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This paper proposes a cross-layer optimization framework for the wireless sensor networks. In a wireless sensor network, each sensor makes a local observation of the underlying physical phenomenon and sends a quantized version of the observation to a central location via wireless links. As the sensor observations are often partial and correlated, the network performance is a complicated and nonseparable function of individual data rates at each sensor. In addition, due to the shared nature of wireless medium, nearby transmissions often interfere with each other. Thus, the traditional "bit-pipe" model for network link capacity no longer holds. This paper deals with the joint optimization of source quantization, routing, and power control in a wireless sensor network. We follow a separate source and channel coding approach and show that the overall network optimization problem can be naturally decomposed into a source coding subproblem at the application layer and a wireless power control subproblem at the physical layer. The interfaces between the layers are precisely the dual optimization variables. In addition, we introduce a novel source coding model at the application layer, which allows the efficient design of practical source quantization schemes at each sensor. Finally, we propose a dual algorithm for the overall network optimization problem. The dual algorithm, when combined with a column-generation method, allows an efficient solution for the overall network optimization problem. [ABSTRACT FROM AUTHOR]

Energy-efficient routing is a critical problem in multihop wireless networks due to the severe power constraint of wireless nodes. Despite its importance and many research efforts toward it, a distributed routing algorithm that maximizes network lifetime is still missing. To address this problem, this paper proposes a novel utility-based nonlinear optimization formulation to the maximum lifetime routing problem. Based on this formulation, a fully distributed localized routing algorithm is further presented, which is proved to converge at the optimal point, where the network lifetime is maximized. Solid theoretical analysis and simulation results are presented to validate the proposed solution. [ABSTRACT FROM AUTHOR]