An Improved Self-Organizing Diffusion Mobile Adaptive Network for Pursuing a Target

TL;DR

This paper introduces two modifications to the ATC diffusion algorithm for mobile adaptive networks, enabling faster convergence and reduced communication by using variable step sizes and selective cooperation, demonstrated through simulations.

Contribution

The paper proposes a novel distance-based variable step size adjustment and selective node cooperation to enhance ATC mobile adaptive networks.

Findings

Faster convergence to the target in simulations.

Reduced communication overhead.

Improved accuracy in target pursuit.

Abstract

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.