Localized Communication and Topology Protocols for Ad Hoc Networks-Part II: A Preface to the Special Section
Bibliographic record
Abstract
1 THE SCOPE WE are very proud and honored to have been entrusted to guest edit this special section. The main goal was to put together a strong issue emphasizing quality and relevance to current interests in this important field. Papers were sought to cover comprehensively the algorithmic issues in the “hot” area of ad hoc and sensor networking. The concentration was on the network layer problems which can be divided into two groups: data communication and topology control problems. In data communication problems, such as routing, quality-of-service routing, geocasting, multicasting, and broadcasting, the primary goal is to fulfill a given communication task successfully between nodes in an ad hoc network. The secondary task is to minimize the communication overhead and power consumption given that in the vast majority of applications nodes run on batteries. Topology control problems are further subdivided into neighbor discovery and network organization problems. In the neighbor discovery problem, the problem is to detect neighboring nodes located within transmission range. In the network organization problem, each node should decide what communication links to establish with neighboring nodes (an example is the Bluetooth scatternet formation problem), and what power management schemes to adopt (examples are “sleep” period operations and adjusting transmission ranges). Due to their theoretical challenges and myriads of practical applications, wireless sensor networks are emerging as one of the priority research and development areas. The applications of sensor networks are envisioned primarily for monitoring the environment (e.g., motion detection, chemicals, temperature) or as key components in embedded systems (e.g., biomedical sensor engineering). This special section also sought submissions on this “hot” topic, including problems such as: physical properties, sensor training, security through intelligent node cooperation, medium access, sensor area coverage with random and deterministic placement, object location, sensor position determination, energy efficient broadcasting and activity scheduling, routing, connectivity, data dissemination and gathering, sensor centric quality of routing, path exposure, tree reconfiguration, topology construction, and transport layer. The main paradigm shift is to apply localized schemes as opposed to existing protocols requiring global information. Localized algorithms are distributed algorithms where simple local node behavior achieves a desired global objective. Localized protocols provide scalable solutions, that is, solutions for wireless networks with an arbitrary number of nodes, which is the main goal of this plan. Sensor and rooftop/mesh networks, for instance, have hundreds or thousands of nodes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".