1. A variable step-size diffusion least mean square method for estimation in adaptive networks, comprising the steps of: (a) establishing an adaptive network having N nodes, where N is an integer greater than one, and for each node k, a number of neighbors of node k is given by N k , including the node k, where k is an integer between one and N; (b) establishing an integer i and initially setting i=1; (c) calculating an output of the adaptive network at each node k as d k (i)=u k,i w 0 +v k (i), where u k,i represents a known regressor row vector of length M, w 0 represents an unknown column vector of length M and v k (i) represents noise in the adaptive network, where M is an integer; (d) calculating an error value e k (i) at each node k as e=d k (i)=d k (i)−u k,i y k,i-1 , where y k,i represents an estimate of an output vector for each node k at iteration i; (e) calculating a node step size μ k for each node k as μ k ( i ) = αμ k ( i - 1 ) + γ ∑ l ∈ N k b lk e l 2 ( i ) , where α and γ are unitless, selectable parameters, l is an integer, and b lk is a combiner weight for sensed data shared by neighbor nodes of node k; (f) calculating a local estimate for each node neighboring node k, f k,i , for each node k as f k , i = y k , i - 1 + μ k ( i ) ∑ l ∈ N k b lk u l T ( i ) e l ( i ) ; (g) calculating the estimate of the output vector y k,i for each node k as y k , i = ∑ l = 1 N k c Ik f l , i , where c lk is a combiner coefficient equal to 1 max (  k ,  l ) when nodes k and l are linked and k does not equal l, c lk is equal to 0 when nodes k and l are not linked, and c lk is equal to 1 - ∑ l ∈  k / { k } c kl when k equals l; (h) if e k (i) is greater than a selected error threshold, then setting i=i+1 and returning to step (c); otherwise, (i) defining a set of output vectors y k for each node k, where y k =y k,i ; and (j) storing the set of output vectors in computer readable memory.