Self Sustainable Cognitive Wireless Sensor Networks with RF Energy Harvesting
Bibliographic record
Abstract
Cognitive Wireless Sensor Network (CWSN)’s offer a solution to the capacity problem for the next generation Beyond 5G (B5G) Wireless Sensor Network (WSN)’s. Secondary User (SU)’s access the Primary User (PU) network by finding unused spectrum and opportunistically accessing it, thereby increasing system capacity. This requires signal detection, analysis & decision making and spectrum exploitation techniques in order for the CWSN to work effectively. As a result, two acute problematic issues in a CWSN are: signal detection and power loss. The focus of this thesis is on efficiently solving the power loss problem, thereby creating a self sustainable CWSN. Harvesting energy from the ambient Radio Frequency (RF) signal has been shown as a viable solution to the power loss problem. Various schemes have been proposed including opportunistic and cooperative RF Wireless Energy Harvesting (RFWEH). Unfortunately, many of these models require RFWEH from low power sources, which is shown in this dissertation to be unsustainable in the long haul. This issue is thoroughly investigated and a novel hybrid RFWEH architecture for CWSNs is proposed. Simulation results show that the proposed model after harvesting energy from high power devices, such as TV and radio towers, results in energy sustainability. This is in sharp contrast to existing work, wherein nodes die out eventually and the network collapses. Furthermore, changing the RFWEH architecture to include a power switch, where individual nodes change RF energy sources, increases system performance by up to 72%. The design of sustainable MAC protocols for CWSNs using RFWEH is also proposed. These include a distributed, centralized and mobility-aware MAC. Simulation results show that the distributed MAC has a 90% node survival rate while keeping network parameters such as throughput, delay and packet loss within acceptable target limits. The centralized MAC outperforms the distributed MAC by up to 40% and the mobility-aware MAC has a 30-80% node survival rate, depending upon speed of individual nodes. Simulation results show that both the stationary (distributed and centralized) and mobile CWSN’s are self sustainable. To the best of our knowledge, this is a unique result as no comparable self-sustainable designs for harvesting energy from high power devices have been found in literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".