<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In many outdoor locations solar power provides the greatest power densities for energy harvesting to power wireless sensor networks in comparison to other practical alternative such as wind, vibrations or temperature gradients. Since solar power is highly variable with location and time, it is necessary to optimise the sensor nodes for individual locations. Presented here, is an assessment of the solar power availability in Manchester, UK (53°28′N, 2°14′W). Wireless sensor nodes are typically low power devices with intended perpetual operation and thus the temporal distribution of available power is important together with the total amount of energy drawn over a given time period. Here we examine direct and diffuse solar radiation data over a period of three years and present methods for the deployment of solar cells for sensor nodes to optimise sensing and communication scenarios. As local weather conditions are highly variable and stochastic in the medium term, we base the future node performance on the weather from a previous year. From analysis of the weather data, the hardware requirements for the sensor node are then made from the power consumption of the sensor node for sensing, sleep and data transmission. It was found that to maximise the time over which the solar irradiance exceeds that required to power our demonstration sensor node, the solar cell should be positioned horizontally.</p></abstract>

In many outdoor locations solar power provides the greatest power densities for energy harvesting to power wireless sensor networks in comparison to other practical alternative such as wind, vibrations or temperature gradients. Since solar power is highly variable with location and time, it is necessary to optimise the sensor nodes for individual locations. Presented here, is an assessment of the solar power availability in Manchester, UK (53°28′N, 2°14′W). Wireless sensor nodes are typically low power devices with intended perpetual operation and thus the temporal distribution of available power is important together with the total amount of energy drawn over a given time period. Here we examine direct and diffuse solar radiation data over a period of three years and present methods for the deployment of solar cells for sensor nodes to optimise sensing and communication scenarios. As local weather conditions are highly variable and stochastic in the medium term, we base the future node performance on the weather from a previous year. From analysis of the weather data, the hardware requirements for the sensor node are then made from the power consumption of the sensor node for sensing, sleep and data transmission. It was found that to maximise the time over which the solar irradiance exceeds that required to power our demonstration sensor node, the solar cell should be positioned horizontally.

Optimisation of solar power intake for wireless sensor networks at temperate latitudes

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In many outdoor locations solar power provides the greatest power densities for energy harvesting to power wireless sensor networks in comparison to other...

Optimisation of solar power intake for wireless sensor networks at temperate latitudes

Published Online: Feb 15, 2020

Page range: 1 - 5

DOI: https://doi.org/10.21307/ijssis-2019-089

Keywords
wireless sensor networks, energy harvesting, energy budgeting, solar power

© 2019 Authors., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Optimisation of solar power intake for wireless sensor networks at temperate latitudes

Published Online: Feb 15, 2020

Page range: 1 - 5

DOI: https://doi.org/10.21307/ijssis-2019-089

Keywordswireless sensor networks, energy harvesting, energy budgeting, solar power

© 2019 Authors., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
wireless sensor networks, energy harvesting, energy budgeting, solar power