On Reliable Broadcast in Low Duty-Cycle Wireless Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Broadcast is one of the most fundamental services in wireless sensor networks (WSNs). It facilitates sensor nodes to propagate messages across the whole network, serving a wide range of higher level operations and thus being critical to the overall network design. A distinct feature of WSNs is that many nodes alternate between active and dormant states, so as to conserve energy and extend the network lifetime. Unfortunately, the impact of such cycles has been largely ignored in existing broadcast implementations that adopt the common assumption of all nodes being active all over the time. In this paper, we revisit the broadcast problem with active/dormant cycles. We show strong evidence that conventional broadcast approaches will suffer from severe performance degradation, and, under low duty cycles, they could easily fail to cover the whole network in an acceptable time frame. To this end, we remodel the broadcast problem in this new context, seeking a balance between efficiency and latency with coverage guarantees. We demonstrate that this problem can be translated into a graph equivalence, and develop a centralized optimal solution. It provides a valuable benchmark for assessing diverse duty-cycle-aware broadcast strategies. We then extend it to an efficient and scalable distributed implementation, which relies on local information and operations only, with built-in loss compensation mechanisms. The performance of our solution is evaluated under diverse network configurations. The results suggest that our distributed solution is close to the lower bounds of both time and forwarding costs, and it well resists to the wireless loss with good scalability on the network size and density. In addition, it enables flexible control toward the quality of broadcast coverage.
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.
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 | 0.000 | 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 | 0.001 |
| 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 it