An Improved Self-Organizing Diffusion Mobile Adaptive Network for Pursuing a Target
This work addresses efficiency in distributed adaptive networks for target pursuit, but it is incremental as it builds on an existing method.
The paper tackles the problem of slow convergence in self-organizing diffusion mobile adaptive networks when nodes are far from a target, by proposing modifications to the Adapt-then-Combine algorithm, resulting in improved performance as shown in simulation tests.
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.