularly suited to the reduced memory and computational resources of a WSN node. The proposed data compression algorithm gives good compression ratio for highly correlated data as well as low correlated data.


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