Metaheuristic Load-Balancing-Based Clustering Technique in Wireless Sensor Networks

Table 3

Mean of standard deviations and confidence intervals for the clusters generated.

Clustering scenario	#nodes			Interval estimate with 95% confidence		Interval estimate with 99% confidence
				With -fitness	With -fitness	With -fitness	With -fitness
Scenario#1 (with 5 clusters)	50	18.9254	4.4628	[4.7541, 15.2459]	[8.7630, 11.2370]	[3.0947, 16.9053]	[8.3717, 11.6283]
	100	24.3826	5.9975	[15.2210, 24.7790]	[18.8245, 21.1755]	[13.7093, 26.2907]	[18.4526, 21.5474]
	150	27.1672	6.9578	[25.6523,34.3477]	[28.8865, 31.1135]	[24.2771, 35.7229]	[28.5343, 31.4657]
	200	25.9534	5.7280	[36.4030, 43.5970]	[39.2061, 40.79939]	[35.2652, 44.7348]	[38.9550, 41.0450]
	50	11.6163	6.3429	[1.7801, 8.2199]	[3.2419, 6.7581]	[0.7616, 9.2384]	[2.6857, 7.3143]
Scenario#2 (with 10 clusters)	100	15.8541	8.4076	[6.8926, 13.1074]	[8.3521, 11.6479]	[5.9096, 14.0904]	[7.8308, 12.1692]
	150	21.6239	9.5944	[11.5395, 18.4605]	[13.4646, 16.5354]	[10.4448, 19.5552]	[12.9789, 17.0211]
	200	21.7987	10.7536	[16.9789, 23.0211]	[18.5096, 21.4904]	[16.0232, 23.9768]	[18.0382, 21.9618]