  In this letter we focus on designing self-organizing diffusion mobile
adaptive networks where the individual agents are allowed to move in pursuit of
an objective (target). The well-known Adapt-then-Combine (ATC) algorithm is
already available in the literature as a useful distributed diffusion-based
adaptive learning network. However, in the ATC diffusion algorithm, fixed step
sizes are used in the update equations for velocity vectors and location
vectors. When the nodes are too far away from the target, such strategies may
require large number of iterations to reach the target. To address this issue,
in this paper we suggest two modifications on the ATC mobile adaptive network
to improve its performance. The proposed modifications include (i)
distance-based variable step size adjustment at diffusion algorithms to update
velocity vectors and location vectors (ii) to use a selective cooperation, by
choosing the best nodes at each iteration to reduce the number of
communications. The performance of the proposed algorithm is evaluated by
simulation tests where the obtained results show the superior performance of
the proposed algorithm in comparison with the available ATC mobile adaptive
network.
