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.

The ranging algorithm allows active stations to measure their distances to the headend for synchronization purpose in Hybrid Fiber Coax (HFC) networks. A practicable mechanism to resolve contention among numerous stations is to randomly delay the transmission of their control messages. Since shorter contention cycle time increases slot throughput, this study develops three mechanisms, fixed random delay, variable random delay, and optimal random delay, to minimize the contention cycle time. Simulation demonstrates that the optimal random delay effectively minimizes the contention cycle time and approaches the theoretical optimum throughput of 0.18 from pure ALOHA. Furthermore, over-estimation reduces the impact on contention cycle time more than under-estimation through sensitivity analysis, and both phenomenon damage slot throughput. Two estimation schemes, maximum likelihood and average likelihood, are thereby presented to estimate the number of active stations for each contention resolution round. Simulation proofs that the proposed estimation schemes are effective even when the estimated number of active stations in initial contention round is inaccurate.