A convex optimization method for autonomous self-organization in dynamic wireless networks

In our attempts to model, characterize and control increasingly complex network systems we introduced a novel physics framework in which communication networks are modeled as physical systems that react to local forces exerted on network nodes. We showed that under clear atmosphere conditions the network communication energy can be modeled as the potential energy of an analogous spring system, which led to the development of distributed mobility control algorithms where nodes react to local forces exerted by neighbor nodes driving the network to energy minimizing configurations. This paper extends our previous work by including the effects of atmospheric attenuation in the channel. We show how our new formulation still results in a convex energy minimization problem. Accordingly, an updated force-driven mobility control algorithm is presented. Exponential forces are shown to appear on backbone nodes when atmospheric attenuation is present, which make them stay closer to each other to optimize backbone connectivity and reduce the network power usage. We present results in terms of power usage, network coverage and backbone connectivity and show how our updated mobility control algorithm allows the network to react to the effects of changing channel conditions.