A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System
Bibliographic record
Abstract
Energy Harvesting techniques are increasingly seen as the solution for freeing the wireless sensor nodes from their battery dependency. However, it remains evident that network performance features, such as network size, packet length, and duty cycle, are influenced by the sum of recovered energy. This paper proposes a new approach to defining the specifications of a stand-alone wireless node based on a Radio-frequency Energy Harvesting System (REHS). To achieve adequate performance regarding the range of the Wireless Sensor Network (WSN), techniques for minimizing the energy consumed by the sensor node are combined with methods for optimizing the performance of the REHS. For more rigor in the design of the autonomous node, a comprehensive energy model of the node in a wireless network is established. For an equitable distribution of network charges between the different nodes that compose it, the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for this purpose. The model considers five energy-consumption sources, most of which are ignored in recently used models. By using the hardware parameters of commercial off-the-shelf components (Mica2 Motes and CC2520 of Texas Instruments), the energy requirement of a sensor node is quantified. A miniature REHS based on a judicious choice of rectifying diodes is then designed and developed to achieve optimal performance in the Industrial Scientific and Medical (ISM) band centralized at 2.45 GHz . Due to the mismatch between the REHS and the antenna, a band pass filter is designed to reduce reflection losses. A gradient method search is used to optimize the output characteristics of the adapted REHS. At 1 mW of input RF power, the REHS provides an output DC power of 0.57 mW and a comparison with the energy requirement of the node allows the Base Station (BS) to be located at 310 m from the wireless nodes when the Wireless Sensor Network (WSN) has 100 nodes evenly spread over an area of 300 × 300 m 2 and when each round lasts 10 min . The result shows that the range of the autonomous WSN increases when the controlled physical phenomenon varies very slowly. Having taken into account all the dissipation sources coexisting in a sensor node and using actual measurements of an REHS, this work provides the guidelines for the design of autonomous nodes based on REHS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".