Design and Analysis of Uplink Transmission Performance Enhancement Methods for Data Collection in Internet-of-Things Networks
Bibliographic record
Abstract
With the increasing demands for the services provided by the Internet-of-Things (IoT) networks, tremendous efforts have been dedicated to enhance the performance of machine-to-machine (M2M) communications. However, due to the limited spectrum resources available for IoT networks, the uplink transmissions that are commonly used for data collection suffer performance degradation when the traffic load increases. To improve the uplink network performance under high traffic load, we propose new enhancement methods for the IoT networks with both single-hop and multi-hop configurations.Specifically, for single-hop networks, this thesis addresses three research objectives. First, successive interference cancellation (SIC) is implemented on top of the pure Aloha (PA) medium access control (MAC) mechanism. The problem is to perform the SIC under an unsynchronized packet transmission framework, and without introducing extra signaling overhead. To this end, a window-based SIC algorithm is presented for the network’s single gateway (GW). Second, in order to evaluate the performance of the SIC-based PA, a throughput model is developed and analyzed to study both the throughput and the packet delivery ratio (PDR) performance metrics. Third, the problem of enabling the SIC-based PA in an IoT network with multiple GWs is solved. The SIC algorithm for PA is redesigned to accommodate single-hop multi-GW networks. A throughput model is also proposed for the newly designed PA-based SIC in multi-GW networks. For the multi-hop IoT networks, the main research objective in this thesis is to allocate proper bandwidth for the nodes in the mesh networks. As a solution, a new distributed bandwidth allocation algorithm is designed. The proposed new design significantly improves the mesh network’s uplink transmission performance at high traffic load. Meanwhile, the new algorithm does not require the configuration of the hysteresis quantum, which makes it more practical than the current state-of-the-art distributed bandwidth allocation algorithms. The performance evaluation results obtained for both the single-hop and multi-hop IoT networks indicate that the proposed enhancement methods can significantly improve the uplink PDR, throughput, and latency for high traffic load scenarios.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".