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In this work, we model the sensor networks as an unsupervised learning and clustering process. We classify nodes according to its static distribution to form known class densities (CCPD). These densities are chosen from specific cross-layer features which maximizes lifetime of power-aware routing algorithms. To circumvent computational complexities of a power-ware communication STACK we introduce path-loss models at the nodes only for high density deployments. We study the cluster heads and formulate the data handling capacity for an expected deployment and use localized probability models to fuse the data with its side information before transmission. So each cluster head has a unique P^sub max^ [10] but not all cluster heads have the same measured value. If the cluster size in n, from the cluster then the first order entropy of data aggregation is ... In a lossless mode if there are no faults in the sensor network then we can show that the highest probability given by P^sub max^ is ambiguous if its frequency is ... otherwise it can be determined by a local function. We further show that the event detection at the cluster heads can be modelled with a pattern ^sup 2^sup m^^ and ^sup m^ , the number of bits can be a correlated pattern of 2 bits and for a tight lower bound we use 3-bit Huffman codes which have entropy of ... These local algorithms are further studied to optimize on power, fault detection and to maximize on the distributed routing algorithm used at the higher layers. From these bounds in large network, it is observed that the power dissipation is network size invariant. The performance of the routing algorithms solely based on success of finding healthy nodes in a large distribution. It is also observed that if the network size is kept constant and the density of the nodes is kept closer then the local pathloss model effects the performance of the routing algorithms. We also obtain the maximum intensity of transmitting nodes for a given category of routing algorithms for an outage constraint, i.e., the lifetime of sensor network. [PUBLICATION ABSTRACT]