The prodigious amount of data movement among sources, data centers, or processing elements precludes the utilization of least-squares (LS) and fusion-center (FC)-based modeling and control. The LS methods are offline in nature, hence may face difficulty in real-time implementation. FC-based methods that are nonrobust due to single point failure, require large communication bandwidth and computationally fast processing unit. To curb these limitations, this article identifies distributed parameter systems by estimating the parameters of spatio-temporal Volterra model using in-network data processing. It can handle the immense volume of data by distributing the processing tasks of FC among the wireless sensor network nodes. To facilitate distributed optimization, the global objective function is reformulated as a multiple constrained separable problem which is then decomposed into augmented Lagrangian form. Then, alternating direction method of multipliers along with coordinate descent method is employed to obtain the global optimal solution collaboratively. Further, a communication-efficient algorithm is designed for the proposed approach to deploy in an ad-hoc network. Simulations are carried out on two industrial distributed parameter systems (catalytic rod and tubular reactor) to illustrate the practicality of the proposed algorithm. © 2005-2012 IEEE.