Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

New applications based on wireless sensor networks (WSN), such as person-locator services, 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 paper, we propose a temporal alignment model for data streams that identifies temporal relationships and which does not require the use of synchronized clocks, global references, centralized schemes, or additional synchronization signals. The identification of temporal relationships without the use of synchronized clocks is achieved by translating temporal dependencies based on a time-line to causal dependencies among streams. Finally, we show the viability and the effectiveness of the model by simulating it over a sensor network with multihop communication.