Quality of Information Maximization in Lifetime-Constrained Wireless Sensor Networks

In this paper, we investigate the quality of information (QoI) maximization problem by jointly optimizing the data rate and transmit power in lifetime-constrained wireless sensor networks. The QoI at the sink node is characterized by the virtue of the network utility, which quantifies the aggregated value of the data gathered from different sensor nodes. Then, a network utility maximization (NUM) problem is formulated to maximize the QoI subject to the constraints of the network lifetime and the link capacity. To avoid oscillation among optimal solutions resulted from the usage of multipath routing, the NUM problem is converted into an equivalent problem by exploiting the proximal optimization approach. Correspondingly, the transformed problem can be solved by the proposed proximal approximation-based resource allocation algorithm (PARA), which has good features of fast convergence and low complexity. Moreover, we develop a successive convex approximation-based algorithm (SCAA) to settle a nonlinear nonconvex difference of convex functions programming in the PARA. Simulation results demonstrate the advantages of the proposed algorithms.