New applications based on wireless sensor networks (WSN), harvest a large amount of data streams that are simultaneously generated by multiple distributed sources. Specifically, in a WSN this paradigm of data generation/transmission is known as event-streaming. In order to be useful, all the collected data must be aligned so that it can be fused at a later phase. To perform such alignment, the sensors need to agree on common temporal references. Unfortunately, this agreement is difficult to achieve mainly due to the lack of perfectly-synchronized physical clocks and the asynchronous nature of the execution. Some solutions tackle the issue of the temporal alignment; however, they demand extra resources to the network deployment since they try to impose global references by using a centralized scheme. In this dissertation, we propose a distributed mechanism that performs at runtime the stream data alignment without requiring the use of synchronized clocks, additional signals or centralized schemes. This is achieved by translating temporal dependencies based on a time-line to causal dependencies among streams. In addition, we propose a spatio-temporal data association approach that extends the data alignment mechanism to include spatial information to perform the data alignment by considering the “closeness ” among streams. To achieve this, the approach makes use of a fuzzy-causal relation defined to relate the space domain with the logical/temporal domain. For our case by establishing a fuzzy-causal relation we determine “how long ago” an event happened before another event.