Name
Abstract | ||
|---|---|---|
Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension for its components, an effective method for the comparison of system implementations is required. System simulations have the capability to accomplish this in an accurate and efficient manner. In this paper, we evaluate the existing work on solar energy harvesting architectures and common methods for their modeling. An analysis of the existing approaches demonstrates a mismatch between the requirement of the task to be both accurate and efficient and the proposed modeling methods, which are either accurate or efficient. As a result, we propose a data-driven modeling method based on artificial neural networks for further evaluation by the research community. Preliminary results of an initial investigation demonstrate the capability of this method to accurately capture the behavior of a solar energy harvesting architecture, while providing a time-efficient model generation procedure based on system-level data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3390/jsan3030207 | JOURNAL OF SENSOR AND ACTUATOR NETWORKS |
Keywords | Field | DocType |
wireless sensor networks, sensor node lifetime, solar energy harvesting, modeling, simulation, system dimensioning | Key distribution in wireless sensor networks,Architecture,Wireless,Effective method,Computer science,Implementation,Real-time computing,Solar energy harvesting,Artificial neural network,Wireless sensor network | Journal |
Volume | Issue | Citations |
3 | 3 | 2 |
PageRank | References | Authors |
0.36 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Bader | 1 | 4 | 5.73 |
| Xinyu Ma | 2 | 2 | 1.03 |
| B Oelmann | 3 | 77 | 21.78 |