Wireless Sensor Networks (WSNs) are composed of small wireless nodes equipped with sensors, a processor, and a radio\r\ncommunication unit, all normally powered by batteries. For most WSN applications, the network is expected to function for\r\nseveral months or years. In the common monitoring application scenario, adjacent nodes in aWSN often sense spatially correlated\r\ndata. Suppressing this correlation can significantly improve the lifetime of the network. The maximum possible network data\r\ncompression can be achieved using distributed source coding (DSC) techniques when nodes encode at Slepian-Wolf rates. This\r\npaper presents contributions to the lifetime optimization problem of WSNs in the form of two algorithms: the Updated-CMAX\r\n(UCMAX) power-aware routing algorithm to optimize the routing tree and the Rate Optimization (RO) algorithm to optimize\r\nthe encoding rates of the nodes. The two algorithms combined offer a solution that maximizes the lifetime of a WSN measuring\r\nspatially correlated data. Simulations show that our proposed approach may significantly extend the lifetime of multihop WSNs\r\nwith nodes that are observing correlated data.
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