Adaptive Algorithms for Batteryless LoRa-Based Sensors
Ambient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery maintenance, last longer and have greater weatherproofing properties due to the lack of a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/14/6568 |
| _version_ | 1797587541468119040 |
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| author | Fabrizio Giuliano Antonino Pagano Daniele Croce Gianpaolo Vitale Ilenia Tinnirello |
| author_facet | Fabrizio Giuliano Antonino Pagano Daniele Croce Gianpaolo Vitale Ilenia Tinnirello |
| author_sort | Fabrizio Giuliano |
| collection | DOAJ |
| description | Ambient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery maintenance, last longer and have greater weatherproofing properties due to the lack of a battery access panel. In this work, we study adaptive transmission algorithms to improve the performance of batteryless IoT sensors based on the LoRa protocol. First, we characterize the device power consumption during sensor measurement and/or transmission events. Then, we consider different scenarios and dynamically tune the most critical network parameters, such as inter-packet transmission time, data redundancy and packet size, to optimize the operation of the device. We design appropriate capacity-based storage, considering a renewable energy source (e.g., photovoltaic panel), and we analyze the probability of energy failures by exploiting both theoretical models and real energy traces. The results can be used as feedback to re-design the device to have an appropriate amount energy storage and meet certain reliability constraints. Finally, a cost analysis is also provided for the energy characteristics of our system, taking into account the dimensioning of both the capacitor and solar panel. |
| first_indexed | 2024-03-11T00:40:20Z |
| format | Article |
| id | doaj.art-b3ade3fdc5a44194adb53a1775e6a8c2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:20Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b3ade3fdc5a44194adb53a1775e6a8c22023-11-18T21:19:32ZengMDPI AGSensors1424-82202023-07-012314656810.3390/s23146568Adaptive Algorithms for Batteryless LoRa-Based SensorsFabrizio Giuliano0Antonino Pagano1Daniele Croce2Gianpaolo Vitale3Ilenia Tinnirello4Department of Engineering, University of Palermo, 90128 Palermo, ItalyDepartment of Engineering, University of Palermo, 90128 Palermo, ItalyDepartment of Engineering, University of Palermo, 90128 Palermo, ItalyInstitute for High Performance Computing and Networking, National Research Council (CNR), 90146 Palermo, ItalyDepartment of Engineering, University of Palermo, 90128 Palermo, ItalyAmbient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery maintenance, last longer and have greater weatherproofing properties due to the lack of a battery access panel. In this work, we study adaptive transmission algorithms to improve the performance of batteryless IoT sensors based on the LoRa protocol. First, we characterize the device power consumption during sensor measurement and/or transmission events. Then, we consider different scenarios and dynamically tune the most critical network parameters, such as inter-packet transmission time, data redundancy and packet size, to optimize the operation of the device. We design appropriate capacity-based storage, considering a renewable energy source (e.g., photovoltaic panel), and we analyze the probability of energy failures by exploiting both theoretical models and real energy traces. The results can be used as feedback to re-design the device to have an appropriate amount energy storage and meet certain reliability constraints. Finally, a cost analysis is also provided for the energy characteristics of our system, taking into account the dimensioning of both the capacitor and solar panel.https://www.mdpi.com/1424-8220/23/14/6568adaptive algorithmsbatterylessenergy harvestinginternet of thingsLoRawireless sensor networks |
| spellingShingle | Fabrizio Giuliano Antonino Pagano Daniele Croce Gianpaolo Vitale Ilenia Tinnirello Adaptive Algorithms for Batteryless LoRa-Based Sensors Sensors adaptive algorithms batteryless energy harvesting internet of things LoRa wireless sensor networks |
| title | Adaptive Algorithms for Batteryless LoRa-Based Sensors |
| title_full | Adaptive Algorithms for Batteryless LoRa-Based Sensors |
| title_fullStr | Adaptive Algorithms for Batteryless LoRa-Based Sensors |
| title_full_unstemmed | Adaptive Algorithms for Batteryless LoRa-Based Sensors |
| title_short | Adaptive Algorithms for Batteryless LoRa-Based Sensors |
| title_sort | adaptive algorithms for batteryless lora based sensors |
| topic | adaptive algorithms batteryless energy harvesting internet of things LoRa wireless sensor networks |
| url | https://www.mdpi.com/1424-8220/23/14/6568 |
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