The objective of simultaneous resource allocation and route optimization problem is to overlay a network of communication resources on an existing network of sensors (or sites); to facilitate the flow of information packets, originating at each sensor, to a destination center (or central processing unit) such that the total cost of communication is minimized. This is an NP-hard problem and the associated cost function is riddled with multiple poor local minima even for fixed locations of sensors and destination center. In the case when the sensors and the destination center have associated dynamics, determining resource location and routing dynamics adds significantly to the complexity. Here we propose a framework that uses the Maximum-Entropy-Principle and a smooth approximation to the total communication cost as a Lyapunov function, to solve the simultaneous resource allocation and route optimization problem in a dynamic setting. Simulation results demonstrate that the proposed algorithm outperforms the frame-by-frame approach with regards to the practical feasibility of the resource dynamics apart from saving heavily on the computational expense involved in the latter.