English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Use of ARIMA model in Sensor network The basic idea of our energy efficientinformation collection scheme is to suppress data transmission if the datasampled by sensor nodes are predictable by the sink node. This is done in twophases 1) Preliminary Data Collection- During this phase sink node collectsenough data so that it can build up ARIMA model for each node. Then sink nodeselects a model for the particular node and sends back the corresponding modelparameters to the node and also keeps them with it. Selecting the model for anode there is a tradeoff between energy consumption and accuracy of prediction.So we choose the model according to C = {\alpha} xMAE + (1 - {\alpha}) x rtran0=< {\alpha} =<1 where the model should minimize C. Here MAE is Mean AbsoluteError which is normalized by some predefined error tolerance and rtran is theratio of number of samples transmitted over total number of samples. 2)Adaptive Data Collection- After the sensor node has received the modelparameters it checks each actual data value with the data value calculated fromthe parameters received. If there is deviation beyond some predefined errortolerance then only it sends the original data value to the sink node.

Merging Two Arima Models for Energy Optimization in WSN