Middleware for Exchange and Validation of Context Data and Information

Preden, Jürgo-Sören; Llinas, James; Motus, Leo

doi:10.1007/978-3-319-28971-7_9

Cited by 2 publications

(2 citation statements)

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“…This section discusses the importance of temporal validity intervals of input data for fusion node and how the value of validity interval of sensor readings affects the selection of temporally compatible inputs for the data fusion in WSN. The necessity of checking and ensuring the sensor data validity has been discussed in our earlier papers, 4,35 where it has been explained how every sensor reading has temporal and spatial validity intervals associated with it. These intervals depend on several aspects, for example, the validity area depends on the location of the WSN and on the properties of the phenomenon being observed, while the temporal validity interval depends both on the properties of the environment where the node is located and on the phenomenon being observed.…”

Section: Modelling Data Streams and Time-selective Data Fusion In Wsnmentioning

confidence: 99%

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Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

Kaugerand

Ehala

Motus

et al. 2018

International Journal of Distributed Sensor Networks

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This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

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Section: Modelling Data Streams and Time-selective Data Fusion In Wsnmentioning

confidence: 99%

“…where t valid is a length of validity interval on fusion node’s time axis. 35 In case the fusion node receives input data from different sources, all the data must be valid at their arrival.…”

Section: Modelling Data Streams and Time-selective Data Fusion In Wsnmentioning

confidence: 99%