Difference between revisions of "SCOTT:BB25.E"
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{{Building Block | {{Building Block | ||
|Title=Improved energy harvesting | |Title=Improved energy harvesting | ||
− | |Page Title= | + | |Page Title=BB25.E Improved energy harvesting |
|Technology Line=Autonomy of Devices/Energy Efficiency of WSN | |Technology Line=Autonomy of Devices/Energy Efficiency of WSN | ||
|Lead partner=Acciona | |Lead partner=Acciona |
Revision as of 18:15, 11 December 2017
Title | Improved energy harvesting |
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Page Title | BB25.E Improved energy harvesting |
Technology Line | Autonomy of Devices/Energy Efficiency of WSN |
Lead partner | Acciona |
Leader | Rafael Socorro |
Contributors | Acciona, Tecnalia |
Related to Use Cases | SCOTT:WP7, SCOTT:WP9, SCOTT:WP12, SCOTT:WP18, SCOTT:WP17 |
Description | * Synergistic analysis of piezoelectric, solar and thermal (heat sinks) energy harvesting and associated electronic for optimized energy harvesting systems: to optimize a energy harvesting system, both the energy production and electrical (hardware) aspect of the design must be balanced and improved.
A. Hierarchical sensing techniques assume that multiple sensors are installed on the sensor nodes, each characterized by its own accuracy and power consumption, to measure the same physical quantity. In most cases, simple sensors are energy-efficient, but provide a very limited resolution. On the other hand, complex sensors can give a more accurate characterization of the sensed phenomenon at the cost of higher energy consumption. Thus, accuracy can be traded off with energy efficiency. At first, low-power sensors are considered to provide a coarse-grained characterization of the sensing field or trigger an event. Then, accurate, but power hungry, sensors can be activated with measurements used to improve the coarser description.
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).
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. |
Main output | * A Dependable Wireless Sensor Node with Harvesting energy for Harsh Environment.
Energy harvesting technology that is mature and meet the requirements of of power of the nodes. |
BB category | Methodology (for SW/HW development), SW component, HW component |
Baseline | The BB starts from ... |
Current TRL | 4 |
Target TRL | 6 |