A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Algorithm 1

Softmax regressed Tanimoto reweight boost classification-based reliable and congestion aware routing algorithm.

Input: sensor Nodes , source node, destination node
Output: improve congestion aware routing in WSN
Begin
(1)	For each
(2)	Measure residual energy of sensor node
(3)	Analyze the residual energy by using softmax regression
(4)	if , then
(5)	is energy-efficient node
(6)	else
(7)	is nonenergy efficient node
(8)	end if
(9)	end for
(10)	Create the route paths between source and destination node
(11)	sends to sink
(12)	send to source
(13)	Construct the multiple routes ‘, and ’
(14)	for each constructed route path
(15)	Construct ‘n’ weak learners
(16)	Measure the Tanimoto similarity correlation
(17)	Classifies route path into two different classes
(18)	Combines all weak learners
(19)	Assign similar weights to the weak classifier
(20)	Compute the error
(21)	Update the weight of weak classifiers
(22)	Find classifier with minimum error
(23)	Attain the strong classification results
(24)	End for
	End