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Aug. 2020
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Issue Published : 87
Paper Published : 1231
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  Journal Paper

Paper Title
Adaptive Sensing For Energy Efficient Data Collection in Wireless Sensor Networks

Abstract
Wireless sensor networks are generally deployed in inaccessible terrains for monitoring certain physical parameters like temperature, humidity, radiations, vibrations etc. As large numbers of sensor nodes are sensing the region, it is likely that sensed data may be both spatially and temporally correlated. These correlations can be exploited to decrease communication and data exchange so as to minimize energy loss. Therefore, effective energy management in these networks should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an Effective Adaptive Sensing Algorithm (EASA) that controls the sensing rates of sensor nodes. To do this, algorithm utilizes the local estimation and correlations at every sensor node and finds correlation among these sensed values at cluster head level. The strategy thrives at dynamically altering the sensing frequency of sensor nodes based on this correlation. Highly correlated values depict the static nature of the event under observations whereas highly uncorrelated values points towards very dynamic event. Thus, sensing a static event repeatedly resulting in redundant data as well as missing some important readings due to low sensing frequency is both taken care of by dynamically setting the sensing frequency. Simulation results show that EASA provides substantial energy saving as compared to other adaptive sensing algorithms. Index Terms— Wireless Sensor Networks, Sensor Node, Cluster Head, Adaptive Sensing, Energy Efficiency.


Author - Vipan Arora, T.P. Sharma

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| Published on 2016-12-15
   
   
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