BB25.E Improved energy harvesting

BB25.E
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Title	Improved energy harvesting
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. Power management Unit: a power management sub- system,“energy harvesting module, which performs the necessary DC-DC conversions while providing maximum power point (MPP) tracking, choosing the highest voltage level from the energy sources provided in the system, controlling the power consumption of the device (with information about the current harvesting conditions) Software techniques to reduce the energy consumption of low- power devices, Adaptive sensing by exploiting three different approaches: hierarchical sensing, adaptive sampling and model- based active sensing. 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. 1) Triggered sensing: The activation of the more accurate and power-consuming sensors after the low-resolution ones, after some activity within the sensed area has been detected, is referred to triggered sensing. 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.
Main output	* 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.
BB category	Methodology (for SW/HW development), SW component, HW component
Baseline	The BB starts from ...
Current TRL	4
Target TRL	6

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