En este documento, una novedosa combinación de estrategias cross-layer en las capas de aplicación y de red, para manejar precisión en intervalos de entrega (y sensado) de información, manejo de latencias centrado en datos y mejoras en el manejo de energía y la robustez, en un escenario no movil, donde los datos son numéricos y cardinales, es propuesta. /Abstract In this document, a novel combination of cross-layer strategies in Application and Network layer, to address timeliness on delivered information (accuracy on sensing intervals), handle latency times on a data-centred way, and improve energy management and robustness in a non-mobile scenario where data is numerical and cardinal is proposed. The network is modelled as an Asynchronous Cellular Automata (CA) with an irregular neighbourhood, and four states. Nodes of the network are the cells of the grid in the CA, and states change depending on internal circumstances of each node and the neighbourhood. According to this model, Scheduling of sensing intervals according to data slopes, is used as a way to improve timeliness on sensing data and to �t latencies to the change rate of data. Dynamic time-based local addressing, is used to allow a reduction in the size of packet overhead without disruptively affecting network size. Neighbour updates are arranged at variable time intervals; every neighbour knows the time at which they will happen, because in the model, an asynchronous cellular automata updates individual cells independently. A new set of Cross-Layer Performance Metrics, for the evaluation of event-driven communication strategies in WSN, is presented. This set of metrics introduce numeric scores for establishing the quality of several WSN features. The scores can be used by designers to determine how well a strategy is performing on those aspects. This performance metrics, de�ne a framework for non-subjective comparison of WSN strategies, that can be used with any existing protocol to evaluate WSN features. The metrics are used in this document to establish performance of the strategies also proposed here. Results show that the POLA strategy is useful for sensing data accurately on the mean cases, without abusing of oversampling; achieving smaller Median Hop Count Modes, and significantly smaller Maximum Hop Counts; achieving smaller Mean Individual Communication Energy Expenditures on non-monotonous Datasets; distributing energy consumptions on the network in a more even way; and Maintaining a small Normalized Diference in Hop Count Mode, when losing up to 45 percent of the network. Future work is envisioned, on improving and adapting the strategies proposed in this document, to �t rescue applications, by incorporating and articulating mobility, improvements with evolutionary techniques, reconstruction of data identity information, arti�cial curiosity and security into the strategies.