Optimization of Adaptive Method for Data Reduction in Wireless Sensor Networks
Keywords:
Wireless Sensor Networks (WSN), Internet of Things (IoT), Least Mean Square Algorithm (LMS), Decouple Least Mean Square AlgorithmAbstract
The term ‘Wireless’ is a cordless technology where the nodes interact or exchange information with the sink node without wired intervention to exchange or transmit any information successfully. Characteristics of the present wireless sensor networks are applied to diverse technological furtherance in minimum power communications and very large-scale integration to sustained functionalities of sensing. Tremendous number of incentive observation and algometry of data are amassed from sensors in Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) applications such as environmental monitoring. However, continuous dissemination of the sensed data postulates eminent energy imbibing. Data reduction duress the sensor nodes to surcease transmitting the data when it is diffident about freshen up. One way to reduce this kind of energy imbibing is to minimize the amount of data exchanged across the sensors, therefore the research work aims to increase the communication and spatial prediction between the sensor nodes and the sink nodes.
In this research work, an Optimization of Adaptive Method for Data Reduction in Wireless Sensor Networks was proposed and implemented. The work adopted a bulging haplotype of two decoupled Least-Mean-Square (LMS) windowed filters with varying length for approximating the immediate metrics values both at the sink and source node such that sensor nodes have to send only their next sensed values that diverse substantially (when a pre-determine threshold) from the anticipated values. The experiment conducted on a real- world dataset of about 2,313,682, which were collected from 54 Mica2dot sensors thus, MATLAB was used as a tool for the implementation. The research work aims to increase the communication model and spatial prediction, which is the limitation of the base paper. The results show that our approach (OAM-DR) has achieved up to 98% communication reduction while retaining or carrying a high accuracy, (i.e. the anticipated values have a digression of ±0.5 from actual data values).
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