Localized Energy-Aware Fault Management in Relay Based Sensor Networks
Bibliographic record
Abstract
In a relay based two-tier sensor network the individual sensor nodes, constituting the lower tier of the network, are partitioned into clusters and transmit their data directly to their respective cluster heads. Higher-powered relay nodes act as cluster heads and form the upper tier of the sensor network, that communicates received data to the base station. In this model, if a relay node becomes faulty, all data from the cluster of the faulty node, as well as data from other clusters that is routed through the faulty node will be lost. To avoid this potentially large data loss, it is critical that, after a failure is detected, data is rerouted as quickly as possible to avoid the failed relay node. This is best achieved by allowing each individual relay node to take corrective action immediately after discovering that a relay node is faulty, without any directive from the base station. Furthermore, it is also important to ensure that the energy dissipation of the relay nodes, using the new route, is as low as possible. In this paper, we propose a novel approach, where only the nodes originally transmitting to the failed node has to change their routing information, and all other nodes continue to operate as before. This greatly simplifies the coordination and synchronization required to establish a new routing scheme, resulting in a faster response time and reduced data loss. We have proposed an integer linear program (ILP) formulation that determines a routing scheme using this approach. We also present a simple but effective heuristic, capable of handling much larger networks. Our simulation results clearly demonstrate that the proposed approach can lead to significant improvements in the lifetime of a two tier network, compared to existing techniques.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".