Self-Healing Wireless Sensor Networks: Results That May Surprise
Bibliographic record
Abstract
According to a recent DARPA announcement, self-healing - the ability of a network to effectively combat coverage and routing holes and network disconnection - represents one of the most desired operational properties of wireless sensor networks (WSNs) for military applications. Although previously considered in the WSN literature, the concept of network self- healing, and specifically self-healing by means of mobile nodes, still remains a greatly understudied research area. hi this paper, by focusing on one particular type of holes - routing holes, the energy aspect of combating these holes through the deployment of a single mobile (super) node is discussed. The specific contributions of the paper include: 1) It is proven that although bridging a routing hole by means of a mobile node may seems very intuitive, the deployment of the mobile is often hard to formally justify. For instance, the use of the mobile turns out to be completely energy unjustifiable in all circle- and square- like shaped holes, regardless of their actual size or number of boundary nodes actively involved in routing. Accordingly, the need to consider other parameters, such as overall transmission delay or static-node failure, when deciding whether/where to deploy the mobile, is demonstrated. 2) Building on the results of 1), we propose OPlaMoN - a simple distributed algorithm for determining the Optimal Placement of a Mobile Node within a routing hole of any arbitrary topology. As the name implies, the algorithm solves a rather complex optimization problem by breaking it into smaller fragments which are, then, partially solved by individual nodes. The final solution is reached through a cooperative decision-making process, assuming a minimum exchange of information among the effected nodes. The algorithm has excellent energy conserving properties and, as such, is highly suited for WSN environments. We believe the findings of this paper can serve as a good starting point and encourage further research on the deployment of mobile nodes for the purpose of self-healing in wireless sensor networks.
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 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 | 0.002 | 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".