Difference between revisions of "SCOTT:BB25.E"

B. Adaptive sampling techniques are aimed at dynamically adapting the sensor sampling rate by exploiting spatial and/or temporal correlation among acquired data (activity-driven adaptive sampling) and/or the available energy whenever the sensor node is able to harvest energy from the environment (harvesting-aware adaptive sampling).

1) Activity-driven adaptive sampling: Activity-driven adaptive sampling exploits the correlation (both temporal and spatial) among the acquired data.

2) Harvesting-aware adaptive sampling: The harvestingaware adaptive sampling techniques exploit knowledge about the residual and the forecasted energy coming from the harvester module to optimize power consumption at the unit level. The approach requires development of models able to characterize the evolution over time of energy availability and the energy consumption of sensor units.

Model-based active sampling consists of building a model of the sensed phenomenon on top of an initial set of sampled data. Once the model is available, next data can be predicted by the model instead of sampling the quantity of interest, hence saving the energy consumed for data sensing. Whenever the requested accuracy is not satisfied anymore, the model needs to be updated, or re-estimated, to adhere to the new dynamics of the physical phenomenon under observation."2) Multi-scale sensing: A different use of hierarchical sensing consists of identifying areas within the monitoring field that require a more accurate observation. This is obtained by relying on a coarse-grained description of the field with lower accuracy sensors and activating additional high- resolution ones only in areas where their accurate acquisitions are requested.

B. Adaptive sampling techniques are aimed at dynamically adapting the sensor sampling rate by exploiting spatial and/or temporal correlation among acquired data (activity-driven adaptive sampling) and/or the available energy whenever the sensor node is able to harvest energy from the environment (harvesting-aware adaptive sampling).

1) Activity-driven adaptive sampling: Activity-driven adaptive sampling exploits the correlation (both temporal and spatial) among the acquired data.

2) Harvesting-aware adaptive sampling: The harvestingaware adaptive sampling techniques exploit knowledge about the residual and the forecasted energy coming from the harvester module to optimize power consumption at the unit level. The approach requires development of models able to characterize the evolution over time of energy availability and the energy consumption of sensor units.

Model-based active sampling consists of building a model of the sensed phenomenon on top of an initial set of sampled data. Once the model is available, next data can be predicted by the model instead of sampling the quantity of interest, hence saving the energy consumed for data sensing. Whenever the requested accuracy is not satisfied anymore, the model needs to be updated, or re-estimated, to adhere to the new dynamics of the physical phenomenon under observation." cannot be used as a page name in this wiki.

Combining Energy Harvesting transducers, an Energy Processing Power Module, low power sensor, an energy aware Microcontroller, link delivers the reality of long life, maintenance- free Zero Power Wireless Sensor Networks in Rail domain.

Energy harvesting technology that is mature and meet the requirements of of power of the nodes."A Dependable Wireless Sensor Node with Harvesting energy for Harsh Environment.

Combining Energy Harvesting transducers, an Energy Processing Power Module, low power sensor, an energy aware Microcontroller, link delivers the reality of long life, maintenance- free Zero Power Wireless Sensor Networks in Rail domain.

Energy harvesting technology that is mature and meet the requirements of of power of the nodes." cannot be used as a page name in this wiki.