Diffusion least-mean squares over adaptive networks with dynamic topologies
The purpose of this paper is to study the performance of diffusion-based distributed adaptive algorithms when relaxing the static assumption on network topology. Adaptive networks with topologies that change across time are useful to model a wide class of real-time sensor networks. This includes topologies where the number of nodes is variable and links are dynamic. We propose two schemes to accommodate dynamic topologies. The first proceeds following a probabilistic mechanism to add or remove nodes at each point in time. The second allows edges to be re-assigned at each iteration. The suggested changes allow retaining fundamental characteristics of the sensor graph, like maximum and minimum node degrees, as their significance impacts the network's throughput and its resistance to failures of neighbors. Simulations were carried for different node collaboration settings and averaged over Monte Carlo runs. Results show that no significant deterioration in performance is observed despite changes in the network size and connectivity, with a gained affinity to accommodate real sensor network systems.