LOFT: A Latency-Oriented Fault Tolerant Transport Protocol for Wireless Sensor-Actuator Networks
Bibliographic record
Abstract
Wireless sensor-actuator networks, or WSANs, refer to a group of sensors and actuators which collect data from the environment and perform application-specific actions in response. To act responsively and accurately, an efficient and reliable data transport protocol is crucial for the sensors to inform the actuators about the environmental events. Unfortunately, the low-power multi-hop communications in WSANs are inherently unreliable; the frequent sensor and link failures as well as the excessive delays due to congestion further aggravate the problem. In this paper, we propose a latency-oriented fault tolerant data transport protocol in WSANs. We argue that reliable data transport in such a real-time system should resist to the transmission failures, and should also consider the importance and freshness of the reported data. We articulate this argument and provide a cross-layer two-step data transport protocol for on- time and fault tolerant data delivery from sensors to actuators. Our protocol adopts smart priority scheduling that differentiates the event data of non-uniform importance. It balances the workload of sensors by checking their queue utilization and copes with node and link failures by an adaptive replication algorithm. We evaluate our protocol through extensive simulations, and the results demonstrate that it achieves the desirable reliability for WSANs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".