Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. This calls for the efficient and effective data-centric routing algorithm to facilitate this advantage. In the first part of this paper, we model the data-centric routing problem by rigorous mixed integer and linear mathematical formulation, where the objective function is to minimize the total transmission cost subject to multicast tree constraints. With the advancement of sensor network technology, sensor nodes with configurable transmission radius capability could further reduce energy consumption. The second part of this paper considers the transmission radius assignment of each sensor node and the data-centric routing assignment jointly. The objective function is to minimize the total power consumption together with consideration of construction of a data aggregation tree and sensor node transmission radius assignment. The solution approach is based on Lagrangean relaxation in conjunction with the novel optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms calculate better solution than other existing heuristics with improvement ratio up to 169% and 59% with respect to fixed transmission radius and configurable transmission radius for network with 300 random generated nodes.

An essential issue in designing, operating and managing a modern network is to assure end-to-end QoS from users perspective, and in the meantime to optimize a certain average performance objective from the systems perspective. So in the first part of this paper, we address the above issue by using the rerouting approach, where the objective is to minimize the average cross-network packet delay in virtual circuit networks with the consideration of an end-to-end delay constraint (DCR) for each O-D pair. The problem is formulated as a multicommodity network flow problem with integer routing decision variables, where additional end-to-end delay constraints are considered. As the traffic demands increases over time, the rerouting approach may not be applicable, which results in the necessity of capacity augmentation. Henceforth, the second part of this paper is to jointly consider the link capacity assignment and the routing problem (JCR) at the same time where the objective is to minimize the total link installation cost with considering the average and end-to-end delay constraints. Unlike previous research tackling this problem with a two-phase approach, we propose an integrated approach to considering the routing and capacity assignment at the same time. The difficulties of DCR and JCR result from the integrality nature and particularly the nonconvexity property associated with the end-to-end delay constraints. We propose novel Lagrangean relaxation based algorithms to solve the DCR and the JCR problems. Through computational experiments, we show that the proposed algorithms calculate near-optimal solutions for the DCR problem and outperform previous two-phase approach for the JCR problem under all tested cases.